Challenges of Incorporating Life Cycle Drug Pricing in Cost-Effectiveness Models: A Review of Methods and Modeling Suggestions

OBJECTIVES

This study aimed to conduct a review of existing methods used to incorporate life cycle drug pricing (LCDP) in cost-effectiveness analyses (CEAs), identify common methodological challenges, and suggest modeling approaches for prospectively implementing LCDP in CEA.

METHODS

Two complementary searches were conducted in PubMed, combined with hand searching and reference mining, to identify English language full-text articles that explored (1) how drug prices change over time and (2) methods used to apply dynamic pricing in cost-effectiveness models (CEMs). Relevant articles were reviewed, and authors discussed the common methodological practices used in the literature and their associated challenges on prospectively implementing LCDP in CEMs. For each key challenge identified, we provide modeling suggestions to address the issue.

RESULTS

We screened 1200 studies based on title and abstract; 117 were reviewed for eligibility, and 47 individual studies were included across both searches. Variations in prices over a product's life cycle are complex and multifactorial, and models applying LCDP in CEA varied in their methodology. We identified 4 key challenges to modeling LCDP in CEA, including how to model price trends before and after loss of exclusivity, how to capture the effect of price changes on future patient cohorts, and how to report results.

CONCLUSION

Accurately quantifying the impact of LCDP requires careful consideration of multiple aspects pertaining to both the evolution of drug prices and how to reflect these in CEA. Although uncertainties remain, our findings can aid implementation and evaluation of LCDP in economic evaluations.

Implementation of Ticagrelor Reduced Mortality in Routine Clinical Care: Evidence From a Natural Experiment Including 109 995 Patients With Myocardial Infarction in Sweden

Background Effectiveness estimates from observational studies on ticagrelor use in routine clinical care are conflicting, with some contrary to the results of the pivotal randomized controlled trial of ticagrelor in acute coronary syndrome. The aim of this study was to estimate the effect of implementing and using ticagrelor in routine clinical care in patients with myocardial infarction using a natural experimental approach. Methods and Results This is a retrospective cohort study including patients hospitalized for myocardial infarction in Sweden between 2009 and 2015. The study exploited differences in the timing and speed of ticagrelor implementation between treatment centers as a source of random treatment assignment. The effect of implementing and using ticagrelor was estimated based on the admitting center's likelihood of treating patients with ticagrelor, measured as the proportion of patients treated in the 90 days before patient admission. The main outcome was 12-month mortality. The study included 109 955 patients, of whom 30 773 were treated with ticagrelor. Being admitted to a treatment center with higher past ticagrelor use was associated with a reduction in 12-month mortality (2.5 percentage points for 100% versus 0% past use [95% CI, 0.2-4.8]). The results are in line with the findings from the ticagrelor pivotal trial. Conclusions Using a natural experiment, this study finds that the implementation and use of ticagrelor in routine clinical care has reduced 12-month mortality in patients admitted to the hospital with myocardial infarction in Sweden and supports the external validity of randomized evidence on ticagrelor effectiveness.

Use of Advanced Flexible Modeling Approaches for Survival Extrapolation from Early Follow-up Data in two Nivolumab Trials in Advanced NSCLC with Extended Follow-up

OBJECTIVES

Immuno-oncology (IO) therapies are often associated with delayed responses that are deep and durable, manifesting as long-term survival benefits in patients with metastatic cancer. Complex hazard functions arising from IO treatments may limit the accuracy of extrapolations from standard parametric models (SPMs). We evaluated the ability of flexible parametric models (FPMs) to improve survival extrapolations using data from 2 trials involving patients with non-small-cell lung cancer (NSCLC).

METHODS

Our analyses used consecutive database locks (DBLs) at 2-, 3-, and 5-y minimum follow-up from trials evaluating nivolumab versus docetaxel in patients with pretreated metastatic squamous (CheckMate-017) and nonsquamous (CheckMate-057) NSCLC. For each DBL, SPMs, as well as 3 FPMs-landmark response models (LRMs), mixture cure models (MCMs), and Bayesian multiparameter evidence synthesis (B-MPES)-were estimated on nivolumab overall survival (OS). The performance of each parametric model was assessed by comparing milestone restricted mean survival times (RMSTs) and survival probabilities with results obtained from externally validated SPMs.

RESULTS

For the 2- and 3-y DBLs of both trials, all models tended to underestimate 5-y OS. Predictions from nonvalidated SPMs fitted to the 2-y DBLs were highly unreliable, whereas extrapolations from FPMs were much more consistent between models fitted to successive DBLs. For CheckMate-017, in which an apparent survival plateau emerges in the 3-y DBL, MCMs fitted to this DBL estimated 5-y OS most accurately (11.6% v. 12.3% observed), and long-term predictions were similar to those from the 5-y validated SPM (20-y RMST: 30.2 v. 30.5 mo). For CheckMate-057, where there is no clear evidence of a survival plateau in the early DBLs, only B-MPES was able to accurately predict 5-y OS (14.1% v. 14.0% observed [3-y DBL]).

CONCLUSIONS

We demonstrate that the use of FPMs for modeling OS in NSCLC patients from early follow-up data can yield accurate estimates for RMST observed with longer follow-up and provide similar long-term extrapolations to externally validated SPMs based on later data cuts. B-MPES generated reasonable predictions even when fitted to the 2-y DBLs of the studies, whereas MCMs were more reliant on longer-term data to estimate a plateau and therefore performed better from 3 y. Generally, LRM extrapolations were less reliable than those from alternative FPMs and validated SPMs but remained superior to nonvalidated SPMs. Our work demonstrates the potential benefits of using advanced parametric models that incorporate external data sources, such as B-MPES and MCMs, to allow for accurate evaluation of treatment clinical and cost-effectiveness from trial data with limited follow-up.

HIGHLIGHTS

Flexible advanced parametric modeling methods can provide improved survival extrapolations for immuno-oncology cost-effectiveness in health technology assessments from early clinical trial data that better anticipate extended follow-up.Advantages include leveraging additional observable trial data, the systematic integration of external data, and more detailed modeling of underlying processes.Bayesian multiparameter evidence synthesis performed particularly well, with well-matched external data.Mixture cure models also performed well but may require relatively longer follow-up to identify an emergent plateau, depending on the specific setting.Landmark response models offered marginal benefits in this scenario and may require greater numbers in each response group and/or increased follow-up to support improved extrapolation within each subgroup.

What Did Time Tell Us? A Comparison and Retrospective Validation of Different Survival Extrapolation Methods for Immuno-Oncologic Therapy in Advanced or Metastatic Renal Cell Carcinoma

BACKGROUND

The immuno-oncologic (IO) mechanism of action may lead to an overall survival (OS) hazard that changes over time, producing shapes that standard parametric extrapolation methods may struggle to reflect. Furthermore, selection of the most appropriate extrapolation method for health technology assessment is often based on trial data with limited follow-up.

OBJECTIVE

To examine this problem, we fitted a range of extrapolation methods to patient-level survival data from CheckMate 025 (NCT01668784, CM-025), a phase III trial comparing nivolumab with everolimus for previously treated advanced renal cell carcinoma (aRCC), to assess their predictive accuracy over time.

METHODS

Six extrapolation methods were examined: standard parametric models, natural cubic splines, piecewise models combining Kaplan-Meier data with an exponential or non-exponential distribution, response-based landmark models, and parametric mixture models. We produced three database locks (DBLs) at minimum follow-ups of 15, 27, and 39 months to align with previously published CM-025 data. A three-step evaluation process was adopted: (1) selection of the distribution family for each method in each of the three DBLs, (2) internal validation comparing extrapolation-based landmark and mean survival with the latest CM-025 dataset (minimum follow-up, 64 months), and (3) external validation of survival projections using clinical expert opinion and long-term follow-up data from other nivolumab studies in aRCC (CheckMate 003 and CheckMate 010).

RESULTS

All extrapolation methods, with the exception of mixture models, underestimated landmark and mean OS for nivolumab compared with CM-025 long-term follow-up data. OS estimates for everolimus tended to be more accurate, with four of the six methods providing landmark OS estimates within the 95% confidence interval of observed OS as per the latest dataset. The predictive accuracy of survival extrapolation methods fitted to nivolumab also showed greater variation than for everolimus. The proportional hazards assumption held for all DBLs, and a dependent log-logistic model provided reliable estimates of longer-term survival for both nivolumab and everolimus across the DBLs. Although mixture models and response-based landmark models provided reasonable estimates of OS based on the 39-month DBL, this was not the case for the two earlier DBLs. The piecewise exponential models consistently underestimated OS for both nivolumab and everolimus at clinically meaningful pre-specified landmark time points.

CONCLUSIONS

This aRCC case study identified marked differences in the predictive accuracy of survival extrapolation methods for nivolumab but less so for everolimus. The dependent log-logistic model did not suffer from overfitting to early DBLs to the same extent as more complex methods. Methods that provide more degrees of freedom may accurately represent survival for IO therapy, particularly if data are more mature or external data are available to inform the long-term extrapolations.

Subcategorizing the Expected Value of Perfect Implementation to Identify When and Where to Invest in Implementation Initiatives

Purpose. Clinical practice variations and low implementation of effective and cost-effective health care technologies are a key challenge for health care systems and may lead to suboptimal treatment and health loss for patients. The purpose of this work was to subcategorize the expected value of perfect implementation (EVPIM) to enable estimation of the absolute and relative value of eliminating slow, low, and delayed implementation. Methods. Building on the EVPIM framework, this work defines EVPIM subcategories to estimate the expected value of eliminating slow, low, or delayed implementation. The work also shows how information on regional implementation patterns can be used to estimate the value of eliminating regional implementation variation. The application of this subcategorization is illustrated by a case study of the implementation of an antiplatelet therapy for the secondary prevention after myocardial infarction in Sweden. Incremental net benefit (INB) estimates are based on published cost-effectiveness assessments and a threshold of SEK 250,000 (£22,300) per quality-adjusted life year (QALY). Results. In the case study, slow, low, and delayed implementation was estimated to represent 22%, 34%, and 44% of the total population EVPIM (2941 QALYs or SEK 735 million), respectively. The value of eliminating implementation variation across health care regions was estimated to 39% of total EVPIM (1138 QALYs). Conclusion. Subcategorizing EVPIM estimates the absolute and relative value of eliminating different parts of suboptimal implementation. By doing so, this approach could help decision makers to identify which parts of suboptimal implementation are contributing most to total EVPIM and provide the basis for assessing the cost and benefit of implementation activities that may address these in future implementation of health care interventions.

ELISPOT assays provide reproducible results among different laboratories for T-cell immune monitoring--even in hands of ELISPOT-inexperienced investigators

Measurements of antibodies in bodily fluids (e.g., by ELISA) have provided robust and reproducible results for decades and such assays have been validated for monitoring of B-cell immunity. In contrast, measuring T-cell immunity has proven to be a challenge due to the need to test live cells in functional assays ex vivo. Several previous efforts looking into the reproducibility of ex vivo T-cell assays between different laboratories, or even within the same laboratory, have provided rather discouraging results. The hypothesis we tested in this study is that those poor results are due to the lack of assay and data analysis standardization, rather than the inherent complexity of T-cell assays. In this study, 11 laboratories across Europe and the United States were provided identical reagents and were asked to follow the same protocol while testing aliquots of the same three cryopreserved peripheral blood mononuclear cells (PBMC) in an interferon-gamma (IFNgamma) ELISPOT assay measuring the antigen-specific T-cell response to a CMV peptide. All individuals performing the assays were ELISPOT novices. At their first attempt, while three of these individuals failed with the basic logistics of the trial, eight detected the peptide-specific CD8+ T-cells in frequencies approximating the values established by the Reference Laboratory. The data show that ELISPOT assays provide reproducible results among different laboratories when the assay procedure and data analysis is standardized. Since ELISPOT assays have been qualified and validated for regulated studies, they are ideal candidates for robust and reproducible monitoring of T-cell activity in vivo.

[Distal esophageal banding. A modified method of surgical treatment of obesity]

64 patients were treated with distal oesophageal banding for morbid obesity from 30/4-1984 to 1/6-1988. By this method a Dacron vascular prosthesis is passed around the oesophagus from incisura cardiaca to pars cardiaca. There will be no upper ventricular pouch. The method was introduced in order to avoid the problems seen after operation with gastric banding, such as valve mechanism secondary to pouch distension and band penetration. Such problems have not been encountered following our operative modification. 18 patients were observed for more than two years. The mean weight loss one year after operation was 28 kg, which was equivalent to 26% of preoperative weight. The main problem is excessive tightening of the band. Therefore, in three patients, the band had to be removed later.

Preparation and characterization of total, rough and smooth microsomes from the lung of control and methylcholanthrene-treated rats

Optimal conditions for the preparation of relatively pure microsomes and microsomal subfractions from rat lung have been determined. The most importnat of these conditions is homogenization of a 20% (w/v) suspension of lung tissue in 0.44 M sucrose/1% (w/v) bovine serum albumin with four up-and-down strokes at 440 rev./min in a Potter-Elvehjem homogenizer. The 10000 X g supernatant prepared from this homogenate can be centrifuged at 105000 X g to obtain total microsomes or subfractionated into rough and smooth microsomes on a Cs+-containing discontinuous sucrose gradient. The total, rough and smooth microsomes have been characterized in terms of their chemical composition, enzymatic activity, and morphology. These preparations should prove useful in studies of various enzymes in lung (e.g. benzpyrene monooxygenase, epoxide hydrase, enzymes of phospholipid and ascorbic acid synthesis) and in subfractionations designed to reveal heterogeneites in the lateral plane of the lung endoplasmic reticulum.