Comparison of Methods for Estimating Therapy Effects by Indirect Comparisons: A Simulation Study

Methods for Indirect Treatment Comparison: Results from a Systematic Literature Review

INTRODUCTION

Health technology assessment (HTA) agencies express a clear preference for randomized controlled trials when assessing the comparative efficacy of two or more treatments. However, an indirect treatment comparison (ITC) is often necessary where a direct comparison is unavailable or, in some cases, not possible. Numerous ITC techniques are described in the literature. A systematic literature review (SLR) was conducted to identify all the relevant literature on existing ITC techniques, provide a comprehensive description of each technique and evaluate their strengths and limitations from an HTA perspective in order to develop guidance on the most appropriate method to use in different scenarios.

METHODS

Electronic database searches of Embase and PubMed, as well as grey literature searches, were conducted on 15 November 2021. Eligible articles were peer-reviewed papers that specifically described the methods used for different ITC techniques and were written in English. The review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

A total of 73 articles were included in the SLR, reporting on seven different ITC techniques. All reported techniques were forms of adjusted ITC. Network meta-analysis (NMA) was the most frequently described technique (in 79.5% of the included articles), followed by matching-adjusted indirect comparison (MAIC) (30.1%), network meta-regression (24.7%), the Bucher method (23.3%), simulated treatment comparison (STC) (21.9%), propensity score matching (4.1%) and inverse probability of treatment weighting (4.1%). The appropriate choice of ITC technique is critical and should be based on the feasibility of a connected network, the evidence of heterogeneity between and within studies, the overall number of relevant studies and the availability of individual patient-level data (IPD). MAIC and STC were found to be common techniques in the case of single-arm studies, which are increasingly being conducted in oncology and rare diseases, whilst the Bucher method and NMA provide suitable options where no IPD is available.

CONCLUSION

ITCs can provide alternative evidence where direct comparative evidence may be missing. ITCs are currently considered by HTA agencies on a case-by-case basis; however, their acceptability remains low. Clearer international consensus and guidance on the methods to use for different ITC techniques is needed to improve the quality of ITCs submitted to HTA agencies. ITC techniques continue to evolve quickly, and more efficient techniques may become available in the future.

Advancing unanchored simulated treatment comparisons: A novel implementation and simulation study

Population-adjusted indirect comparisons, developed in the 2010s, enable comparisons between two treatments in different studies by balancing patient characteristics in the case where individual patient-level data (IPD) are available for only one study. Health technology assessment (HTA) bodies increasingly rely on these methods to inform funding decisions, typically using unanchored indirect comparisons (i.e., without a common comparator), due to the need to evaluate comparative efficacy and safety for single-arm trials. Unanchored matching-adjusted indirect comparison (MAIC) and unanchored simulated treatment comparison (STC) are currently the only two approaches available for population-adjusted indirect comparisons based on single-arm trials. However, there is a notable underutilisation of unanchored STC in HTA, largely due to a lack of understanding of its implementation. We therefore develop a novel way to implement unanchored STC by incorporating standardisation/marginalisation and the NORmal To Anything (NORTA) algorithm for sampling covariates. This methodology aims to derive a suitable marginal treatment effect without aggregation bias for HTA evaluations. We use a non-parametric bootstrap and propose separately calculating the standard error for the IPD study and the comparator study to ensure the appropriate quantification of the uncertainty associated with the estimated treatment effect. The performance of our proposed unanchored STC approach is evaluated through a comprehensive simulation study focused on binary outcomes. Our findings demonstrate that the proposed approach is asymptotically unbiased. We argue that unanchored STC should be considered when conducting unanchored indirect comparisons with single-arm studies, presenting a robust approach for HTA decision-making.

The Acceptance of Indirect Treatment Comparison Methods in Oncology by Health Technology Assessment Agencies in England, France, Germany, Italy, and Spain

INTRODUCTION

Randomized controlled trials (RCTs) are the gold standard when comparing treatment effectiveness, and Health Technology Assessment (HTA) agencies state a clear preference for such direct comparisons. When these are not available, an indirect treatment comparison (ITC) is an alternative option. The objective of this study was to assess the acceptance of ITC methods by HTA agencies across England, France, Germany, Italy, and Spain, using oncology cases for a homogeneous sample of HTA evaluations.

METHODS

The study was conducted on the PrismAccess database in May 2021 to retrieve HTA evaluation reports for oncology treatments for solid tumors, in which an ITC was presented. The analysis was restricted to HTA evaluation reports published between April 2018 and April 2021 in England, France, Germany, Italy, and Spain. Identified HTA evaluation reports were screened and reviewed by two independent reviewers. For each ITC presented, the methodology and its acceptance by the HTA agency were analyzed.

RESULTS

Five hundred and forty-three HTA evaluation reports were identified, of which 120 (22%) presented an ITC. This proportion was the highest in England (51%) and lowest in France (6%). The overall acceptance rate of ITC methods was 30%, with the highest in England (47%) and lowest in France (0%). Network meta-analysis (NMA; 23%) was the most commonly used ITC technique, with a 39% acceptance rate overall, followed by Bucher ITC (19%; 43% acceptance rate) and matching-adjusted indirect comparison (13%; 33% acceptance rate). The most common criticisms of the ITC methods from HTA agencies related to data limitations (heterogeneity and lack of data; 48% and 43%, respectively) and the statistical methods used (41%).

CONCLUSIONS

The generally low acceptance rate of ITC methods by HTA agencies in oncology suggests that, whilst in the absence of a direct comparison ITCs may provide relevant evidence, this evidence is not widely considered sufficient for the purpose of HTA evaluations. The perception of ITC methods for the purpose of HTA evaluations varies substantially between countries. There is a need for further clarity on the properties of ITC techniques and the assessment of their results as ITC methods continue to evolve quickly and further techniques may become available in the future.

Clinical Efficacy of Sarilumab Versus Upadacitinib Over 12 weeks: An Indirect Treatment Comparison

INTRODUCTION

The efficacy of sarilumab and upadacitinib, in combination with disease-modifying antirheumatic drugs (DMARDs), was demonstrated in phase 3 clinical trials of patients with rheumatoid arthritis (RA) refractive to previous biologic DMARDs. In the absence of head-to-head clinical trials, the matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC) estimate the relative efficacy of sarilumab and upadacitinib in patients with RA who had an inadequate response to previous biologic DMARDs.

METHODS

Patient-level data for sarilumab were obtained from the TARGET trial (NCT01709578) and published aggregate data for upadacitinib were obtained from the SELECT-BEYOND trial (NCT02706847). For the MAIC, individual patient data from the TARGET trial were assigned weights such that weighted mean baseline characteristics of the treatment effect modifiers matched those from SELECT-BEYOND. For the STC, the TARGET patient-level data and mean baseline values from SELECT-BEYOND were used to simulate sarilumab treatment effects for a SELECT-BEYOND population. Endpoints evaluated included the American College of Rheumatology (ACR) response criteria ACR20/50/70, Disease Activity Score-28 for Rheumatoid Arthritis with C-reactive protein (DAS28-CRP) < 3.2, DAS28-CRP < 2.6, Simple Disease Activity Index (SDAI) < 3.3, and Clinical Disease Activity Index (CDAI) < 2.8 at 12 weeks.

RESULTS

The analysis included 365 patients from TARGET and aggregated data of 333 patients from SELECT-BEYOND. Matching for potential treatment effect baseline modifiers (i.e., age, oral glucocorticoid use, tender joint count of 68 counts, swollen joint count of 66 counts, serum CRP level, and patient global assessment of disease activity) resulted in a reduction of the effective sample size of TARGET population to 166. Following MAIC and STC analysis, the odds of achieving all aforementioned clinical outcomes versus placebo at week 12 were similar for sarilumab and upadacitinib.

CONCLUSION

In the MAIC and STC analyses from TARGET and SELECT-BEYOND trials, the efficacy of sarilumab and upadacitinib were comparable.

Two-stage matching-adjusted indirect comparison

BACKGROUND

Anchored covariate-adjusted indirect comparisons inform reimbursement decisions where there are no head-to-head trials between the treatments of interest, there is a common comparator arm shared by the studies, and there are patient-level data limitations. Matching-adjusted indirect comparison (MAIC), based on propensity score weighting, is the most widely used covariate-adjusted indirect comparison method in health technology assessment. MAIC has poor precision and is inefficient when the effective sample size after weighting is small.

METHODS

A modular extension to MAIC, termed two-stage matching-adjusted indirect comparison (2SMAIC), is proposed. This uses two parametric models. One estimates the treatment assignment mechanism in the study with individual patient data (IPD), the other estimates the trial assignment mechanism. The first model produces inverse probability weights that are combined with the odds weights produced by the second model. The resulting weights seek to balance covariates between treatment arms and across studies. A simulation study provides proof-of-principle in an indirect comparison performed across two randomized trials. Nevertheless, 2SMAIC can be applied in situations where the IPD trial is observational, by including potential confounders in the treatment assignment model. The simulation study also explores the use of weight truncation in combination with MAIC for the first time.

RESULTS

Despite enforcing randomization and knowing the true treatment assignment mechanism in the IPD trial, 2SMAIC yields improved precision and efficiency with respect to MAIC in all scenarios, while maintaining similarly low levels of bias. The two-stage approach is effective when sample sizes in the IPD trial are low, as it controls for chance imbalances in prognostic baseline covariates between study arms. It is not as effective when overlap between the trials' target populations is poor and the extremity of the weights is high. In these scenarios, truncation leads to substantial precision and efficiency gains but induces considerable bias. The combination of a two-stage approach with truncation produces the highest precision and efficiency improvements.

CONCLUSIONS

Two-stage approaches to MAIC can increase precision and efficiency with respect to the standard approach by adjusting for empirical imbalances in prognostic covariates in the IPD trial. Further modules could be incorporated for additional variance reduction or to account for missingness and non-compliance in the IPD trial.

A comparison of methods for enriching network meta-analyses in the absence of individual patient data

During drug development, a biomarker is sometimes identified as separating a patient population into those with more and those with less benefit from evaluated treatments. Consequently, later studies might be targeted, while earlier ones are performed in mixed patient populations. This poses a challenge in evidence synthesis, especially if only aggregated data are available. Starting from this scenario, we investigate three commonly used network meta-analytic estimation methods, the naive estimation approach, the stand-alone analysis, and the network meta-regression. Additionally, we adapt and modify two methods which are used in evidence synthesis to combine randomized controlled trials with observational studies, the enrichment-through-weighting approach and the informative prior estimation. We evaluate all five methods in a simulation study with 32 scenarios using bias, RMSE, coverage, precision, and power. Additionally, we re-visit a clinical data set to exemplify and discuss the application. In the simulation study, none of the methods was observed to be clearly favorable over all investigated scenarios. However, the stand-alone analysis and the naive estimation performed comparably or worse than the other methods in all evaluated performance measures and simulation scenarios and are therefore not recommended. While substantial between-trial heterogeneity is challenging for all estimation approaches, the performance of the network meta-regression, the enriching-through weighting approach and the informative prior approach was dependent on the simulation scenario and the performance measure of interest. Furthermore, as these estimation methods are drawing slightly different assumptions, some of which require the presence of additional information for estimation, we recommend sensitivity-analyses wherever possible. This article is protected by copyright. All rights reserved.