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Economic evaluation of factorial randomised controlled trials: challenges, methods and recommendations.

ABSTRACT: Increasing numbers of economic evaluations are conducted alongside randomised controlled trials. Such studies include factorial trials, which randomise patients to different levels of two or more factors and can therefore evaluate the effect of multiple treatments alone and in combination. Factorial trials can provide increased statistical power or assess interactions between treatments, but raise additional challenges for trial-based economic evaluations: interactions may occur more commonly for costs and quality-adjusted life-years (QALYs) than for clinical endpoints; economic endpoints raise challenges for transformation and regression analysis; and both factors must be considered simultaneously to assess which treatment combination represents best value for money. This article aims to examine issues associated with factorial trials that include assessment of costs and/or cost-effectiveness, describe the methods that can be used to analyse such studies and make recommendations for health economists, statisticians and trialists. A hypothetical worked example is used to illustrate the challenges and demonstrate ways in which economic evaluations of factorial trials may be conducted, and how these methods affect the results and conclusions. Ignoring interactions introduces bias that could result in adopting a treatment that does not make best use of healthcare resources, while considering all interactions avoids bias but reduces statistical power. We also introduce the concept of the opportunity cost of ignoring interactions as a measure of the bias introduced by not taking account of all interactions. We conclude by offering recommendations for planning, analysing and reporting economic evaluations based on factorial trials, taking increased analysis costs into account. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

SUBMITTER: Dakin H

PROVIDER: S-EPMC5599939 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Partial factorial trials compare two or more pairs of treatments on overlapping patient groups, randomising some (but not all) patients to more than one comparison. The aims of this research were to compare different methods for conducting and analysing economic evaluations on partial factorial trials and assess the implications of considering factors simultaneously rather than drawing independent conclusions about each comparison. METHODS:We estimated total costs and quality-adjusted life years (QALYs) within 10 years of surgery for 2252 patients in the Knee Arthroplasty Trial who were randomised to one or more comparisons of different surgical types. We compared three analytical methods: an "at-the-margins" analysis including all patients randomised to each comparison (assuming no interaction); an "inside-the-table" analysis that included interactions but focused on those patients randomised to two comparisons; and a Bayesian vetted bootstrap, which used results from patients randomised to one comparison as priors when estimating outcomes for patients randomised to two comparisons. Outcomes comprised incremental costs, QALYs and net benefits. RESULTS:Qualitative interactions were observed for costs, QALYs and net benefits. Bayesian bootstrapping generally produced smaller standard errors than inside-the-table analysis and gave conclusions that were consistent with at-the-margins analysis, while allowing for these interactions. By contrast, inside-the-table gave different conclusions about which intervention had the highest net benefits compared with other analyses. CONCLUSIONS:All analyses of partial factorial trials should explore interactions and assess whether results are sensitive to assumptions about interactions, either as a primary analysis or as a sensitivity analysis. For partial factorial trials closely mirroring routine clinical practice, at-the-margins analysis may provide a reasonable estimate of average costs and benefits for the whole trial population, even in the presence of interactions. However, such conclusions will be misleading if there are large interactions or if the proportion of patients allocated to different treatments differs markedly from what occurs in clinical practice. The Bayesian bootstrap provides an alternative to at-the-margins analysis for analysing clinical or economic endpoints from partial factorial trials, which allows for interactions while making use of the whole sample. The same techniques could be applied to analyses of clinical endpoints. TRIAL REGISTRATION:ISRCTN, ISRCTN45837371 . Registered on 25 April 2003.

Project description:OBJECTIVE:To explore the implementation of the International Committee of Medical Journal Editors (ICMJE) data-sharing policy which came into force on 1 July 2018 by ICMJE-member journals and by ICMJE-affiliated journals declaring they follow the ICMJE recommendations. DESIGN:A cross-sectional survey of data-sharing policies in 2018 on journal websites and in data-sharing statements in randomised controlled trials (RCTs). SETTING:ICMJE website; PubMed/Medline. ELIGIBILITY CRITERIA:ICMJE-member journals and 489 ICMJE-affiliated journals that published an RCT in 2018, had an accessible online website and were not considered as predatory journals according to Beall's list. One hundred RCTs for member journals and 100 RCTs for affiliated journals with a data-sharing policy, submitted after 1 July 2018. MAIN OUTCOME MEASURES:The primary outcome for the policies was the existence of a data-sharing policy (explicit data-sharing policy, no data-sharing policy, policy merely referring to ICMJE recommendations) as reported on the journal website, especially in the instructions for authors. For RCTs, our primary outcome was the intention to share individual participant data set out in the data-sharing statement. RESULTS:Eight (out of 14; 57%) member journals had an explicit data-sharing policy on their website (three were more stringent than the ICMJE requirements, one was less demanding and four were compliant), five (35%) additional journals stated that they followed the ICMJE requirements, and one (8%) had no policy online. In RCTs published in these journals, there were data-sharing statements in 98 out of 100, with expressed intention to share individual patient data reaching 77 out of 100 (77%; 95% CI 67% to 85%). One hundred and forty-five (out of 489) ICMJE-affiliated journals (30%; 26% to 34%) had an explicit data-sharing policy on their website (11 were more stringent than the ICMJE requirements, 85 were less demanding and 49 were compliant) and 276 (56%; 52% to 61%) merely referred to the ICMJE requirements. In RCTs published in affiliated journals with an explicit data-sharing policy, data-sharing statements were rare (25%), and expressed intentions to share data were found in 22% (15% to 32%). CONCLUSION:The implementation of ICMJE data-sharing requirements in online journal policies was suboptimal for ICMJE-member journals and poor for ICMJE-affiliated journals. The implementation of the policy was good in member journals and of concern for affiliated journals. We suggest the conduct of continuous audits of medical journal data-sharing policies in the future. REGISTRATION:The protocol was registered before the start of the research on the Open Science Framework (https://osf.io/n6whd/).

Project description:BACKGROUND:In individually randomised trials we might expect interventions delivered in groups or by care providers to result in clustering of outcomes for participants treated in the same group or by the same care provider. In partially nested randomised controlled trials (pnRCTs) this clustering only occurs in one trial arm, commonly the intervention arm. It is important to measure and account for between-cluster variability in trial design and analysis. We compare analysis approaches for pnRCTs with continuous outcomes, investigating the impact on statistical inference of cluster sizes, coding of the non-clustered arm, intracluster correlation coefficient (ICCs), and differential variance between intervention and control arm, and provide recommendations for analysis. METHODS:We performed a simulation study assessing the performance of six analysis approaches for a two-arm pnRCT with a continuous outcome. These include: linear regression model; fully clustered mixed-effects model with singleton clusters in control arm; fully clustered mixed-effects model with one large cluster in control arm; fully clustered mixed-effects model with pseudo clusters in control arm; partially nested homoscedastic mixed effects model, and partially nested heteroscedastic mixed effects model. We varied the cluster size, number of clusters, ICC, and individual variance between the two trial arms. RESULTS:All models provided unbiased intervention effect estimates. In the partially nested mixed-effects models, methods for classifying the non-clustered control arm had negligible impact. Failure to account for even small ICCs resulted in inflated Type I error rates and over-coverage of confidence intervals. Fully clustered mixed effects models provided poor control of the Type I error rates and biased ICC estimates. The heteroscedastic partially nested mixed-effects model maintained relatively good control of Type I error rates, unbiased ICC estimation, and did not noticeably reduce power even with homoscedastic individual variances across arms. CONCLUSIONS:In general, we recommend the use of a heteroscedastic partially nested mixed-effects model, which models the clustering in only one arm, for continuous outcomes similar to those generated under the scenarios of our simulations study. However, with few clusters (3-6), small cluster sizes (5-10), and small ICC (?0.05) this model underestimates Type I error rates and there is no optimal model.

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Economic evaluation of factorial randomised controlled trials: challenges, methods and recommendations.

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