Cost-effectiveness Analysis in R Using a Multi-state Modeling Survival Analysis Framework: A Tutorial

Estimating Costs Associated with Disease Model States Using Generalized Linear Models: A Tutorial

Estimates of costs associated with disease states are required to inform decision analytic disease models to evaluate interventions that modify disease trajectory. Increasingly, decision analytic models are developed using patient-level data with a focus on heterogeneity between patients, and there is a demand for costs informing such models to reflect individual patient costs. Statistical models of health care costs need to recognize the specific features of costs data which typically include a large number of zero observations for non-users, and a skewed and heavy right-hand tailed distribution due to a small number of heavy healthcare users. Different methods are available for modelling costs, such as generalized linear models (GLMs), extended estimating equations and latent class approaches. While there are tutorials addressing approaches to decision modelling, there is no practical guidance on the cost estimation to inform such models. Therefore, this tutorial aims to provide a general guidance on estimating healthcare costs associated with disease states in decision analytic models. Specifically, we present a step-by-step guide to how individual participant data can be used to estimate costs over discrete periods for participants with particular characteristics, based on the GLM framework. We focus on the practical aspects of cost modelling from the conceptualization of the research question to the derivation of costs for an individual in particular disease states. We provide a practical example with step-by-step R code illustrating the process of modelling the hospital costs associated with disease states for a cardiovascular disease model.

Improved estimation of overall survival and progression-free survival for state transition modeling

Aim: National Institute for Health and Care Excellence guidance (Technical Support Document 19) highlights a key challenge of state transition models (STMs) being their difficulty in achieving a satisfactory fit to the observed within-trial endpoints. Fitting poorly to data over the trial period can then have implications for long-term extrapolations. A novel estimation approach is defined in which the predicted overall survival (OS) and progression-free survival (PFS) extrapolations from an STM are optimized to provide closer estimates of the within-trial endpoints. Materials & methods: An STM was fitted to the SQUIRE trial data in non-small-cell lung cancer (obtained from Project Data Sphere). Two methods were used: a standard approach whereby the maximum likelihood was utilized for the individual transitions and the best-fitting parametric model selected based on AIC/BIC, and a novel approach in which parameters were optimized by minimizing the area between the STM-predicted OS and PFS curves and the corresponding OS and PFS Kaplan-Meier curves. Sensitivity analyses were conducted to assess uncertainty. Results: The novel approach resulted in closer estimations to the OS and PFS Kaplan-Meier for all combinations of parametric distributions analyzed compared with the standard approach. Though the uncertainty associated with the novel approach was slightly larger, it provided better estimates to the restricted mean survival time in 10 of the 12 parametric distributions analyzed. Conclusion: A novel approach is defined which provides an alternative STM estimation method enabling improved fits to modeled endpoints, which can easily be extended to more complex model structures.

P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study

Background and Aims

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of cancer with a high incidence globally. This study aimed to investigate the role of P2RY13 in the progression of ccRCC and elucidate its mechanism of action.

Methods

Gene Expression Omnibus and The Cancer Genome Atlas databases were used to extract gene expression profiles of ccRCC. These profiles were annotated and visualized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA). The STRING database was used to establish a protein-protein interaction network and to analyze the functional similarity. The GEPIA2 database was used to predict survival associated with hub genes. Meanwhile, the TIMER2.0 database was used to assess immune cell infiltration and its link with the hub genes. Immunohistochemistry (IHC) was used to determine the difference between ccRCC and adjacent normal tissue.

Results

We identified 272 differentially expressed genes (DEGs). GO and KEGG analyses suggested that DEGs were primarily involved in lymphocyte activation, inflammatory response, immunological effector mechanism pathways. By cytohubba, the 20 highest-scoring hub genes were screened to identify critical genes in the protein-protein interaction network linked with ccRCC. Resting dendritic cells, CD8 T cells, and activated mast cells all showed a significant positive correlation with these hub genes. Moreover, a higher immune score was associated with increased prognostic risk scores, which in turn correlated with a poorer prognosis. IHC revealed that P2RY13 was expressed at higher levels in ccRCC compared to para-cancer tissues.

Conclusion

Identifying the DEGs will aid in the understanding of the causes and molecular mechanisms involved in ccRCC. P2RY13 may play a pivotal role in the progression and prognosis of ccRCC, potentially driving carcinogenesis though immune system mechanisms.

The Cost-Effectiveness of Pertuzumab for the Treatment of Metastatic HER2+ Breast Cancer in Czechia: A Semi-Markov Model Using Cost States

OBJECTIVES

This article estimates the cost-effectiveness of adding pertuzumab to the combination of trastuzumab and docetaxel within the first-line treatment for metastatic breast cancer with the amplification of HER2+.

METHODS

Data from Czech clinical practice recorded in the BREAST register are used. A semi-Markov model with states derived based on the treatment phases (first-line medication, no medication, next-line medication, death) is defined to estimate costs from the healthcare payers' perspective. The benefits are estimated as patient survival until death. The Kaplan-Meier estimates are supplemented by the Cox proportional hazard and the accelerated failure time models to control for patient characteristics. Health-related quality-of-life indicators are derived from relevant literature.

RESULTS

Based on the used data, adding pertuzumab does not result in statistically significantly longer survival while inducing higher treatment costs (€163 360 compared with €90 112 per patient in 2018 prices). Statistically longer survival was not supported by the log-rank test (P = .97), the Cox proportional hazard model, or the accelerated failure time model using the Gompertz distribution. The incremental cost-effectiveness ratio (€87 200) substantially exceeds the willingness to pay for 1 quality-adjusted life-year (€46 500).

CONCLUSIONS

This analysis indicates that adding pertuzumab cannot be considered cost-effective in Czechia. However, the observed phenomenon may be attributed to the limited duration of patient follow-up periods at the time of the study's execution (mean of 20-21 months). Importantly, we find that using states connected to specific treatment phases is appropriate for a retrospective analysis of patient-level clinical data.

One versus three weeks hypofractionated whole breast radiotherapy for early breast cancer treatment: the FAST-Forward phase III RCT

Background

FAST-Forward aimed to identify a 5-fraction schedule of adjuvant radiotherapy delivered in 1 week that was non-inferior in terms of local cancer control and as safe as the standard 15-fraction regimen after primary surgery for early breast cancer. Published acute toxicity and 5-year results are presented here with other aspects of the trial.

Design

Multicentre phase III non-inferiority trial. Patients with invasive carcinoma of the breast (pT1-3pN0-1M0) after breast conservation surgery or mastectomy randomised (1 : 1 : 1) to 40 Gy in 15 fractions (3 weeks), 27 Gy or 26 Gy in 5 fractions (1 week) whole breast/chest wall (Main Trial). Primary endpoint was ipsilateral breast tumour relapse; assuming 2% 5-year incidence for 40 Gy, non-inferiority pre-defined as < 1.6% excess for 5-fraction schedules (critical hazard ratio = 1.81). Normal tissue effects were assessed independently by clinicians, patients and photographs.

Sub-studies

Two acute skin toxicity sub-studies were undertaken to confirm safety of the test schedules. Primary endpoint was proportion of patients with grade ≥ 3 acute breast skin toxicity at any time from the start of radiotherapy to 4 weeks after completion. Nodal Sub-Study patients had breast/chest wall plus axillary radiotherapy testing the same three schedules, reduced to the 40 and 26 Gy groups on amendment, with the primary endpoint of 5-year patient-reported arm/hand swelling.

Limitations

A sequential hypofractionated or simultaneous integrated boost has not been studied.

Participants

Ninety-seven UK centres recruited 4096 patients (1361:40 Gy, 1367:27 Gy, 1368:26 Gy) into the Main Trial from November 2011 to June 2014. The Nodal Sub-Study recruited an additional 469 patients from 50 UK centres. One hundred and ninety and 162 Main Trial patients were included in the acute toxicity sub-studies.

Results

Acute toxicity sub-studies evaluable patients: (1) acute grade 3 Radiation Therapy Oncology Group toxicity reported in 40 Gy/15 fractions 6/44 (13.6%); 27 Gy/5 fractions 5/51 (9.8%); 26 Gy/5 fractions 3/52 (5.8%). (2) Grade 3 common toxicity criteria for adverse effects toxicity reported for one patient. At 71-month median follow-up in the Main Trial, 79 ipsilateral breast tumour relapse events (40 Gy: 31, 27 Gy: 27, 26 Gy: 21); hazard ratios (95% confidence interval) versus 40 Gy were 27 Gy: 0.86 (0.51 to 1.44), 26 Gy: 0.67 (0.38 to 1.16). With 2.1% (1.4 to 3.1) 5-year incidence ipsilateral breast tumour relapse after 40 Gy, estimated absolute differences versus 40 Gy (non-inferiority test) were -0.3% (-1.0-0.9) for 27 Gy (p = 0.0022) and -0.7% (-1.3-0.3) for 26 Gy (p = 0.00019). Five-year prevalence of any clinician-assessed moderate/marked breast normal tissue effects was 40 Gy: 98/986 (9.9%), 27 Gy: 155/1005 (15.4%), 26 Gy: 121/1020 (11.9%). Across all clinician assessments from 1 to 5 years, odds ratios versus 40 Gy were 1.55 (1.32 to 1.83; p < 0.0001) for 27 Gy and 1.12 (0.94-1.34; p = 0.20) for 26 Gy. Patient and photographic assessments showed higher normal tissue effects risk for 27 Gy versus 40 Gy but not for 26 Gy. Nodal Sub-Study reported no arm/hand swelling in 80% and 77% in 40 Gy and 26 Gy at baseline, and 73% and 76% at 24 months. The prevalence of moderate/marked arm/hand swelling at 24 months was 10% versus 7% for 40 Gy compared with 26 Gy.

Interpretation

Five-year local tumour incidence and normal tissue effects prevalence show 26 Gy in 5 fractions in 1 week is a safe and effective alternative to 40 Gy in 15 fractions for patients prescribed adjuvant local radiotherapy after primary surgery for early-stage breast cancer.

Future work

Ten-year Main Trial follow-up is essential. Inclusion in hypofractionation meta-analysis ongoing. A future hypofractionated boost trial is strongly supported.

Trial registration

FAST-Forward was sponsored by The Institute of Cancer Research and was registered as ISRCTN19906132.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 09/01/47) and is published in full in Health Technology Assessment; Vol. 27, No. 25. See the NIHR Funding and Awards website for further award information.

Cost-Effectiveness Analysis of Pembrolizumab as an Adjuvant Treatment of Renal Cell Carcinoma Post-nephrectomy in the United States

INTRODUCTION

Pembrolizumab was recently approved as an adjuvant treatment of renal cell carcinoma (RCC), based on prolonged disease-free survival compared to placebo in the phase III KEYNOTE-564 trial. The objective of this study was to evaluate the cost-effectiveness of pembrolizumab as monotherapy in the adjuvant treatment of RCC post-nephrectomy, from a US health sector perspective.

PATIENTS AND METHODS

A Markov model with 4 health states (disease-free, locoregional recurrence, distant metastases, and death) was developed to compare the cost and effectiveness of pembrolizumab versus routine surveillance or sunitinib. Transition probabilities were estimated using patient-level KEYNOTE-564 data (cutoff: June 14, 2021), a retrospective study, and published literature. Costs of adjuvant and subsequent treatments, adverse events, disease management, and terminal care were estimated in 2022 US$. Utilities were based on EQ-5D-5L data collected in KEYNOTE-564. Outcomes included costs, life-years (LYs), and quality-adjusted LYs (QALYs). Robustness was assessed through one-way and probabilistic sensitivity analyses.

RESULTS

Total cost per patient was $549,353 for pembrolizumab, $505,094 for routine surveillance, and $602,065 for sunitinib. Over a lifetime, pembrolizumab provided gains of 0.96 QALYs (1.00 LYs) compared to routine surveillance, yielding an incremental cost-effectiveness ratio of $46,327/QALY. Pembrolizumab dominated sunitinib with 0.89 QALYs (0.91 LYs) gained while saving costs. At a $150,000/QALY threshold, pembrolizumab was cost-effective versus both routine surveillance and sunitinib in 84.2% of probabilistic simulations.

CONCLUSION

Pembrolizumab is projected to be cost-effective as an adjuvant RCC treatment versus routine surveillance or sunitinib based on a typical willingness-to-pay threshold.

Cost-Effectiveness Analysis of Pembrolizumab as an Adjuvant Treatment of Resected Stage IIB or IIC Melanoma in the United States

INTRODUCTION

Pembrolizumab was approved in the US as adjuvant treatment of patients with stage IIB or IIC melanoma post-complete resection, based on prolonged recurrence-free survival vs. placebo in the Phase 3 KEYNOTE-716 trial. This study aimed to evaluate the cost-effectiveness of pembrolizumab vs. observation as adjuvant treatment of stage IIB or IIC melanoma from a US health sector perspective.

METHODS

A Markov cohort model was constructed to simulate patient transitions among recurrence-free, locoregional recurrence, distant metastasis, and death. Transition probabilities from recurrence-free and locoregional recurrence were estimated via multistate parametric modeling based on patient-level data from an interim analysis (data cutoff date: 04-Jan-2022). Transition probabilities from distant metastasis were based on KEYNOTE-006 data and network meta-analysis. Costs were estimated in 2022 US dollars. Utilities were based on applying US value set to EQ-5D-5L data collected in trial and literature.

RESULTS

Compared to observation, pembrolizumab increased total costs by $80,423 and provided gains of 1.17 quality-adjusted life years (QALYs) and 1.24 life years (LYs) over lifetime, resulting in incremental cost-effectiveness ratios of $68,736/QALY and $65,059/LY. The higher upfront costs of adjuvant treatment were largely offset by reductions in costs of subsequent treatment, downstream disease management, and terminal care, reflecting the lower risk of recurrence with pembrolizumab. Results were robust in one-way sensitivity and scenario analyses. At a $150,000/QALY threshold, pembrolizumab was cost-effective vs. observation in 73.9% of probabilistic simulations that considered parameter uncertainty.

CONCLUSION

As an adjuvant treatment of stage IIB or IIC melanoma, pembrolizumab was estimated to reduce recurrence, extend patients' life and QALYs, and be cost-effective versus observation at a US willingness-to-pay threshold.

Cost-Effectiveness of Pembrolizumab as an Adjuvant Treatment in Colombia for Melanoma Patients with Lymph Node Involvement After Complete Resection

INTRODUCTION

The KEYNOTE-054 trial found that adjuvant treatment with pembrolizumab improved recurrence-free survival versus placebo in completely resected high-risk stage III melanoma patients. We assessed the cost-effectiveness of adjuvant pembrolizumab in Colombia compared with watchful waiting, a widely used strategy despite the high risk of recurrence with surgery alone.

METHODS

A four-health state [recurrence-free (RF), locoregional recurrence (LR), distant metastases (DM), and death) Markov model was developed to assess the lifetime medical costs and outcomes (3% annual discount), along with cost-effectiveness ratios (ICERs). The transitions from the RF and LR states were modeled using KEYNOTE-054 data, and those from the DM state were modeled using data from the KEYNOTE-006 trial and a network meta-analysis of advanced treatments received after adjuvant pembrolizumab and watchful waiting. The health state utilities were derived from KEYNOTE-054 Euro-QoL data and literature. Costs are expressed in 2021 Colombian pesos (COP).

RESULTS

Over a 46-year time horizon, patients on adjuvant pembrolizumab and watchful waiting were estimated to gain 9.69 and 7.56 quality-adjusted life-years (QALYs), 10.83 and 8.65 life-years (LYs), and incur costs of COP 663,595,726 and COP 563,237,206, respectively. The proportion of LYs spent in RF state was 84.63% for pembrolizumab and 72.13% for watchful waiting, yielding lower subsequent treatment, disease management, and terminal care costs for pembrolizumab. Adjuvant pembrolizumab improved survival by 2.18 LYs and 2.13 QALYs versus watchful waiting. The ICER per QALY was COP 47,081,917, primarily driven by recurrence rates and advanced melanoma treatments. The deterministic sensitivity analysis results were robust and consistent across various reasonable inputs and alternative scenarios. At a willingness-to-pay threshold of COP 69,150,201 per QALY, the probability of pembrolizumab being cost-effective was 65.70%.

CONCLUSION

Pembrolizumab is cost-effective as an adjuvant treatment compared to watchful waiting among patients with high-risk stage III melanoma after complete resection in Colombia.

A Simple Cost-Effectiveness Model of Screening: An Open-Source Teaching and Research Tool Coded in R

Applied cost-effectiveness analysis models are an important tool for assessing health and economic effects of healthcare interventions but are not best suited for illustrating methods. Our objective is to provide a simple, open-source model for the simulation of disease-screening cost-effectiveness for teaching and research purposes. We introduce our model and provide an initial application to examine changes to the efficiency frontier as input parameters vary and to demonstrate face validity. We described a vectorised, discrete-event simulation of screening in R with an Excel interface to define parameters and inspect principal results. An R Shiny app permits dynamic interpretation of simulation outputs. An example with 8161 screening strategies illustrates the cost and effectiveness of varying the disease sojourn time, treatment effectiveness, and test performance characteristics and costs on screening policies. Many of our findings are intuitive and straightforward, such as a reduction in screening costs leading to decreased overall costs and improved cost-effectiveness. Others are less obvious and depend on whether we consider gross outcomes or those net to no screening. For instance, enhanced treatment of symptomatic disease increases gross effectiveness, but reduces the net effectiveness and cost-effectiveness of screening. A lengthening of the preclinical sojourn time has ambiguous effects relative to no screening, as cost-effectiveness improves for some strategies but deteriorates for others. Our simple model offers an accessible platform for methods research and teaching. We hope it will serve as a public good and promote an intuitive understanding of the cost-effectiveness of screening.

Treatment of supraglottic squamous cell carcinoma with advanced technologies: observational prospective evaluation of oncological outcomes, functional outcomes, quality of life and cost-effectiveness (SUPRA-QoL)

BACKGROUND

Over the past decade, therapeutic options in head and neck supraglottic squamous cell carcinoma have constantly evolved. The classical total laryngectomy has been partially replaced by alternative organ- and function-sparing techniques with the same prognosis but less morbidity, such as Radiotherapy, Transoral Laser Microsurgery (TLM) and Trans-Oral Robotic Surgery (TORS). Up to now, a prospective comparison of these innovant techniques has not been conducted.

METHODS/DESIGN

We will conduct an original international multicentric prospective nonrandomized clinical trial to compare the efficacy between these treatments (Arm 1: Radiotherapy ± chemotherapy; Arm 2: TLM and Arm 3: TORS) with 4 classes of outcomes: quality of life (QoL), oncological outcomes, functional outcomes and economic resources. The population will include cT1-T2 /cN0-N1/M0 supraglottic squamous cell carcinoma. The primary outcome is a Clinical Dysphagia QoL evaluation assessed by the MD Anderson Dysphagia questionnaire. Secondary outcomes include others QoL evaluation, oncological and functional measures and cost parameters. The sample size needs to reach 36 patients per arm (total 108).

DISCUSSION

In the current literature, no prospective head-to-head trials are available to compare objectively these different treatments. With the increase of highly efficient treatments and the increase of oncological survival, it is imperative also to develop management strategies that optimize QoL and functional results. We will conduct this innovate prospective trial in order to obtain objective data in these two main issues.

TRIAL REGISTRATION

NCT05611515 posted on 10/11/2022 (clinicaltrial.fgov).

An Investigation into the Relationship Between Choice of Model Structure and How to Adjust for Subsequent Therapies Using a Case Study in Oncology

BACKGROUND

A common challenge in health technology assessments (HTAs) of cancer treatments is how subsequent therapy use within the trial follow-up may influence cost-effectiveness model outcomes. Although overall survival (OS) is often a key driver of model results, there are no guidelines to advise how to adjust for this potential confounding, with different approaches available dependent on the model structure.

OBJECTIVE

We compared a partitioned survival analysis (PartSA) with a semi-Markov multi-state model (MSM) structure, with and without attempts to adjust for the impact of subsequent therapies on OS using a case study describing outcomes for people with relapsed/refractory multiple myeloma.

METHODS

Both model structures included three health states: pre-progression, progressed disease and death. Three traditional crossover methods were considered within the context of the PartSA, whereas for the MSM, the probability of post-progression death was pooled across arms. Impacts on the model incremental cost-effectiveness ratio (ICER) were recorded.

RESULTS

The unadjusted PartSA produced an ICER of £623,563, and after adjustment yielded an ICER range of £381,340-£386,907. The unadjusted MSM produced an ICER of £1,283,780. Adjusting OS in the MSM resulted in an ICER of £345,486.

CONCLUSIONS

The simplicity of the PartSA is lost when the decision problem becomes more complex (for example, when OS data are confounded by subsequent therapies). In this setting, the MSM structure may be considered more flexible, with fewer and less restrictive assumptions required versus the PartSA. Researchers should consider important study design features that may influence the generalisability of data when undertaking model conceptualisation.

Exploring different research questions via complex multi-state models when using registry-based repeated prescriptions of antidepressants in women with breast cancer and a matched population comparison group

BACKGROUND

Multi-state models are used to study several clinically meaningful research questions. Depending on the research question of interest and the information contained in the data, different multi-state structures and modelling choices can be applied. We aim to explore different research questions using a series of multi-state models of increasing complexity when studying repeated prescriptions data, while also evaluating different modelling choices.

METHODS

We develop a series of research questions regarding the probability of being under antidepressant medication across time using multi-state models, among Swedish women diagnosed with breast cancer (n = 18,313) and an age-matched population comparison group of cancer-free women (n = 92,454) using a register-based database (Breast Cancer Data Base Sweden 2.0). Research questions were formulated ranging from simple to more composite ones. Depending on the research question, multi-state models were built with structures ranging from simpler ones, like single-event survival analysis and competing risks, up to complex bidirectional and recurrent multi-state structures that take into account the recurring start and stop of medication. We also investigate modelling choices, such as choosing a time-scale for the transition rates and borrowing information across transitions.

RESULTS

Each structure has its own utility and answers a specific research question. However, the more complex structures (bidirectional, recurrent) enable accounting for the intermittent nature of prescribed medication data. These structures deliver estimates of the probability of being under medication and total time spent under medication over the follow-up period. Sensitivity analyses over different definitions of the medication cycle and different choices of timescale when modelling the transition intensity rates show that the estimates of total probabilities of being in a medication cycle over follow-up derived from the complex structures are quite stable.

CONCLUSIONS

Each research question requires the definition of an appropriate multi-state structure, with more composite ones requiring such an increase in the complexity of the multi-state structure. When a research question is related with an outcome of interest that repeatedly changes over time, such as the medication status based on prescribed medication, the use of novel multi-state models of adequate complexity coupled with sensible modelling choices can successfully address composite, more realistic research questions.

Blended Survival Curves: A New Approach to Extrapolation for Time-to-Event Outcomes from Clinical Trials in Health Technology Assessment

BACKGROUND

Survival extrapolation is essential in cost-effectiveness analysis to quantify the lifetime survival benefit associated with a new intervention, due to the restricted duration of randomized controlled trials (RCTs). Current approaches of extrapolation often assume that the treatment effect observed in the trial can continue indefinitely, which is unrealistic and may have a huge impact on decisions for resource allocation.

OBJECTIVE

We introduce a novel methodology as a possible solution to alleviate the problem of survival extrapolation with heavily censored data from clinical trials.

METHOD

The main idea is to mix a flexible model (e.g., Cox semiparametric) to fit as well as possible the observed data and a parametric model encoding assumptions on the expected behavior of underlying long-term survival. The two are "blended" into a single survival curve that is identical with the Cox model over the range of observed times and gradually approaching the parametric model over the extrapolation period based on a weight function. The weight function regulates the way two survival curves are blended, determining how the internal and external sources contribute to the estimated survival over time.

RESULTS

A 4-y follow-up RCT of rituximab in combination with fludarabine and cyclophosphamide versus fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia is used to illustrate the method.

CONCLUSION

Long-term extrapolation from immature trial data may lead to significantly different estimates with various modelling assumptions. The blending approach provides sufficient flexibility, allowing a wide range of plausible scenarios to be considered as well as the inclusion of external information, based, for example, on hard data or expert opinion. Both internal and external validity can be carefully examined.

HIGHLIGHTS

Interim analyses of trials with limited follow-up are often subject to high degrees of administrative censoring, which may result in implausible long-term extrapolations using standard approaches.In this article, we present an innovative methodology based on "blending" survival curves to relax the traditional proportional hazard assumption and simultaneously incorporate external information to guide the extrapolation.The blended method provides a simple and powerful framework to allow a careful consideration of a wide range of plausible scenarios, accounting for model fit to the short-term data as well as the plausibility of long-term extrapolations.

Development and validation of a decision model for the evaluation of novel lung cancer treatments in the Netherlands

Recent discoveries in molecular diagnostics and drug treatments have improved the treatment of patients with advanced (inoperable) non-squamous non-small cell lung cancer (NSCLC) from solely platinum-based chemotherapy to more personalized treatment, including targeted therapies and immunotherapies. However, these improvements come at considerable costs, highlighting the need to assess their cost-effectiveness in order to optimize lung cancer care. Traditionally, cost-effectiveness models for the evaluation of new lung cancer treatments were based on the findings of the randomized control trials (RCTs). However, the strict RCT inclusion criteria make RCT patients not representative of patients in the real-world. Patients in RCTs have a better prognosis than patients in a real-world setting. Therefore, in this study, we developed and validated a diagnosis-treatment decision model for patients with advanced (inoperable) non-squamous NSCLC based on real-world data in the Netherlands. The model is a patient-level microsimulation model implemented as discrete event simulation with five health events. Patients are simulated from diagnosis to death, including at most three treatment lines. The base-model (non-personalized strategy) was populated using real-world data of patients treated with platinum-based chemotherapy between 2008 and 2014 in one of six Dutch teaching hospitals. To simulate personalized care, molecular tumor characteristics were incorporated in the model based on the literature. The impact of novel targeted treatments and immunotherapies was included based on published RCTs. To validate the model, we compared survival under a personalized treatment strategy with observed real-world survival. This model can be used for health-care evaluation of personalized treatment for patients with advanced (inoperable) NSCLC in the Netherlands.

Health Economic Evaluation Using Markov Models in R for Microsoft Excel Users: A Tutorial

A health economic evaluation (HEE) is a comparative analysis of alternative courses of action in terms of both costs and consequences. A cost-effectiveness analysis is a type of HEE that compares an intervention to one or more alternatives by estimating how much it costs to gain an additional unit of health outcome. Cost-effectiveness analyses are commonly performed using Microsoft (MS) Excel. However, there is current interest in using other software that is better suited to more complex problems, methods, and data, as well as improved reproducibility and transparency. That is, it is increasingly important to be able to repeat an analysis of a particular data set and obtain the same results, and access the analysis and results in a clear and comprehensive openly available form. In this tutorial we provide a step-by-step guide on how to implement a mainstay model of HEE, namely a Markov model, in the statistical programming language R. The adoption of R for the purpose of cost-effectiveness analysis is highly dependent on the ability of the health economic modeller to understand, learn, and apply programming-type skills. R is likely to be less familiar than MS Excel for many modellers and so coding a cost-effectiveness model in R can be a large jump. We describe the technical details from the perspective of a MS Excel user to help bridge the gap between software and reduce the learning curve by providing for the first-time side-by-side comparisons of the Markov model example in MS Excel and R.

Cost-effectiveness of 5 fraction and partial breast radiotherapy for early breast cancer in the UK: model-based multi-trial analysis

PURPOSE

We estimated the cost-effectiveness of 4 radiotherapy modalities to treat early breast cancer in the UK. In a subgroup of patients eligible for all modalities, we compared whole-breast (WB) and partial breast (PB) radiotherapy delivered in either 15 (WB15F, PB15F) or 5 fractions (WB5F, PB5F). In a subgroup ineligible for PB radiotherapy, we compared WB15F to WB5F.

METHODS

We developed a Markov cohort model to simulate lifetime healthcare costs and quality-adjusted life years (QALYs) for each modality. This was informed by the clinical analysis of two non-inferiority trials (FAST Forward and IMPORT LOW) and supplemented with external literature. The primary analysis assumed that radiotherapy modality influences health only through its impact on locoregional recurrence and radiotherapy-related adverse events.

RESULTS

In the primary analysis, PB5F had the least cost and greatest expected QALYs. WB5F had the least cost and the greatest expected QALYs in those only eligible for WB radiotherapy. Applying a cost-effectiveness threshold of £15,000/QALY, there was a 62% chance that PB5F was the cost-effective alternative in the PB eligible group, and there was a 100% chance that WB5F was cost-effective in the subgroup ineligible for PB radiotherapy.

CONCLUSIONS

Hypofractionation to 5 fractions and partial breast radiotherapy modalities offer potentially important benefits to the UK health system.

Cost-Effectiveness Analysis of Sequential Treatment Strategies for Advanced Melanoma in Real Life in France

Nine drugs have been marketed for 10 years for the treatment of advanced melanoma (AM). With half of patients reaching a second line, the optimal sequence of treatments remains unclear. To inform policy-makers about their efficiency, we performed a cost-effectiveness analysis of sequential strategies in clinical practice in France, for BRAF-mutated and wild-type patients. A multistate model was developed to describe treatment sequences, associated costs, and health outcomes over 10 years. Sequences, clinical outcomes, utility scores, and economic data were extracted from the prospective Melbase cohort, collecting individual data in 1518 patients since 2013, from their AM diagnosis until their death. To adjust the differences in patients' characteristics among sequences, weighting by inverse probability was used. In the BRAF-mutated population, the MONO-targeted therapies (TT)-anti-PD1 sequence was the less expensive, whereas the anti-PD1-BI-TT sequence had an incremental cost-effectiveness ratio (ICER) of 180,441 EUR/QALY. Regarding the BRAF wild-type population, the three sequences constituted the cost-effective frontier, with ICERs ranging from 116 to 806,000 EUR/QALY. For BRAF-mutated patients, the sequence anti-PD1-BI-TT appeared to be the most efficient one in BRAF-mutated AM patients until 2018. Regarding the BRAF wild-type population until 2018, the sequence starting with IPI+NIVO appeared inefficient compared to anti-PD1, considering the extra cost for the QALY gained.

A long-term cost-effectiveness analysis of cardiac resynchronisation therapy with or without defibrillator based on health claims data

BACKGROUND

In Germany, CRT devices with defibrillator capability (CRT-D) have become the predominant treatment strategy for patients with heart failure and cardiac dyssynchrony. However, according to current guidelines, most patients would also be eligible for the less expensive CRT pacemaker (CRT-P). We conducted a cost-effectiveness analysis for CRT-P devices compared to CRT-D devices from a German payer's perspective.

METHODS

Longitudinal health claims data from 3569 patients with de novo CRT implantation from 2014 to 2019 were used to parametrise a cohort Markov model. Model outcomes were costs and effectiveness measured in terms of life years. Transition probabilities were derived from multivariable parametric survival regression that controlled for baseline differences of CRT-D and CRT-P patients. Deterministic and probabilistic sensitivity analyses were conducted.

RESULTS

The Markov model predicted a median survival of 84 months for CRT-P patients and 92 months for CRT-D patients. In the base case, CRT-P devices incurred incremental costs of € - 13,093 per patient and 0.30 incremental life years were lost. The ICER was € 43,965 saved per life year lost. In the probabilistic sensitivity analysis, uncertainty regarding the effectiveness was observed but not regarding costs.

CONCLUSION

This modelling study illustrates the uncertainty of the higher effectiveness of CRT-D devices compared to CRT-P devices. Given the difference in incremental costs between CRT-P and CRT-D treatment, there would be significant potential cost savings to the healthcare system if CRT-D devices were restricted to patients likely to benefit from the additional defibrillator.

An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis

Colon cancer is one of the leading malignancies with poor prognosis worldwide. Immune cell infiltration has a potential prognostic value for colon cancer. This study aimed to establish an immune-related prognostic risk model for colon cancer by bioinformatics analysis. A total of 1670 differentially expressed genes (DEGs), including 177 immune-related genes, were identified from The Cancer Genome Atlas (TCGA) dataset. A prognostic risk model was constructed based on six critical immune-related genes (C-X-C motif chemokine ligand 1 (CXCL1), epiregulin (EREG), C-C motif chemokine ligand 24 (CCL24), fatty acid binding protein 4 (FABP4), tropomyosin 2 (TPM2), and semaphorin 3G (SEMA3G)). This model was validated using the microarray dataset GSE35982. In addition, Cox regression analysis showed that age and clinical stage were correlated with prognostic risk scores. Kaplan–Meier survival analysis showed that high risk scores correlated with low survival probabilities in patients with colon cancer. Downregulated TPM2, FABP4, and SEMA3G levels were positively associated with the activated mast cells, monocytes, and macrophages M2. Upregulated CXCL1 and EREG were positively correlated with macrophages M1 and activated T cells CD4 memory, respectively. Based on these results, we can conclude that the proposed prognostic risk model presents promising novel signatures for the diagnosis and prognosis prediction of colon cancer. This model may provide therapeutic benefits for the development of immunotherapy for colon cancer.

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer

Objective. Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods. Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan–Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results. Univariate Cox regression and Kaplan–Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan–Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa ( $p<0.05$ ). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions. The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.

Methods and Study Design for Cancer Health Economics Research: Summary of Discussions From a Breakout Session

The legitimacy of findings from cancer health economics research depends on study design and methods. A breakout session, Methods and Study Design for Cancer Health Economics Research, was convened at the Future of Cancer Health Economics Research Conference to discuss 2 commonly used analytic tools for cancer health economics research: observational studies and decision-analytic modeling. Observational studies include analysis of data collected with the primary purpose of supporting economic evaluation or secondary use of data collected for another purpose. Modeling studies develop a parametrized structure, such as a decision tree, to estimate hypothetical impact. Whereas observational studies focus on what has happened and why, modeling studies address what may happen. We summarize the discussion at this breakout session, focusing on 3 key elements of high-quality cancer health economics research: study design, analytical methods, and addressing uncertainty.

Opportunities and Barriers to the Development and Use of Open Source Health Economic Models: A Survey

OBJECTIVES

Health economic (HE) models are routinely used to support health policy and resource allocation decisions but are often considered "black boxes" that may be prone to error and bias. Open source models (OSMs) have been advocated to increase the transparency, credibility, and reuse of HE models. Previous studies have demonstrated interest in OSMs among the health economics and outcomes research community, but the number of OSMs remains low.

METHODS

We conducted an online survey of ISPOR (the leading professional society for health economics and outcomes research) members' perspectives on the usefulness of OSMs and barriers to their development and implementation.

RESULTS

Respondents (N = 230) included academics (27%), pharmaceutical (or related) industry representatives (23%), health research or consulting representatives (21%), governmental or nonprofit agency representatives (10%), and others (19%). Respondents were generally not familiar with barriers to the development and adoption of OSMs. Most agreed that OSMs would improve transparency (92%), efficiency (76%), and HE model reuse (86%) and promote confidence in using HE models (75%). The use of OSMs by health technology assessment authorities was considered a very important indicator of the usefulness of OSMs by 49% of respondents. Three-quarters of respondents perceived legal concerns and the ability to transfer data as important barriers to the development and use of OSMs.

CONCLUSIONS

Respondents believe that OSMs could increase the transparency, efficiency, and credibility of HE models, but that several barriers hamper their widespread adoption. Our results suggest that fundamental changes may be needed across the health economics and outcomes research community if OSMs are to become widely adopted.

Estimating and Extrapolating Survival Using a State-Transition Modeling Approach: A Practical Application in Multiple Myeloma

OBJECTIVES

State-transition models (STMs) applied in oncology have given limited considerations to modeling postprogression survival data. This study presents an application of an STM focusing on methods to evaluate the postprogression transition and its impact on survival predictions.

METHODS

Data from the lenalidomide plus dexamethasone arm of the ASPIRE trial was used to estimate transition rates for an STM. The model accounted for the competing risk between the progression and preprogression death events and included an explicit structural link between the time to progression and subsequent death. The modeled transition rates were used to simulate individual disease trajectories in a discrete event simulation framework, based on which progression-free survival and overall survival over a 30-year time horizon were estimated. Survival predictions were compared with the observed trial data, matched external data, and estimates obtained from a more conventional partitioned survival analysis approach.

RESULTS

The rates of progression and preprogression death were modeled using piecewise exponential functions. The rate of postprogression mortality was modeled using an exponential function accounting for the nonlinear effect of the time to progression. The STM provided survival estimates that closely fitted the trial data and gave more plausible long-term survival predictions than the best-fitting Weibull model applied in a partitioned survival analysis.

CONCLUSIONS

The fit of the STM suggested that the modeled transition rates accurately captured the underlying disease process over the modeled time horizon. The considerations of this study may apply to other settings and facilitate a wider use of STMs in oncology.

From Spreadsheets to Script: Experiences From Converting a Scottish Cardiovascular Disease Policy Model into R

Given the advantages in transparency, reproducibility, adaptability and computational efficiency in R, there is a growing interest in converting existing spreadsheet-based models into an R script for model re-use and upskilling training among health economic modellers. The objective of this exercise was to convert the Scottish Cardiovascular Disease (CVD) Policy Model from Excel to R and discuss the lessons learnt throughout this process. The CVD model is a competing risk state transition cohort model. Four health economists, with varied experience of R, attempted to replicate an identical model structure in R based on the model in Excel and reproduce the intermediate and final results. Replications varied in their use of specialist health economics packages in addition to standard data management packages. Two versions of the CVD model were created in R along with a Shiny app. Version 1 was developed without health economics specialist packages and produced identical results to the Excel version. Version 2 used the heemod package and did not achieve the same results, possibly due to the non-standard elements of the model and limited time to adapt the functions. The R model requires less than half the computational time than the Excel model. Conversion of the spreadsheet models to script models is feasible for health economists. A step-by-step guide for the conversion process is provided and modellers' experience is discussed. Coding without specialist packages allows full flexibility, while specialist packages may add convenience if the model structure is suitable. Whichever approach is taken, transparency and replicability remain the key criteria in model programming. Model conversions must maintain standards in these areas regardless of the choice of software.

Cost-utility analysis of adding abiraterone acetate plus prednisone/prednisolone to long-term hormone therapy in newly diagnosed advanced prostate cancer in England: Lifetime decision model based on STAMPEDE trial data

Adding abiraterone acetate (AA) plus prednisolone (P) to standard of care (SOC) improves survival in newly diagnosed advanced prostate cancer (PC) patients starting hormone therapy. Our objective was to determine the value for money to the English National Health Service (NHS) of adding AAP to SOC. We used a decision analytic model to evaluate cost-effectiveness of providing AAP in the English NHS. Between 2011-2014, the STAMPEDE trial recruited 1917 men with high-risk localised, locally advanced, recurrent or metastatic PC starting first-line androgen-deprivation therapy (ADT), and they were randomised to receive SOC plus AAP, or SOC alone. Lifetime costs and quality-adjusted life-years (QALYs) were estimated using STAMPEDE trial data supplemented with literature data where necessary, adjusting for baseline patient and disease characteristics. British National Formulary (BNF) prices (£98/day) were applied for AAP. Costs and outcomes were discounted at 3.5%/year. AAP was not cost-effective. The incremental cost-effectiveness ratio (ICER) was £149,748/QALY gained in the non-metastatic (M0) subgroup, with 2.4% probability of being cost-effective at NICE's £30,000/QALY threshold; and the metastatic (M1) subgroup had an ICER of £47,503/QALY gained, with 12.0% probability of being cost-effective. Scenario analysis suggested AAP could be cost-effective in M1 patients if priced below £62/day, or below £28/day in the M0 subgroup. AAP could dominate SOC in the M0 subgroup with price below £11/day. AAP is effective for non-metastatic and metastatic disease but is not cost-effective when using the BNF price. AAP currently only has UK approval for use in a subset of M1 patients. The actual price currently paid by the English NHS for abiraterone acetate is unknown. Broadening AAP's indication and having a daily cost below the thresholds described above is recommended, given AAP improves survival in both subgroups and its cost-saving potential in M0 subgroup.

Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus

BACKGROUND

Multi-state models are used in complex disease pathways to describe a process where an individual moves from one state to the next, taking into account competing states during each transition. In a multi-state setting, there are various measures to be estimated that are of great epidemiological importance. However, increased complexity of the multi-state setting and predictions over time for individuals with different covariate patterns may lead to increased difficulty in communicating the estimated measures. The need for easy and meaningful communication of the analysis results motivated the development of a web tool to address these issues.

RESULTS

MSMplus is a publicly available web tool, developed via the Shiny R package, with the aim of enhancing the understanding of multi-state model analyses results. The results from any multi-state model analysis are uploaded to the application in a pre-specified format. Through a variety of user-tailored interactive graphs, the application contributes to an improvement in communication, reporting and interpretation of multi-state analysis results as well as comparison between different approaches. The predicted measures that can be supported by MSMplus include, among others, the transition probabilities, the transition intensity rates, the length of stay in each state, the probability of ever visiting a state and user defined measures. Representation of differences, ratios and confidence intervals of the aforementioned measures are also supported. MSMplus is a useful tool that enhances communication and understanding of multi-state model analyses results.

CONCLUSIONS

Further use and development of web tools should be encouraged in the future as a means to communicate scientific research.

A Novel CpG Methylation Risk Indicator for Predicting Prognosis in Bladder Cancer

Purpose

Bladder cancer (BLCA) is one of the most common cancers worldwide. In a large proportion of BLCA patients, disease recurs and/or progress after resection, which remains a major clinical issue in BLCA management. Therefore, it is vital to identify prognostic biomarkers for treatment stratification. We investigated the efficiency of CpG methylation for the potential to be a prognostic biomarker for patients with BLCA.

Patients and Methods

Overall, 357 BLCA patients from The Cancer Genome Atlas (TCGA) were randomly separated into the training and internal validation cohorts. Least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) were used to select candidate CpGs and build the methylation risk score model, which was validated for its prognostic value in the validation cohort by Kaplan–Meier analysis. Hazard curves were generated to reveal the risk nodes throughout the follow-up. Gene Set Enrichment Analysis (GSEA) was used to reveal the potential biological pathways associated with the methylation model. Quantitative real-time polymerase chain reaction (PCR) and western blotting were performed to verify the expression level of the methylated genes.

Results

After incorporating the CpGs obtained by the two algorithms, CpG methylation of eight genes corresponding to TNFAIP8L3, KRTDAP, APC, ZC3H3, COL9A2, SLCO4A1, POU3F3, and ADARB2 were prominent candidate predictors in establishing a methylation risk score for BLCA (MRSB), which was used to divide the patients into high- and low-risk progression groups (p < 0.001). The effectiveness of the MRSB was validated in the internal cohort (p < 0.001). In the MRSB high-risk group, the hazard curve exhibited an initial wide, high peak within 10 months after treatment, whereas some gentle peaks around 2 years were noted. Furthermore, a nomogram comprising MRSB, age, sex, and tumor clinical stage was developed to predict the individual progression risk, and it performed well. Survival analysis implicated the effectiveness of MRSB, which remains significant in all the subgroup analysis based on the clinical features. A functional analysis of MRSB and the corresponding genes revealed potential pathways affecting tumor progression. Validation of quantitative real-time PCR and western blotting revealed that TNFAIP8L3 was upregulated in the BLCA tissues.

Conclusion

We developed the MRSB, an eight-gene-based methylation signature, which has great potential to be used to predict the post-surgery progression risk of BLCA.

Onwards and Upwards: A Systematic Survey of Economic Evaluation Methods in Oncology

INTRODUCTION

The type of methods used in economic evaluations of health technology can lead to results that may influence decisions. Despite the potential impact on decision making, there is very little documentation of methods used in economic evaluation in oncology pertaining to key assumptions and extrapolation methods of survival benefits, especially in terms of survival analysis techniques and methods for extrapolation.

OBJECTIVES

The primary objectives of this study were to identify, examine, and describe the methods used in economic evaluations in oncology over a 10-year period, while secondary objectives included examining the use of identified methods across different geographic regions.

METHODS

A systematic search of the published oncology literature was conducted to identify economic evaluations of advanced or metastatic cancers published between 2010 and 2019 using the PUBMED, Ovid MEDLINE, and EMBASE databases. A random sample was taken, and information on type of study, data source, modeling techniques, and survival analysis methods were abstracted and descriptively summarized.

RESULTS

A total of 8481 abstracts were identified and 76 economic evaluations were abstracted and assessed. Most identified studies were from North America (38%), East Asia (21%), continental Europe (18%), or the UK (16%), and most commonly focused on lung cancer (18%), colorectal cancer (16%), or breast cancer (13%). A large majority of studies were based on data from randomized controlled trials (82%), utilized a cost-utility approach (82%), and took a public healthcare system perspective (83%). Common model structures included Markov (49%) and partitioned survival (17%). Fitted parametric curves were the most commonly used extrapolation method (89%) for overall survival and most often utilized the Weibull distribution (64%). Secondary assessments showed modest regional variation in the use of identified methods, including the use of fitted parametric curves, testing of the proportional hazards assumption, and validation of results.

CONCLUSION

A majority of papers in the study sample reported basic characteristics of study type, data source used, modeling techniques, and utilization of survival analysis methods. However, greater detail in reporting extrapolation methods, statistical analyses, and validation of results could be potential improvements, especially across regions, in order to support greater consistency in decision making. Future research could document the diffusion of novel modeling techniques into economic evaluation.

Treatment outcome and readmission risk among women in women-only versus mixed-gender drug treatment programs in Chile

Introduction:

Traditional treatment programs for substance use disorder (SUD) tend to be male-dominated environments, which can negatively affect women’s access to treatment and related outcomes. Women’s specific treatment needs have led some providers to develop women-only SUD treatment programs in several countries. In Chile, women-only programs were only fully implemented in 2010. We compared treatment outcomes and readmission risk for adult women admitted to state-funded women-only versus mixed-gender SUD treatment programs in Chile.

Methods:

We used a registry-based retrospective cohort design of adult women in women-only (N = 8200) and mixed-gender (N = 13,178) SUD treatment programs from 2010 to 2019. The study obtained data from the National Drug and Alcohol Service from Chile. We used a multistate model to estimate the probabilities of experiencing treatment completion, discharge without completion (i.e., patient-initiated discharge and administrative discharge), or readmission, as well as the likelihood of being readmitted, conditioned on prior treatment outcome. We adjusted models for multiple baseline characteristics (e.g., substance use, socioeconomic).

Results:

Overall, 24% of women completed treatment and 54% dropped out of treatment. The proportion of patient-initiated discharges within the first three month was larger in women-only than in mixed-gender programs (19% vs. 12%). In both programs, women who completed treatment were more likely to experience readmission at three months, and one and three years. In the long term, women in the women-only programs were more likely to complete treatment than women in mixed-gender programs (34% vs. 23%, respectively). The readmission probability was higher among women who previously completed treatment than those who had a discharge without completion (40% vs 21% among women in women-only programs; 38% vs. 19% among women in mixed-gender programs, respectively); no differences occurred in the risk of readmission between women-only and mixed-gender programs.

Conclusions:

In terms of treatment outcomes and readmission risk, women-only programs had similar results to mixed-gender programs in Chile. The added value of these specialized programs should be addressed in further research.

Estimation of Transition Probabilities for State-Transition Models: A Review of NICE Appraisals

State transition models are used to inform health technology reimbursement decisions. Within state transition models, the movement of patients between the model health states over discrete time intervals is determined by transition probabilities (TPs). Estimating TPs presents numerous issues, including missing data for specific transitions, data incongruence and uncertainty around extrapolation. Inappropriately estimated TPs could result in biased models. There is limited guidance on how to address common issues associated with TP estimation. To assess current methods for estimating TPs and to identify issues that may introduce bias, we reviewed National Institute for Health and Care Excellence Technology Appraisals published from 1 January, 2019 to 27 May, 2020. Twenty-eight models (from 26 Technology Appraisals) were included in the review. Several methods for estimating TPs were identified: survival analysis (n = 11); count method (n = 9); multi-state modelling (n = 7); logistic regression (n = 2); negative binomial regression (n = 2); Poisson regression (n = 1); and calibration (n = 1). Evidence Review Groups identified several issues relating to TP estimation within these models, including important transitions being excluded (n = 5); potential selection bias when estimating TPs for post-randomisation health states (n = 2); issues concerning the use of multiple data sources (n = 4); potential biases resulting from the use of data from different populations (n = 2), and inappropriate assumptions around extrapolation (n = 3). These issues remained unresolved in almost every instance. Failing to address these issues may bias model results and lead to sub-optimal decision making. Further research is recommended to address these methodological problems.

Molecular mechanism study of HGF/c-MET pathway activation and immune regulation for a tumor diagnosis model

Background

Hepatocyte growth factor (HGF) binds to the c-mesenchymal-epithelial transition (C-MET) receptor and activates downstream signaling pathways, playing an essential role in the development of various cancers. Given the role of this signaling pathway, the primary therapeutic direction focuses on identifying and designing HGF inhibitors, antagonists and other molecules to block the binding of HGF to C-MET, thereby limiting the abnormal state of other downstream genes.

Methods

This study focuses on the analysis of immune-related genes and corresponding immune functions that are significantly associated with the HGF/c-MET pathway using transcriptome data from 11 solid tumors.

Results

We systematically analyzed 11 different cancers, including expression correlation, immune infiltration, tumor diagnosis and survival prognosis from HGF/c-MET pathway and immune regulation, two biological mechanisms having received extensive attention in cancer analysis.

Conclusion

We found that the HGF/c-MET pathway affected the tumor microenvironment mainly by interfering with expression levels of other genes. Immune infiltration is another critical factor involved in changes to the tumor microenvironment. The downstream immune-related genes activated by the HGF/c-MET pathway regulate immune-related pathways, which in turn affect the degree of infiltration of immune cells. Immune infiltration is significantly associated with cancer development and prognosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02051-2.

Cost-Effectiveness of Multitarget Stool DNA Testing vs Colonoscopy or Fecal Immunochemical Testing for Colorectal Cancer Screening in Alaska Native People

OBJECTIVE

To estimate the cost-effectiveness of multitarget stool DNA testing (MT-sDNA) compared with colonoscopy and fecal immunochemical testing (FIT) for Alaska Native adults.

PATIENTS AND METHODS

A Markov model was used to evaluate the 3 screening test effects over 40 years. Outcomes included colorectal cancer (CRC) incidence and mortality, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). The study incorporated updated evidence on screening test performance and adherence and was conducted from December 15, 2016, through November 6, 2019.

RESULTS

With perfect adherence, CRC incidence was reduced by 52% (95% CI, 46% to 56%) using colonoscopy, 61% (95% CI, 57% to 64%) using annual FIT, and 66% (95% CI, 63% to 68%) using MT-sDNA. Compared with no screening, perfect adherence screening extends life by 0.15, 0.17, and 0.19 QALYs per person with colonoscopy, FIT, and MT-sDNA, respectively. Colonoscopy is the most expensive strategy: approximately $110 million more than MT-sDNA and $127 million more than FIT. With imperfect adherence (best case), MT-sDNA resulted in 0.12 QALYs per person vs 0.05 and 0.06 QALYs per person by FIT and colonoscopy, respectively. Probabilistic sensitivity analyses supported the base-case analysis. Under varied adherence scenarios, MT-sDNA either dominates or is cost-effective (ICERs, $1740-$75,868 per QALY saved) compared with FIT and colonoscopy.

CONCLUSION

Each strategy reduced costs and increased QALYs compared with no screening. Screening by MT-sDNA results in the largest QALY savings. In Markov model analysis, screening by MT-sDNA in the Alaska Native population was cost-effective compared with screening by colonoscopy and FIT for a wide range of adherence scenarios.

Cost-Effectiveness of Pembrolizumab for the Adjuvant Treatment of Melanoma Patients with Lymph Node Involvement Who Have Undergone Complete Resection in Argentina

Introduction

The KEYNOTE-054 trial demonstrated that adjuvant pembrolizumab improves recurrence-free survival in completely resected stage III melanoma versus watchful waiting (hazard ratio [HR] = 0.57; 98.4% confidence interval [CI], 0.43–0.74). We evaluated the cost-effectiveness of pembrolizumab in Argentina, where watchful waiting is still widely used among these patients despite the high risk of recurrence with surgery alone.

Methods

A four-health state model was used (recurrence-free, locoregional recurrence [LR], distant metastases [DM], death). Lifetime medical costs to payers (72.08 Argentine pesos [AR$] = 1.00 U.S. dollar [USD]) and outcomes (3% annual discount) were assessed, together with incremental cost-effectiveness ratios (ICERs). First and LR→DM recurrences were modeled using KEYNOTE-054 and real-world data, respectively. No benefits of adjuvant treatment were assumed post-progression. Pre-DM and post-DM mortality was based on KEYNOTE-054 and on a network meta-analysis of advanced treatments expected in each arm, respectively. Utilities were derived from KEYNOTE-054 Euro-QoL data using an Argentinian algorithm, and from the literature. Public ex-factory drug prices were used.

Results

Patients in the pembrolizumab and the watchful waiting arms accrued 8.78 and 5.83 quality-adjusted life-years (QALYs), 9.91 and 6.98 life-years, and costs of AR$12,698,595 (176,174 USD) and AR$11,967,717 (166,034 USD), respectively. The proportion of life-years accrued that were recurrence-free was 80.8% and 56.9% in the pembrolizumab and the watchful waiting arms, respectively. Pembrolizumab patients gained 2.94 life-years and 2.96 QALYs versus watchful waiting; the ICER per QALY was AR$247,094 (3428 USD). Recurrence rates and advanced melanoma treatments were the key drivers of the ICER. At a threshold of AR$1,445,325 (29,935 USD) per QALY, pembrolizumab had an 83.5% probability of being cost-effective versus watchful waiting.

Conclusions

Adjuvant pembrolizumab after complete resection of melanoma with node involvement is highly cost-effective relative to watchful waiting in Argentina, across disease stage subgroups and BRAF mutational status. This strongly supports its coverage and reimbursement across the entire health system.

Multistate Modeling of COVID-19 Patients Using a Large Multicentric Prospective Cohort of Critically Ill Patients

The mortality of COVID-19 patients in the intensive care unit (ICU) is influenced by their state at admission. We aimed to model COVID-19 acute respiratory distress syndrome state transitions from ICU admission to day 60 outcome and to evaluate possible prognostic factors. We analyzed a prospective French database that includes critically ill COVID-19 patients. A six-state multistate model was built and 17 transitions were analyzed either using a non-parametric approach or a Cox proportional hazard model. Corticosteroids and IL-antagonists (tocilizumab and anakinra) effects were evaluated using G-computation. We included 382 patients in the analysis: 243 patients were admitted to the ICU with non-invasive ventilation, 116 with invasive mechanical ventilation, and 23 with extracorporeal membrane oxygenation. The predicted 60-day mortality was 25.9% (95% CI: 21.8%-30.0%), 44.7% (95% CI: 48.8%-50.6%), and 59.2% (95% CI: 49.4%-69.0%) for a patient admitted in these three states, respectively. Corticosteroids decreased the risk of being invasively ventilated (hazard ratio (HR) 0.59, 95% CI: 0.39-0.90) and IL-antagonists increased the probability of being successfully extubated (HR 1.8, 95% CI: 1.02-3.17). Antiviral drugs did not impact any transition. In conclusion, we observed that the day-60 outcome in COVID-19 patients is highly dependent on the first ventilation state upon ICU admission. Moreover, we illustrated that corticosteroid and IL-antagonists may influence the intubation duration.

Economic Evaluations of Mental Health Programs for Children and Adolescents in the United States: A Systematic Review

The United States (US) spent 201 billion dollars on mental health-related concerns in 2016, ranking mental illness as the leading cause of disability and the single largest source of economic burden worldwide. With mental health-related treatment costs and economic burden only projected to rise, there is an increasing need for cost-inclusive evaluations of mental health interventions in the US. This systematic review evaluated the intervention characteristics and the quality of 9 economic evaluation studies (e.g., cost-effectiveness, cost-benefit) of youth mental health services conducted in the US from 2003 to 2019. Existing evaluations suggest that certain mental health interventions for youth, among the few that have been formally evaluated, may be cost-effective and cost-beneficial. However, intervention characteristics were generally homogenous, a majority of studies did not adhere to the standard of economic evaluations of the CHEERS checklist, and outcome measures were not consistently clinically useful, limiting the utility of such youth mental health economic evaluations to policymakers. By adhering to standards of economic evaluations and diversifying the characteristics of interventions subject to economic evaluations, intervention researchers can increase confidence in conclusions about which youth mental health interventions are cost-effective or cost-beneficial and more meaningfully inform evidence-based mental health policy.

Choice between implants in knee replacement: protocol for a Bayesian network meta-analysis, analysis of joint registries and economic decision model to determine the effectiveness and cost-effectiveness of knee implants for NHS patients-The KNee Implant Prostheses Study (KNIPS)

INTRODUCTION

Knee replacements are highly successful for many people, but if a knee replacement fails, revision surgery is generally required. Surgeons and patients may choose from a range of implant components and combinations that make up knee replacement constructs, all with potential implications for how long a knee replacement will last. To inform surgeon and patient decisions, a comprehensive synthesis of data from randomised controlled trials is needed to evaluate the effects of different knee replacement implants on overall construct survival. Due to limited follow-up in trials, joint registry analyses are also needed to assess the long-term survival of constructs. Finally, economic modelling can identify cost-effective knee replacement constructs for different patient groups.

METHODS AND ANALYSIS

In this protocol, we describe systematic reviews and network meta-analyses to synthesise evidence on the effectiveness of knee replacement constructs used in total and unicompartmental knee replacement and analyses of two national joint registries to assess long-term outcomes. Knee replacement constructs are defined by bearing materials and mobility, constraint, fixation and patella resurfacing. For men and women in different age groups, we will compare the lifetime cost-effectiveness of knee replacement constructs.

ETHICS AND DISSEMINATION

Systematic reviews are secondary analyses of published data with no ethical approval required. We will design a common joint registry analysis plan and provide registry representatives with information for submission to research or ethics committees. The project has been assessed by the National Health Service (NHS) REC committee and does not require ethical review.Study findings will be disseminated to clinicians, researchers and administrators through open access articles, presentations and websites. Specific UK-based groups will be informed of results including National Institute for Health Research and National Institute for Health and Care Excellence, as well as international orthopaedic associations and charities. Effective dissemination to patients will be guided by our patient-public involvement group and include written lay summaries and infographics.

PROSPERO REGISTRATION NUMBER

CRD42019134059 and CRD42019138015.

Cost-effectiveness of Tisagenlecleucel vs Standard Care in High-risk Relapsed Pediatric Acute Lymphoblastic Leukemia in Canada

Importance

Tisagenlecleucel, a chimeric antigen receptor T-cell therapy for relapsed or refractory pediatric acute lymphoblastic leukemia, has been approved for use in multiple jurisdictions. The public list price is US $475 000, or more than CaD $600 000. Assessing the cost-effectiveness of tisagenlecleucel is necessary to inform policy makers on the economic value of this treatment.

Objective

To assess the value for money of tisagenlecleucel compared with current standard care for tisagenlecleucel-eligible pediatric patients with acute lymphoblastic leukemia under unknown long-term effectiveness.

Design, Setting, and Participants

A cost-utility analysis of tisagenlecleucel compared with current standard care using a Canadian population-based registry of pediatric patients with acute lymphoblastic leukemia was performed. Results from 3 pooled single-arm tisagenlecleucel clinical trials and a provincial pediatric cancer registry were combined to create treatment and control arms, respectively. The population-based control arm consisted of patients meeting clinical trial inclusion and exclusion criteria, starting at second relapse. Multistate and individual-level simulation modeling were combined to predict patient lifetime health trajectories by treatment strategy. Tisagenlecleucel efficacy was modeled across long-term cure rates, from 10% to 40%, to account for limited information on its long-term effectiveness. Uncertainty was tested with 1-way and probabilistic sensitivity analysis. Data were collected in September 2017, and analysis began in December 2017.

Exposures

Tisagenlecleucel compared with current standard care for tisagenlecleucel-eligible patients.

Main Outcomes and Measures

Relative health care costs, survival gains, and quality-adjusted life-years (QALYs) between tisagenlecleucel and current standard care.

Results

The treatment and control arms were modeled on 192 and 118 patients, respectively. The mean (SD) age of control individuals was 10 (4.25) years, and the mean (SD) age of the pooled clinical trial sample was 11 (6) years. The control individuals had 78 boys (66%), and the pooled clinical trial sample had 102 boys (53%). Treatment with tisagenlecleucel was associated with an additional 2.14 to 9.85 life years or 1.68 to 6.61 QALYs, compared with current care. The average additional cost of tisagenlecleucel was CaD $470 013 (US $357 031). Accounting for the total discounted cost over the patient lifetime resulted in an incremental cost of CaD $71 000 (US $53 933) to CaD $281 000 (US $213 453) per QALY gain.

Conclusions and Relevance

To our knowledge, this study offers the first cost-effectiveness analysis of tisagenlecleucel compared with current standard care for pediatric patients with acute lymphoblastic leukemia using a constructed population-based control arm. At a willingness-to-pay threshold of $150 000/QALY, tisagenlecleucel had a 32% likelihood of being cost-effective. Tisagenlecleucel cost-effectiveness would fall below $50 000/QALY with a long-term cure rate of over 0.40 or a price discount of 49% at its currently known effectiveness.

Partitioned Survival and State Transition Models for Healthcare Decision Making in Oncology: Where Are We Now?

OBJECTIVES

Partitioned survival models (PSMs) are routinely used to inform reimbursement decisions for oncology drugs. We discuss the appropriateness of PSMs compared to the most common alternative, state transition models (STMs).

METHODS

In 2017, we published a National Institute for Health and Care Excellence (NICE) Technical Support Document (TSD 19) describing and critically reviewing PSMs. This article summarizes findings from TSD 19, reviews new evidence comparing PSMs and STMs, and reviews recent NICE appraisals to understand current practice.

RESULTS

PSMs evaluate state membership differently from STMs and do not include a structural link between intermediate clinical endpoints (eg, disease progression) and survival. PSMs directly consider clinical trial endpoints and can be developed without access to individual patient data, but limit the scope for sensitivity analyses to explore clinical uncertainties in the extrapolation period. STMs facilitate these sensitivity analyses but require development of robust survival models for individual health-state transitions. Recent work has shown PSMs and STMs can produce substantively different survival extrapolations and that extrapolations from STMs are heavily influenced by specification of the underlying survival models. Recent NICE appraisals have not generally included both model types, reviewed individual clinical event data, or scrutinized life-years accrued in individual health states.

CONCLUSIONS

The credibility of survival predictions from PSMs and STMs, including life-years accrued in individual health states, should be assessed using trial data on individual clinical events, external data, and expert opinion. STMs should be used alongside PSMs to support assessment of clinical uncertainties in the extrapolation period, such as uncertainty in post-progression survival.

A comparison of partitioned survival analysis and state transition multi-state modelling approaches using a case study in oncology

AIMS

To construct and compare a partitioned-survival analysis (PartSA) and a semi-Markov multi-state model (MSM) to investigate differences in estimated cost effectiveness of a novel cancer treatment from a UK perspective.

MATERIALS AND METHODS

Data from a cohort of late-stage cancer patients (N > 700) enrolled within a randomized, controlled trial were used to populate both modelling approaches. The statistical software R was used to fit parametric survival models to overall survival (OS) and progression-free survival (PFS) data to inform the PartSA (package "flexsurv"). The package "mstate" was used to estimate the MSM transitions (permitted transitions: (T1) "progression-free" to "dead", (T2) "post-progression" to "death", and (T3) "pre-progression" to "post-progression"). Key costs included were treatment-related (initial, subsequent, and concomitant), adverse events, hospitalizations and monitoring. Utilities were stratified by progression. Outcomes were discounted at 3.5% per annum over a 15-year time horizon.

RESULTS

The PartSA and MSM approaches estimated incremental cost-effectiveness ratios (ICERs) of £342,474 and £411,574, respectively. Scenario analyses exploring alternative parametric forms provided incremental discounted life-year estimates that ranged from +0.15 to +0.33 for the PartSA approach, compared with -0.13 to +0.23 for the MSM approach. This variation was reflected in the range of ICERs. The PartSA produced ICERs between £234,829 and £522,963, whereas MSM results were more variable and included instances where the intervention was dominated and ICERs above £7 million (caused by very small incremental QALYs).

LIMITATIONS AND CONCLUSIONS

Structural uncertainty in economic modelling is rarely explored due to time and resource limitations. This comparison of structural approaches indicates that the choice of structure may have a profound impact on cost-effectiveness results. This highlights the importance of carefully considered model conceptualization, and the need for further research to ascertain when it may be most appropriate to use each approach.

An Economic Evaluation of Pembrolizumab Versus Other Adjuvant Treatment Strategies for Resected High-Risk Stage III Melanoma in the USA

BACKGROUND AND OBJECTIVE

Over the past 5 years, adjuvant treatment options for surgically resected stage III melanoma have expanded with the introduction of several novel immune checkpoint inhibitors and targeted therapies. Pembrolizumab, a programmed cell death protein 1 inhibitor, received US Food and Drug Administration approval in 2019 for resected high-risk stage III melanoma based on significantly longer recurrence-free survival versus placebo. This study evaluated the cost-effectiveness of pembrolizumab versus other adjuvant treatment strategies for resected high-risk stage III melanoma from a US health system perspective.

METHODS

A Markov cohort-level model with four states (recurrence-free, locoregional recurrence, distant metastases, death) estimated costs and quality-adjusted life-years (QALYs) for pembrolizumab versus routine observation and other adjuvant comparators: ipilimumab in the overall population; and dabrafenib + trametinib in the BRAF-mutation positive (BRAF+) subgroup. Transition probabilities starting from recurrence-free were estimated through parametric multi-state modeling based on phase 3 KEYNOTE-054 (NCT02362594) trial data for pembrolizumab and observation, and network meta-analyses for other comparators. Post-recurrence transitions were modeled based on electronic medical records data and trials in advanced/metastatic melanoma. Utilities were derived using quality-of-life data from KEYNOTE-054 and literature. Costs of treatment, adverse events, disease management, and terminal care were included.

RESULTS

Over a lifetime, pembrolizumab, ipilimumab, and observation were associated with QALYs of 9.24, 7.09, and 5.95 and total costs of $511,290, $992,721, and $461,422, respectively (2019 US dollars). Pembrolizumab was thus dominant (less costly, more effective) versus ipilimumab, with an incremental cost-effectiveness ratio of $15,155/QALY versus observation. In the BRAF+ subgroup, pembrolizumab dominated dabrafenib + trametinib and observation, decreasing costs by $62,776 and $11,250 and increasing QALYs by 0.93 and 3.10 versus these comparators, respectively. Results were robust in deterministic and probabilistic sensitivity analyses.

CONCLUSIONS

As adjuvant treatment for resected stage III melanoma, pembrolizumab was found to be dominant and therefore cost-effective compared with the active comparators ipilimumab and dabrafenib + trametinib. Pembrolizumab increased costs relative to observation in the overall population, with sufficient incremental benefit to be considered cost-effective based on typical willingness-to-pay thresholds.

R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study

INTRODUCTION

Health economics models are typically built in Microsoft Excel^® owing to its wide familiarity, accessibility and perceived transparency. However, given the increasingly rapid and analytically complex decision-making needs of both the pharmaceutical industry and the field of health economics and outcomes research (HEOR), the demands of cost-effectiveness analyses may be better met by the programming language R.

OBJECTIVE

This case study provides an explicit comparison between Excel and R for contemporary cost-effectiveness analysis.

METHODS

We constructed duplicate cost-effectiveness models using Excel and R (with a user interface built using the Shiny package) to address a hypothetical case study typical of contemporary health technology assessment.

RESULTS

We compared R and Excel versions of the same model design to determine the advantages and limitations of the modelling platforms in terms of (i) analytical capability, (ii) data safety, (iii) building considerations, (iv) usability for technical and non-technical users and (v) model adaptability.

CONCLUSIONS

The findings of this explicit comparison are used to produce recommendations for when R might be more suitable than Excel in contemporary cost-effectiveness analyses. We conclude that selection of appropriate modelling software needs to consider case-by-case modelling requirements, particularly (i) intended audience, (ii) complexity of analysis, (iii) nature and frequency of updates and (iv) anticipated model run time.

A risk prediction model of DNA methylation improves prognosis evaluation and indicates gene targets in prostate cancer

Aim: Prostate cancer (PCa) is the most common malignancy found in males worldwide. Although it is mostly indolent, PCa still poses a serious threat to long-term health. Materials & methods: The Cancer Genome Atlas data were randomly divided into training and validation groups. Least absolute shrinkage and selection operator regression on DNA methylation data in the training group was conducted to build the model, which was validated in the validation group. Weighted correlation network analysis was conducted on RNA-seq data to identify the therapy target. Functional validation (western blot, quantitative real-time PCR, cell transfection, Cell Counting Kit-8 assay, colony formation assay, wound healing assay and transwell invasion assay) for the target was conducted. Results: The model is an independent predictor of prognosis. The knockdown of FOXD1 inhibits cell proliferation, migration and invasion of PCa. Conclusion: The risk of patients could be evaluated by the model, which revealed that FOXD1 might promote poor prognosis.

The Clinical Significance of PPEF1 as a Promising Biomarker and Its Potential Mechanism in Breast Cancer

Background

Breast cancer (BC) is the leading cause of malignancy death in females worldwide. While intense efforts have been made to elucidate the pathogeny, the molecular mechanism of BC remains elusive. Thus, this study aimed to investigate the role of PPEF1 in the progression of BC and further explore the better clinical significance.

Methods

The diagnostic and prognostic values of elevated PPEF1 expression in BC were unveiled via public databases analysis. In addition, Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA) and Protein–protein interaction (PPI) analysis were performed to explore the potential functions and molecular mechanisms of PPEF1 in BC progression. Experimentally, transwell and CCK-8 assays were carried out to estimate the effects of PPEF1 on the BC metastasis. Meanwhile, the differential expressions of PPEF1 in paraffin-embedded tissues and serum samples were, respectively, analyzed by Immunohistochemical (IHC) analysis and enzyme-linked immunosorbent assay (ELISA) kit.

Results

The transcriptional levels of PPEF1 were higher in BC than in normal breast tissues or adjacent normal tissues. Moreover, survival analysis revealed that higher PPEF1 expression was negatively associated with overall survival (OS), all events-free (AE-free) and metastatic recurrence-free (MR-free) survival, and further was an independent risk factor of unfavorable prognosis in BC patients. Additionally, the present study provided the first evidence that PPEF1 participated in multiple biological processes and underly signaling pathways involving in tumorigenesis and development of BC. Furthermore, PPEF1 promotes the BC progression and can be used as a noninvasive diagnostic marker. Noteworthy, the combined determination of serum PPEF1 and traditional tumor markers can enhance diagnostic accuracy thus is of vital importance in the early diagnosis of BC.

Conclusion

PPEF1 exerted a tumorigenic role and involved in molecular mechanism of tumorigenesis in BC which served as a promising biomarker for prognosis and diagnosis.

Developing Open-Source Models for the US Health System: Practical Experiences and Challenges to Date with the Open-Source Value Project

The Innovation and Value Initiative started the Open-Source Value Project with the aim to improve the credibility and relevance of model-based value assessment in the context of the US healthcare environment. As a core activity of the Open-Source Value Project, the Innovation and Value Initiative develops and provides access to flexible open-source economic models that are developed iteratively based on public feedback and input. In this article, we describe our experience to date with the development of two currently released, Open-Source Value Project models, one in rheumatoid arthritis and one in epidermal growth factor receptor-positive non-small-cell lung cancer. We developed both Open-Source Value Project models using the statistical programming language R instead of spreadsheet software (i.e., Excel), which allows the models to capture multiple model structures, model sequential treatment with individual patient simulations, and improve integration with formal evidence synthesis. By developing the models in R, we were also able to use version control systems to manage changes to the source code, which is needed for iterative and collaborative model development. Similarly, Open-Source Value Project models are freely available to the public to provide maximum transparency and facilitate collaboration. Development of the rheumatoid arthritis and non-small-cell lung cancer model platforms has presented multiple challenges. The development of multiple components of the model platform tailored to different audiences, including web interfaces, required more resources than a cost-effectiveness analysis for a publication would. Furthermore, we faced methodological hurdles, in particular related to the incorporation of multiple competing model structures and novel elements of value. The iterative development based on public feedback also posed some challenges during the review phase, where methodological experts did not always understand feedback from clinicians and vice versa. Response to the Open-Source Value Project by the modeling community and patient organizations has been positive, but feedback from US decision makers has been limited to date. As we progress with this project, we hope to learn more about the feasibility, benefits, and challenges of an open-source and collaborative approach to model development for value assessment.

Cost-effectiveness of pembrolizumab for the adjuvant treatment of resected high-risk stage III melanoma in the United States

Aims: To evaluate the cost-effectiveness of adjuvant pembrolizumab relative to observation alone following complete resection of high-risk stage III melanoma with lymph node involvement, from a US health system perspective. Materials and methods: A Markov cohort model with four health states (recurrence-free, locoregional recurrence, distant metastases, and death) was developed to estimate costs, life-years, and quality-adjusted life-years (QALYs) associated with pembrolizumab vs observation over a lifetime (46-year) horizon. Using a parametric multi-state modeling approach, transition probabilities starting from recurrence-free were estimated based on patient-level data from KEYNOTE-054 (NCT02362594), a direct head-to-head phase 3 trial. Post-recurrence transition probabilities were informed by real-world retrospective data and clinical trials in advanced melanoma. Health state utilities and adverse event-related disutility were derived from KEYNOTE-054 trial data and published literature. Costs of drug acquisition and administration, adverse events, disease management, and terminal care were estimated in 2018 US dollars. Deterministic and probabilistic sensitivity analyses were conducted to assess robustness. Results: Over a lifetime horizon, adjuvant pembrolizumab and observation were associated with total QALYs of 9.24 and 5.95, total life-years of 10.54 and 7.15, and total costs of $489,820 and $440,431, respectively. The resulting incremental cost-effectiveness ratios (ICERs) for pembrolizumab vs observation were $15,009/QALY and $14,550/life-year. Across the range of input values and assumptions tested in deterministic sensitivity analyses, pembrolizumab ranged from being a dominant strategy to having an ICER of $57,449/QALY vs observation. The ICER was below a willingness-to-pay threshold of $100,000/QALY in 90.2% of probabilistic simulations. Limitations: Long-term extrapolation of outcomes was based on interim results from KEYNOTE-054, with a median follow-up of 15 months. Conclusions: Based on common willingness-to-pay benchmarks, pembrolizumab is highly cost-effective compared with observation alone for the adjuvant treatment of completely resected stage III melanoma in the US.

Statistical primer: a cost-effectiveness analysis

Cost-effectiveness analyses (CEAs) of new treatment strategies are increasingly reported. This can be a part of a clinical trial or as a separate study. Governments and healthcare payers frequently require a CEA to decide whether a new treatment strategy will be reimbursed. CEA is a framework to assess the effectiveness and costs of a new treatment strategy (e.g. a drug or intervention) when compared with a reference strategy. Effectiveness is often measured in life-years or quality-adjusted life-years, whereas costs consist of direct costs (the costs of the treatment), induced costs (downstream costs and cost offsets) and indirect costs. In this statistical primer, the rationale for assessing the economic consequences of new therapies is explained, followed by the fundamental concepts of CEAs, the different types of CEAs and an introduction to interpretation of CEAs. Finally, a real-world example of a CEA is discussed, comparing cost-effectiveness of transcatheter versus surgical aortic valve replacement in patients with severe aortic stenosis at intermediate surgical risk.

R You Still Using Excel? The Advantages of Modern Software Tools for Health Technology Assessment

Economic models are used in health technology assessments (HTAs) to evaluate the cost-effectiveness of competing medical technologies and inform the efficient use of healthcare resources. Historically, these models have been developed with specialized commercial software (such as TreeAge) or more commonly with spreadsheet software (almost always Microsoft Excel). Although these tools may be sufficient for relatively simple analyses, they put unnecessary constraints on the analysis that may ultimately limit its credibility and relevance. In contrast, modern programming languages such as R, Python, Matlab, and Julia facilitate the development of models that are (i) clinically realistic, (ii) capable of quantifying decision uncertainty, (iii) transparent and reproducible, and (iv) reusable and adaptable. An HTA environment that encourages use of modern software can therefore help ensure that coverage and pricing decisions confer greatest possible benefit and capture all scientific uncertainty, thus enabling correct prioritization of future research.

Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.