Despite extensive research, the use of response-adaptive randomization (RAR) in clinical trials has remained controversial. Korn and Freidlin's 2011 article reignited this debate back then, prompting numerous responses, including one by Zhu, Rosenberger, and Hu that remained unpublished until now. This article features the original response by Zhu, Rosenberger, and Hu, providing a valuable opportunity to revisit the original arguments, examine subsequent developments in RAR methodology, and offer a more complete historical perspective on this enduring debate. The piece also includes a concluding section by one of the special issue's co-editor that explores the nuances and complexities of RAR implementation in the context of contemporary clinical trial design.

Cluster analysis has been widely used in biomedical studies for disaggregating heterogeneous diseases and identifying disease subtypes that may inform clinical decisions. In the era of advanced data science and engineering, cluster analysis faces new challenges due to high dimensionality, multimodality and computational complexity. In the present study, we propose a fast integrative clustering approach based on variational Bayesian inference, called iClusterVB. The iClusterVB enables the integration of multiple datasets into the clustering process while performing feature selection in high-dimensional settings for mixed data types, including continuous, categorical, and count data. Simulation studies are performed to compare the performance of iClusterVB with six competing methods and highlight its advantages. Additionally, iClusterVB is applied to three real-life studies to demonstrate its utility in identifying important features and cancer subtypes that are associated with distinct survival probabilities. A user-friendly R package iClusterVB and a tutorial are developed to implement the proposed approach.

In randomized clinical trials with a time-to-event outcome, the intervention effect could be quantified by a difference in restricted mean survival time (ΔRMST) between the intervention and control groups, defined as the expected survival duration gain due to the intervention over a fixed follow-up period. In cluster randomized trials (CRTs), social units are randomized to intervention or control groups; the correlation between survival times of the individuals within the same cluster must be taken into account in the statistical analysis. In a previous work, we proposed the use of pseudo-values regression, based on generalized estimating equations (GEEs), for estimating ΔRMST in CRTs. We showed that this method correctly estimated the ΔRMST and controlled the type I error rate in CRTs with at least 50 clusters. Here, we propose methods for CRTs with a small number of clusters (<50). We evaluated the performance of four bias-corrections of the GEE sandwich variance estimator of the intervention effect. We also considered the use of a Student t distribution as an alternative to the normal distribution of the GEE Wald test statistic for testing the intervention effect and constructing the confidence interval. With a simulation study, assuming proportional or non-proportional hazards, we showed that the Student t distribution outperformed the normal distribution in terms of type I error rate, and the Fay and Graubard bias-corrected variance led to an appropriate type I error rate whatever the number of clusters. Therefore, we recommend the use of the Fay and Graubard variance estimator combined with a Student t distribution for the pseudo-values regression to correctly estimate the variance of the intervention effect. Finally, we provide an illustrative analysis of the DEMETER trial evaluating the use of a specific endotracheal tube for subglottic secretion drainage to prevent ventilator-associated pneumonia, by comparing each of the methods considered.

Effect sizes typically vary among studies of the same intervention. In a random effects meta-analysis, this source of variation is taken into account, at least to some extent. However, when we have only one study, the heterogeneity remains hidden and unaccounted for. Treating the study-level effect as if it is the population-level effect leads to underestimation of the uncertainty. We propose an empirical Bayesian approach to address this problem. We start by estimating the distribution of the population-level effects and heterogeneity among 1635 meta-analyses from the Cochrane Database of Systematic Reviews. Using both synthetic data and cross-validation, we assess the consequences of using these estimated distributions as prior information for the analysis of single trials. We find that our Bayesian "meta-analyses of single studies" perform much better than naively assuming non-varying effects. The prior on the heterogeneity results in better quantification of the uncertainty. The prior on the treatment effect substantially reduces the mean squared error both for estimating the study-level and population-level effects. For the latter, this reduction is equivalent to doubling the sample size.

This study describes and compares the performance of several semi-parametric statistical modeling approaches to dynamically classify subjects into two groups, based on an irregularly and sparsely sampled curve. The motivating example of this study is the diagnosis of a complication following cardiac surgery, based on repeated measures of a single cardiac biomarker where early detection enables prompt intervention by clinicians. We first simulate data to compare the dynamic predictive performance over time for growth charts, conditional growth charts, a varying-coefficient model, a generalized functional linear model and longitudinal discriminant analysis. Our results demonstrate that functional regression approaches that implicitly incorporate historic information through random effects, provide superior discriminative ability compared to approaches that do not take historic information into account or explicitly model historic information through autoregressive terms. Semi-parametric modeling approaches show a benefit in terms of dynamic discriminative ability compared to the clinical practice of using a fixed threshold on the raw measured value. Under high degrees of sparsity the functional regression approaches are less advantageous compared to varying-coefficient models or quantile regression. The class imbalance of the outcome affects the historic and non-historic approaches in equal measure, with lower event rates reducing performance. Finally, the functional regression and varying-coefficient model were applied to a real-world clinical dataset to demonstrate their performance and application.

Individualized treatment rules leverage patient-level information to tailor treatments for individuals. Estimating these rules, with the goal of optimizing expected patient outcomes, typically relies on individual-level data to identify the variability in treatment effects across patient subgroups defined by different covariate combinations. To increase the statistical power for detecting treatment–covariate interactions and the generalizability of the findings, data from multisite studies are often used. However, sharing sensitive patient-level health data is sometimes restricted. Additionally, due to funding or time constraints, only a subset of available treatments can be included at each site, but an individualized treatment rule considering all treatments is desired. In this work, we adopt a two-stage Bayesian network meta-analysis approach to estimate individualized treatment rules for multiple treatments using multisite data without disclosing individual-level data beyond the sites. Simulation results demonstrate that our approach can provide consistent estimates of the parameters that fully characterize the optimal individualized treatment rule. We illustrate the method’s application through an analysis of data from the Sequenced Treatment Alternatives to Relieve Depression study, the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care study, and the Research Evaluating the Value of Augmenting Medication with Psychotherapy study.

The identification of biomarkers for disease onset in longitudinal studies necessitates precise estimation of the association between longitudinal markers and survival outcomes. Currently, methods for estimating these associations in the context of left-truncated and clustered survival outcomes are lacking. In this study, we propose a novel model tailored to this scenario and develop several estimation methods: last observation carried forward, regression calibration, and a two-stage likelihood approach for joint modeling of longitudinal and survival processes. Simulation results indicate that the last observation carried forward method performs well only with a dense grid and no marker measurement error. For less dense grids and low measurement error, regression calibration approaches are preferred. Joint modeling approaches outperform calibration methods in the presence of measurement error, although they may suffer from numerical instability. In cases of numerical instability, calibration methods might be a good alternative. We applied these methodologies to the TwinsUK data to estimate the effect of bone mineral density (BMD) as a longitudinal marker on fracture incidence in 766 elderly females, 138 of whom experienced a fracture. The survival model included a shared gamma-distributed frailty to account for correlation between the times to fracture of twin pairs. Estimates obtained using calibration and joint modeling approaches indicated a larger BMD effect compared to the last observation carried forward method, likely due to the irregular BMD measurement process and minimal measurement error. Overall, our methods offer valuable tools for modeling the effect of a longitudinal marker on survival outcomes in complex designs.

Mechanistic interaction concerns how exposures affect the outcome. When investigating mechanisms, synergism is the most mentioned type in the fields of genetic study and pharmacology. Synergism is defined under the framework of sufficient component cause model, which is difficult to be quantified directly. Sufficient cause interaction (SCI) is the only alternative metric to imply the existence of synergism. VanderWeele and Robins provided empirical tests for SCIs. However, this test only assesses the lower bound of SCIs rather than estimate SCIs directly due to the lack of the degree of freedom, which causes low power. To address this issue, in this study, we propose a novel method to estimate the probability of individual with SCI by introducing a new factor named quasi-instrumental variable, which is necessary for the background condition of SCI. We also develop a corresponding hypothesis test and show that it is more powerful than the existing empirical test. We demonstrate this method by applying it to estimate the synergistic effects between intestinal bacteria on the formation of Parkinson's disease.

The use of external data in clinical trials offers numerous advantages, such as reducing enrollment, increasing study power, and shortening trial duration. In Bayesian inference, information in external data can be transferred into an informative prior for future borrowing (i.e. prior synthesis). However, multisource external data often exhibits heterogeneity, which can cause information distortion during the prior synthesizing. Clustering helps identifying the heterogeneity, enhancing the congruence between synthesized prior and external data. Obtaining optimal clustering is challenging due to the trade-off between congruence with external data and robustness to future data. We introduce two overlapping indices: the overlapping clustering index and the overlapping evidence index . Using these indices alongside a K-means algorithm, the optimal clustering result can be identified by balancing this trade-off and applied to construct a prior synthesis framework to effectively borrow information from multisource external data. By incorporating the (robust) meta-analytic predictive (MAP) prior within this framework, we develop (robust) Bayesian clustering MAP priors. Simulation studies and real-data analysis demonstrate their advantages over commonly used priors in the presence of heterogeneity. Since the Bayesian clustering priors are constructed without needing the data from prospective study, they can be applied to both study design and data analysis in clinical trials.

Competing risks models can involve more than one time scale. A relevant example is the study of mortality after a cancer diagnosis, where time since diagnosis but also age may jointly determine the hazards of death due to different causes. Multiple time scales have rarely been explored in the context of competing events. Here, we propose a model in which the cause-specific hazards vary smoothly over two times scales. It is estimated by two-dimensional -splines, exploiting the equivalence between hazard smoothing and Poisson regression. The data are arranged on a grid so that we can make use of generalised linear array models for efficient computations. The R-package TwoTimeScales implements the model. As a motivating example we analyse mortality after diagnosis of breast cancer and we distinguish between death due to breast cancer and all other causes of death. The time scales are age and time since diagnosis. We use data from the Surveillance, Epidemiology and End Results (SEER) program. In the SEER data, age at diagnosis is provided with a last open-ended category, leading to coarsely grouped data. We use the two-dimensional penalised composite link model to ungroup the data before applying the competing risks model with two time scales.