Authors: Thomas, Marius
Title: Subgroup analyses and investigations of treatment effect heterogeneity in clinical dose-finding trials
Language (ISO): en
Abstract: Identifying subgroups, which respond differently to a treatment is an important part of drug development. Exploratory subgroup analyses, which have the aim to identify subgroups of patients with differential treatment effects are thus common in many randomized clinical trials. Statistically these analyses are known to be challenging the number of possible subgroups is often large, which leads to multiplicity issues. Often such subgroup analyses are also performed for early phase clinical trials, where an additional challenge is the small sample size. In recent years several statistical approaches to these problems have been proposed, employing for example tree-based recursive partitioning algorithms, which are well-suited for handling interactions, penalized regression methods, which can be used to prevent overfitting when explicitly modeling a large number of covariate effects or Bayesian approaches, which allow incorporating uncertainty and can be used to make optimal decisions with regard to subgroups. The available literature focuses however on two-arm clinical trials, where patients are randomized to the experimental treatment or a control (e.g. current standard of care or placebo). A particular focus of this cumulative thesis is the development of statistical methodology for identification of subgroups in dose-finding trials, in which patients are administered several doses of a new drug. Dose-finding trials play a key role in the drug development process, since they provide valuable information about the effect of the dose on efficacy and safety. For identifying subgroups in this setting we consider the treatment effect to be a function of the dose and then try to identify relevant covariate effects on this treatment effect curve. These identified covariates can then be used to define subgroups with higher treatment effects but also subgroups, which require a different dose of the treatment. We propose two different approaches for this purpose. Firstly, a tree-based recursive partitioning algorithm, which detects covariate effects on the parameters of dose-response models and builds a tree of subgroups with different dose-response curves. Secondly, a Bayesian hierarchical model, which makes use of shrinkage priors to prevent overfitting in the considered settings with low sample sizes and a large number of considered covariates. In addition to approaches for subgroup identification we also consider the problem of testing a prespecified subgroup in addition to the full population in dose-finding trials. In a dose-finding setting contrast tests are often used to test for a significant dose-response signal, while taking the underlying dose-response relationship into account. Optimal contrast tests can be derived, when the underlying dose-response model is known, however often there is uncertainty about this underlying model. Testing procedures, which allow for uncertainty with regard to the underlying model and perform multiple contrast tests are therefore popular approaches in such settings. As a part of this thesis we extend such approaches to settings with multiple populations, in particular the situation, in which a prespecified subgroup is considered in addition to the full population. A last part of this cumulative thesis focuses on treatment effect estimation in identified subgroups. Naive treatment effect estimates in subgroups will often suffer from selection bias, especially when the number of considered subgroups is large. Several approaches to obtain adjusted treatment effect estimates in such situations have been proposed, using resampling, model averaging or penalized regression. We compare these approaches in an extensive simulation study for a large range of scenarios, in which such analyses are performed.
Subject Headings: Selection bias
Resampling
Regression trees
Bayesian hierarchical models
Multiple testing
Dose-response models
Subject Headings (RSWK): Arzneimittelentwicklung
Stichprobe
Resampling
Bias
Regressionsmodell
Dosis-Wirkungs-Beziehung
URI: http://hdl.handle.net/2003/37948
http://dx.doi.org/10.17877/DE290R-19933
Issue Date: 2019
Appears in Collections:Lehrstuhl Mathematische Statistik und biometrische Anwendungen

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