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Multiplicity-adjusted semiparametric benefiting subgroup identification in clinical trials.

ABSTRACT: Background A recent focus in the health sciences has been the development of personalized medicine, which includes determining the population for which a given treatment is effective. Due to limited data, identifying the true benefiting population is a challenging task. To tackle this difficulty, the credible subgroups approach provides a pair of bounding subgroups for the true benefiting subgroup, constructed so that one is contained by the benefiting subgroup while the other contains the benefiting subgroup with high probability. However, the method has so far only been developed for parametric linear models. Methods In this article, we develop the details required to follow the credible subgroups approach in more realistic settings by considering nonlinear and semiparametric regression models, supported for regulatory science by conditional power simulations. We also present an improved multiple testing approach using a step-down procedure. We evaluate our approach via simulations and apply it to data from four trials of Alzheimer's disease treatments carried out by AbbVie. Results Semiparametric modeling yields credible subgroups that are more robust to violations of linear treatment effect assumptions, and careful choice of the population of interest as well as the step-down multiple testing procedure result in a higher rate of detection of benefiting types of patients. The approach allows us to identify types of patients that benefit from treatment in the Alzheimer's disease trials. Conclusion Attempts to identify benefiting subgroups of patients in clinical trials are often met with skepticism due to a lack of multiplicity control and unrealistically restrictive assumptions. Our proposed approach merges two techniques, credible subgroups, and semiparametric regression, which avoids these problems and makes benefiting subgroup identification practical and reliable.

SUBMITTER: Schnell PM

PROVIDER: S-EPMC8796253 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Importance:Multiple analyses in a clinical trial can increase the probability of inaccurately concluding that there is a statistically significant treatment effect. However, to date, it is unknown how many randomized clinical trials (RCTs) perform adjustments for multiple comparisons, the lack of which could lead to erroneous findings. Objectives:To assess the prevalence of multiplicity and whether appropriate multiplicity adjustments were performed among cardiovascular RCTs published in 6 medical journals with a high impact factor. Design, Setting, and Participants:In this cross-sectional study, cardiovascular RCTs were selected from all over the world, characterized as North America, Western Europe, multiregional, and rest of the world. Data were collected from past issues of 3 cardiovascular journals (Circulation, European Heart Journal, and Journal of the American College of Cardiology) and 3 general medicine journals (JAMA, The Lancet, and The New England Journal of Medicine) with high impact factors published between August 1, 2015, and July 31, 2018. Supplements and trial protocols of each of the included RCTs were also searched for multiplicity. Data were analyzed December 20 to 27, 2018. Exposures:Data from the selected RCTs were extracted and verified independently by 2 researchers using a structured data instrument. In case of disagreement, a third reviewer helped to achieve consensus. An RCT was considered to have multiple treatment groups if it had more than 2 arms; multiple outcomes were defined as having more than 1 primary outcome, and multiple analyses were defined as analysis of the same outcome variable in multiple ways. Multiplicity was examined only for the analysis of the primary end point. Main Outcomes and Measures:Outcomes of interest were percentages of primary analyses that performed multiplicity adjustment of primary end points. Results:Of 511 cardiovascular RCTs included in this analysis, 300 (58.7%) had some form of multiplicity; of these 300, only 85 (28.3%) adjusted for multiplicity. Intervention type and funding source had no statistically significant association with the reporting of multiplicity risk adjustment. Trials that assessed mortality vs nonmortality outcomes were more likely to contain a multiplicity risk in their primary analysis (66.3% [177 of 267] vs 50.4% [123 of 244]; P?<?.001), and larger trials vs smaller trials were less likely to make any adjustments for multiplicity (35.6% [52 of 146] vs 21.4% [33 of 154]; P?=?.001). Conclusions and Relevance:Findings from this study suggest that cardiovascular RCTs published in medical journals with high impact factors demonstrate infrequent adjustments to correct for multiple comparisons in the primary end point. These parameters may be improved by more standardized reporting.

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Multiplicity-adjusted semiparametric benefiting subgroup identification in clinical trials.

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