Literature DB >> 30592133

Subgroup analysis and interpretation for phase 3 confirmatory trials: White paper of the EFSPI/PSI working group on subgroup analysis.

Aaron Dane1, Amy Spencer2, Gerd Rosenkranz3, Ilya Lipkovich4, Tom Parke5.   

Abstract

Subgroup by treatment interaction assessments are routinely performed when analysing clinical trials and are particularly important for phase 3 trials where the results may affect regulatory labelling. Interpretation of such interactions is particularly difficult, as on one hand the subgroup finding can be due to chance, but equally such analyses are known to have a low chance of detecting differential treatment effects across subgroup levels, so may overlook important differences in therapeutic efficacy. EMA have therefore issued draft guidance on the use of subgroup analyses in this setting. Although this guidance provided clear proposals on the importance of pre-specification of likely subgroup effects and how to use this when interpreting trial results, it is less clear which analysis methods would be reasonable, and how to interpret apparent subgroup effects in terms of whether further evaluation or action is necessary. A PSI/EFSPI Working Group has therefore been investigating a focused set of analysis approaches to assess treatment effect heterogeneity across subgroups in confirmatory clinical trials that take account of the number of subgroups explored and also investigating the ability of each method to detect such subgroup heterogeneity. This evaluation has shown that the plotting of standardised effects, bias-adjusted bootstrapping method and SIDES method all perform more favourably than traditional approaches such as investigating all subgroup-by-treatment interactions individually or applying a global test of interaction. Therefore, these approaches should be considered to aid interpretation and provide context for observed results from subgroup analyses conducted for phase 3 clinical trials.
© 2018 John Wiley & Sons, Ltd.

Entities:  

Keywords:  bias adjustment; late-phase clinical programmes; regulatory labelling; subgroup analysis

Mesh:

Year:  2018        PMID: 30592133     DOI: 10.1002/pst.1919

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  1 in total

Review 1.  Subgroup Analyses in Oncology Trials: Regulatory Considerations and Case Examples.

Authors:  Anup K Amatya; Mallorie H Fiero; Erik W Bloomquist; Arup K Sinha; Steven J Lemery; Harpreet Singh; Amna Ibrahim; Martha Donoghue; Lola A Fashoyin-Aje; R Angelo de Claro; Nicole J Gormley; Laleh Amiri-Kordestani; Rajeshwari Sridhara; Marc R Theoret; Paul G Kluetz; Richard Pazdur; Julia A Beaver; Shenghui Tang
Journal:  Clin Cancer Res       Date:  2021-06-11       Impact factor: 13.801

  1 in total

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