Statistical Methods in Medical Research

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ISSN / EISSN : 0962-2802 / 1477-0334
Current Publisher: SAGE Publications (10.1177)
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Total articles ≅ 2,369
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K Edgar, D Jackson, K Rhodes, T Duffy, C-F Burman,
Statistical Methods in Medical Research; doi:10.1177/09622802211017574

Abstract:
Background The number of Phase III trials that include a biomarker in design and analysis has increased due to interest in personalised medicine. For genetic mutations and other predictive biomarkers, the trial sample comprises two subgroups, one of which, say [Formula: see text] is known or suspected to achieve a larger treatment effect than the other [Formula: see text]. Despite treatment effect heterogeneity, trials often draw patients from both subgroups, since the lower responding [Formula: see text] subgroup may also gain benefit from the intervention. In this case, regulators/commissioners must decide what constitutes sufficient evidence to approve the drug in the [Formula: see text] population. Methods and Results Assuming trial analysis can be completed using generalised linear models, we define and evaluate three frequentist decision rules for approval. For rule one, the significance of the average treatment effect in [Formula: see text] should exceed a pre-defined minimum value, say [Formula: see text]. For rule two, the data from the low-responding group [Formula: see text] should increase statistical significance. For rule three, the subgroup-treatment interaction should be non-significant, using type I error chosen to ensure that estimated difference between the two subgroup effects is acceptable. Rules are evaluated based on conditional power, given that there is an overall significant treatment effect. We show how different rules perform according to the distribution of patients across the two subgroups and when analyses include additional (stratification) covariates in the analysis, thereby conferring correlation between subgroup effects. Conclusions When additional conditions are required for approval of a new treatment in a lower response subgroup, easily applied rules based on minimum effect sizes and relaxed interaction tests are available. Choice of rule is influenced by the proportion of patients sampled from the two subgroups but less so by the correlation between subgroup effects.
Jan P Burgard, , Ralf Münnich, Domingo Morales
Statistical Methods in Medical Research; doi:10.1177/09622802211017583

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Simón Ramírez, Adolfo J Quiroz,
Statistical Methods in Medical Research; doi:10.1177/09622802211009258

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Statistical Methods in Medical Research; doi:10.1177/09622802211017299

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, Fabio Paredes, Claudio Vargas, Catterina Ferreccio
Statistical Methods in Medical Research; doi:10.1177/09622802211013830

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Hormatollah Pourreza, , Einolah Deiri
Statistical Methods in Medical Research; doi:10.1177/09622802211009262

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Liya Fu, You-Gan Wang
Statistical Methods in Medical Research; doi:10.1177/09622802211012012

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, , Jagbir Gill, Clifford Miles, Troy Plumb
Statistical Methods in Medical Research; doi:10.1177/09622802211009265

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Leyla Azarang, Roch Giorgi, the CENSUR working survival group
Statistical Methods in Medical Research; doi:10.1177/09622802211003608

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Katy C Molina, , Vera D Tomazella, Eder A Milani
Statistical Methods in Medical Research; doi:10.1177/09622802211011187

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