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Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal.

ABSTRACT: Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies, such as the National Institute for Health and Care Excellence (NICE). These methods use individual patient data from a subset of trials to form population-adjusted indirect comparisons between treatments, in a specific target population. Recently proposed population adjustment methods include the Matching-Adjusted Indirect Comparison (MAIC) and the Simulated Treatment Comparison (STC). Despite increasing popularity, MAIC and STC remain largely untested. Furthermore, there is a lack of clarity about exactly how and when they should be applied in practice, and even whether the results are relevant to the decision problem. There is therefore a real and present risk that the assumptions being made in one submission to a reimbursement agency are fundamentally different to-or even incompatible with-the assumptions being made in another for the same indication. We describe the assumptions required for population-adjusted indirect comparisons, and demonstrate how these may be used to generate comparisons in any given target population. We distinguish between anchored and unanchored comparisons according to whether a common comparator arm is used or not. Unanchored comparisons make much stronger assumptions, which are widely regarded as infeasible. We provide recommendations on how and when population adjustment methods should be used, and the supporting analyses that are required to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal. Simulation studies are needed to examine the properties of population adjustment methods and their robustness to breakdown of assumptions.

SUBMITTER: Phillippo DM

PROVIDER: S-EPMC5774635 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal.

Phillippo David M DM Ades Anthony E AE Dias Sofia S Palmer Stephen S Abrams Keith R KR Welton Nicky J NJ

Medical decision making : an international journal of the Society for Medical Decision Making 20170819 2

Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies, such as the National Institute for Health and Care Excellence (NICE). These methods use individual patient data from a subset of trials to form population-adjusted indir ...[more]

PMID: 28823204

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Project description:ObjectiveRimegepant is an orally administered small-molecule calcitonin gene-related peptide (CGRP) receptor antagonist, with demonstrated efficacy in the acute treatment of migraine. Recent estimates from a single-arm trial (BHV3000-201) have also shown evidence of long-term preventive effects in monthly migraine days (MMDs) and health-related quality of life (HRQoL). This study aimed to compare MMDs and HRQoL data for oral rimegepant to those obtained in placebo-controlled trials for injectable anti-CGRP monoclonal antibodies (mAbs) galcanezumab and erenumab.MethodsMatching-adjusted indirect comparisons (MAICs) were conducted using rimegepant subject-level data and published aggregate-level results from mAb trials. Rimegepant baseline characteristics were matched to the pooled subject characteristics from EVOLVE-I/II (galcanezumab vs. placebo; n = 1773) and STRIVE (ereumab vs. placebo; n = 955) by reweighting the rimegepant subjects to more closely match the distributions observed in these trials. To align with inclusion criteria of the mAb trials, only the subset of rimegepant subjects with a history of 4-14 MMDs were included (n = 257). Weighted mean differences were used to calculate adjusted change in MMDs, Migraine Disability Assessment Test (MIDAS) score, and Migraine-Specific Quality of Life Questionnaire version 2 (MSQv2) scores from baseline to week 12.ResultsWhen matched to the EVOLVE trials, rimegepant was superior to placebo with a mean difference in MMD change from baseline [95% confidence interval] of -1.16 [-1.80, -0.52] and was not statistically significantly different from galcanezumab 0.59 [-0.13, 1.32]. When matched to the STRIVE trial, rimegepant was superior to placebo -1.59 [-2.15, -1.03] and was not statistically significantly different from erenumab -0.06 [-0.61, 0.50]. Rimegepant showed superior MIDAS and MSQv2 results compared with placebo in both EVOLVE trials and in the STRIVE trial, no statistically significant differences from galcanezumab and erenumab regarding MIDAS, and favorable results compared with erenumab across all MSQv2 domains, while being generally similar to galcanezumab across all MSQv2 domains.ConclusionsWhen adjustments were made to reflect baseline characteristics in published literature, supporting data from BHV3000-201 suggest that rimegepant every other day is an effective therapy in reducing disability and MMDs and enhancing migraine-specific HRQoL. These data support the preventive benefit observed in randomized trials of rimegepant and further validate its efficacy for both acute and preventive treatment of migraine.

Project description:BackgroundAnchored 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.MethodsA 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.ResultsDespite 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.ConclusionsTwo-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.

Project description:BackgroundThree oral drugs (ambrisentan, bosentan, and sildenafil) have been widely used to treat patients with pulmonary arterial hypertension (PAH). 1) There are no studies that directly compare the safety and efficacy of these three drugs. Existing studies could not meet the physician's need to select the most beneficial drugs for patients. 2) Principal component analysis is mainly used for scale analysis and has not been reported in clinical field. 3) When the results of the indirect meta-analysis were not satisfactory, no new solutions have been proposed in existing meta-analysis studies.MethodsThe overall process of this study is divided into 4 steps 1) literature search and data extraction; 2) principal component analysis; 3) common reference-based indirect comparison meta-analysis; 4) formal adjusted indirect comparison.ResultsNine randomized controlled trials (RCTs) experiments and eight long-term experiments were selected. The main influencing factors are mortality, 6-min walk distance (6MW), mean pulmonary arterial pressure (PAP), cardiac index (CI) by principal component analysis. There was no significant heterogeneity among the indirect meta-analysis of three drugs. But in the formal adjusted indirect comparison 1) the level of PAP of sildenafil group (60.5 ± 22.35, 220) was higher than that of the other three groups, placebo (53.5 ± 17.63, 507) (p < 0.001), ambrisentan (49.5 ± 15.08, 130) (p < 0.001), and bosentan (54.6 ± 118.41, 311) (p < 0.001); 2) the level of CI of sildenafil group (54 ± 18, 311) was higher than that of the other three groups, placebo (2.7 ± 1.09, 518) (p < 0.001), ambrisentan (2.5 ± 0.75, 130) (p < 0.001), and bosentan (2.5 ± 1.06, 333) (p < 0.001). In addition, sildenafil significantly improved the survival rate comparing with ambrisentan and bosentan.ConclusionsThe results of this study suggest that sildenafil might be more suitable for long-term treatment of PAH patients than ambrisentan and bosentan. In order to enable clinicians to draw conclusions more quickly and directly in the data-rich literature, we suggest the use of principal component analysis combined with formal adjusted indirect comparison to compare the efficacy and safety of drugs.

Project description:IntroductionRandomized 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.MethodsThe 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.ResultsFive 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%).ConclusionsThe 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.

Project description:ObjectiveWe aim to develop a comprehensive checklist for evaluating Health Technology Assessment (HTA) studies commissioned in India. The primary objective of this work is to capture all vital aspects of an HTA study in terms of conduct, reporting and quality.MethodologyThe development of a quality appraisal checklist included 3 steps. First, a targeted review of the literature was done to gather information on existing HTA checklists. After reviewing these checklists, an initial draft of the HTA quality appraisal checklist (HTA-QAC) for India was prepared with discussion amongst the authors. Second, the draft checklist was reviewed by the members of the Technical Appraisal Committee (TAC) and their feedback was incorporated. Subsequently, the revised checklist was presented at a virtual meeting of the TAC. Finally, a pilot phase was undertaken to apply HTA-QAC for the approved HTA study reports. Three rounds of virtual discussions were held with the researchers who were involved in the conduct of these HTA studies to resolve any discordance in opinion or develop solutions for the problems in the use of the HTA-QAC followed by a further revision of the checklist.ResultsThe HTA-QAC is divided into two parts: a self-reporting section to be completed by the author, and the other to be completed by the reviewer. The reviewer checklist has two sections: one to review the report and the other to review the model. The author section is in a self-reporting format, which includes details of basic study information, the rationale for the study, policy relevance, study description, study methods, reporting of model parameters, and results. The reviewer section of the checklist focuses on the quality aspect of the conducted study. The domains included in the report review include details on study methodology, results, discussion, and conclusion. The second part of the reviewer section of HTA-QAC constitutes a review of the model in terms of model assumptions, functionality, model inputs, calculations, uncertainty analysis, model output, and model validation.ConclusionWe recommend a standardised process of quality appraisal to ensure the high quality of HTA evidence for policy use in the Indian context. The proposed HTA-QAC will help authors to ensure standardised reporting, as well as allow reviewers to assess the quality of analysis.

Dataset Information

Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal.

Publications

Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal.

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