Björn Bornkamp

Subgroup selection in clinical trials is essential for identifying patient groups that react differently to a treatment, thereby enabling personalized medicine. In particular, subgroup selection can identify patient groups that respond particularly well to a treatment or that encounter adverse events more often. However, this is a post‐selection inference problem, which may pose challenges for traditional techniques used for subgroup analysis, such as increased Type I error rates and potential biases from data‐driven subgroup identification. In this paper, we discuss two recently developed methods for subgroup selection in regression problems: one based on generalized linear modeling and another on isotonic regression. We demonstrate how these methods can be used for data‐driven subgroup identification in the analysis of clinical trials, focusing on two distinct tasks: identifying patient groups that are safe from manifesting adverse events and identifying patient groups with high treatment effect, while controlling for Type I error in both cases. A thorough simulation study is conducted to evaluate the strengths and weaknesses of each method, providing detailed insight into the sensitivity of the Type I error rate control to modeling assumptions.

This paper reviews and compares methods to assess treatment effect heterogeneity in the context of parametric regression models. These methods include the standard likelihood ratio tests, bootstrap likelihood ratio tests, and Goeman's global test, motivated by testing whether the random effect variance is zero. We place particular emphasis on tests based on the score‐residual of the treatment effect and explore different variants of tests in this class. All approaches are compared in a simulation study, and the approach based on residual scores is illustrated in a clinical trial with a time‐to‐event outcome comparing treatment vs. placebo. Our findings demonstrate that score‐residual‐based methods provide practical, flexible, and reliable tools for exploring treatment effect heterogeneity and treatment effect modifiers, and can provide useful guidance for decision‐making around treatment effect heterogeneity.

Assessing treatment effect heterogeneity (TEH) in clinical trials is crucial, as it provides insights into the variability of treatment responses among patients, influencing key decisions related to drug development. Furthermore, it can lead to personalized medicine by tailoring treatments to individual patient characteristics. This paper introduces novel methodologies for assessing treatment effects using the individualized treatment effect as a basis. To estimate this effect, we use a doubly robust (DR) learner to infer a pseudo‐outcome that reflects the causal contrast. This pseudo‐outcome is then used to perform three objectives: (1) a global test for heterogeneity, (2) ranking covariates based on their influence on effect modification, and (3) providing estimates of the individualized treatment effect. We compare the DR‐learner with various alternatives and competing methods in a simulation study, and also use it to assess heterogeneity in a pooled analysis of five Phase III trials in psoriatic arthritis (PsA). By integrating these methods with the recently proposed Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors (WATCH) workflow, we provide a robust framework for analyzing TEH, offering insights that enable more informed decision‐making in this challenging area.

In clinical trials, patients may discontinue treatments prematurely, breaking the initial randomization. In our motivating study, a randomized controlled trial in oncology, patients assigned the investigational treatment may discontinue it due to adverse events. The ICH E9(R1) Addendum provides guidelines for handling such “intercurrent events.” The right strategy to adopt depends on the questions of interest. We propose adopting a principal stratum strategy and decomposing the overall intention‐to‐treat effect into principal causal effects for groups of patients defined by their potential discontinuation behaviour. We first show how to implement a principal stratum strategy to assess causal effects on a survival outcome in the presence of continuous‐time treatment discontinuation, its advantages, and the conclusions that can be drawn. Our strategy allows us to properly handle the time‐to‐event intermediate variable, which is not defined for patients who would not discontinue, and to account for the fact that the discontinuation time and the primary endpoint are subject to censoring. We employ a flexible model‐based Bayesian approach to tackle these complexities, providing easily interpretable results. We apply this Bayesian principal stratification framework to analyze synthetic data of the motivating oncology trial. Supported by a simulation study, we shed light on the role of covariates in this framework. Beyond making structural and parametric assumptions more credible, they lead to more precise inference. Also, they can be used to characterize patients' discontinuation behavior, which could help inform clinical practice and future protocols.

This article discusses futility analyses for the MCP‐Mod methodology. Formulas are derived for calculating predictive and conditional power for MCP‐Mod, which also cover the case when longitudinal models are used allowing to utilize incomplete data from patients at interim. A simulation study is conducted to evaluate the repeated sampling properties of the proposed decision rules and to assess the benefit of using a longitudinal versus a completer only model for decision making at interim. The results suggest that the proposed methods perform adequately and a longitudinal analysis outperforms a completer only analysis, particularly when the recruitment speed is higher and the correlation over time is larger. The proposed methodology is illustrated using real data from a dose‐finding study for severe uncontrolled asthma.

Dose selection is critical in pharmaceutical drug development, as it directly impacts therapeutic efficacy and patient's safety of a drug. The Generalized Multiple Comparison Procedures and Modeling approach is commonly used in Phase II trials for testing and estimation of dose‐response relationships. However, its effectiveness in small sample sizes, particularly with binary endpoints, is hindered by issues like complete separation in logistic regression, leading to non existence of estimates. Motivated by an actual clinical trial using the MCP‐Mod approach, this paper introduces penalized maximum likelihood estimation (MLE) and randomization‐based inference techniques to address these challenges. Randomization‐based inference allows for exact finite sample inference, while population‐based inference for MCP‐Mod typically relies on asymptotic approximations. Simulation studies demonstrate that randomization‐based tests can enhance statistical power in small to medium‐sized samples while maintaining control over type‐I error rates, even in the presence of time trends. Our results show that residual‐based randomization tests using penalized MLEs not only improve computational efficiency but also outperform standard randomization‐based methods, making them an adequate choice for dose‐finding analyses within the MCP‐Mod framework. Additionally, we apply these methods to pharmacometric settings, demonstrating their effectiveness in such scenarios. The results in this paper underscore the potential of randomization‐based inference for the analysis of dose‐finding trials, particularly in small sample contexts.

The MCP‐Mod approach by Bretz et al. is commonly applied for dose–response testing and estimation in clinical trials. The MCP part of MCP‐Mod was originally developed to detect a dose–response signal using a multiple contrast test, but it is not appropriate to make a specific claim that the drug has a positive effect at an individual dose. In this paper, we extend the MCP‐Mod approach to obtain confirmatory p‐values for detecting a dose–response signal as well as for the pairwise comparisons of the individual doses against placebo. We apply the closed test principle from Marcus et al. to the optimal contrast tests based on a candidate set of plausible dose–response shapes available at the planning stage of a clinical trial. We show that the contrast coefficients have to be optimized under suitable constraints to guarantee strong Type 1 error rate control at a pre‐specified significance level. Motivated by a recent clinical trial, we evaluate the operating characteristics of the proposed methods in a comprehensive simulation study.

This article proposes a Workflow for Assessing Treatment effeCt Heterogeneity (WATCH) in clinical drug development targeted at clinical trial sponsors. WATCH is designed to address the challenges of investigating treatment effect heterogeneity (TEH) in randomized clinical trials, where sample size and multiplicity limit the reliability of findings. The proposed workflow includes four steps: analysis planning, initial data analysis and analysis dataset creation, TEH exploration, and multidisciplinary assessment. The workflow offers a general overview of how treatment effects vary by baseline covariates in the observed data and guides the interpretation of the observed findings based on external evidence and the best scientific understanding. The workflow is exploratory and not inferential/confirmatory in nature but should be preplanned before database lock and analysis start. It is focused on providing a general overview rather than a single specific finding or subgroup with a differential effect.

The ICH E9 (R1) guidance and the related estimand framework propose to clearly define and separate the clinical question of interest formulated as estimand from the estimation method. With that it becomes important to assess the validity of the estimation method and the assumptions that must be made. When going beyond the intention to treat analyses that can rely on randomization, causal inference is usually used to discuss the validity of estimation methods for the estimand of interest. In pharmacometrics, mixed‐effects models are routinely used to analyze longitudinal clinical trial data; however, they are rarely discussed as a method for causal inference. Here, we evaluate nonlinear mixed‐effects modeling and simulation (NLME M&S) in the context of causal inference as a standardization method for longitudinal data in the presence of confounders. Standardization is a well‐known method in causal inference to correct for confounding by analyzing and combining results from subgroups of patients. We show that nonlinear mixed‐effects modeling is a particular implementation of standardization that conditions on individual parameters described by the random effects of the mixed‐effects model. As an example, we use a simulated clinical trial with within‐subject dose titration. Being interested in the outcome of the hypothetical situation that patients adhere to the planned treatment schedule, we put assumptions in a causal diagram. From the causal diagram, conditional independence assumptions are derived either by conditioning on the individual parameters or on earlier outcomes. With both conditional independencies unbiased estimates can be obtained.