The optimal designs (ODs) for parallel-arm longitudinal cluster randomized trials, multiple-period cluster randomized crossover (CRXO) trials, and stepped wedge cluster randomized trials (SW-CRTs), including closed-cohort and repeat cross-sectional designs, have been studied separately under a cost-efficiency framework based on generalized estimating equations (GEEs). However, whether a global OD exists across longitudinal designs and randomization schedules remains unknown. Therefore, this research addresses a critical gap by comparing OD feature across complete longitudinal cluster randomized trial designs with two treatment conditions and continuous outcomes. We define the OD as the design with either the lowest cost to obtain a desired level of power or the largest power given a fixed budget. For each of these ODs, we obtain the optimal number of clusters and the optimal cluster-period size (number of participants per cluster per period). To ensure equitable comparisons, we consider the GEE treatment effect estimator with the same block exchangeable correlation structure and develop OD algorithms with the lowest cost for each of six study designs. To obtain OD with the largest power, we summarize the previous and propose new OD algorithms and formulae. We suggest using the number of treatment sequences , where T is the number of time-periods, in both the optimal closed-cohort and repeated cross-sectional SW-CRTs to have the lowest cost. This is consistent with our previous findings for ODs with the largest power in SW-CRTs. Comparing all six ODs, we conclude that optimal closed-cohort CRXO trials are global ODs, yielding both the lowest cost and largest power.

Background: There is emerging evidence of the increasing uptake of response-adaptive randomization (RAR) in clinical trials. However, a systematic review of RAR trials, their context of use, characteristics, and stakeholder acceptance has been lacking. Methods: We performed a systematic review of clinical trials that utilized elements of RAR, identified via the Cortellis Regulatory Intelligence database following a pre-specified selection process. We report a summary of relevant characteristics of the identified trials. Results: Out of 170 records, 39 RAR trials were identified (22 completed, 17 ongoing as of October 2024). The majority were Phase 2-focused studies (phases 1/2, 2, 2b, and 2/3), academically sponsored, and concentrated in oncology, neurology, and infectious diseases. Small molecules and biologics were the most common investigational products. Among the 22 completed trials, seven reported positive outcomes. Notably, two of these trials provided pivotal data that informed the further development and subsequent regulatory approval of the investigational compounds. Conclusion: Over the past two decades, RAR has been increasingly utilized in complex adaptive trials across diverse therapeutic areas and clinical research phases. This systematic review provides a critical "baseline" for tracing the dynamics of RAR applications and should help the clinical research community recognize RAR as a valuable methodology for optimizing future trial designs.

Saved time is used in Alzheimer's disease (AD) trials as an easy interpretation of the treatment benefit to communicate with patients, family members, and caregivers. The projection approach is frequently applied to estimate saved time and its confidence interval (CI) by using the placebo or treatment disease progression curves. The estimated standard error of saved time by using these existing methods does not account for the correlation between outcomes. In addition, there was no closed-form CI for researchers to use in practice. To fill this critical gap, we derive the closed-form CI for saved time estimated from the placebo or treatment disease progression curves. We compare them with regard to coverage probability and interval width under various disease progression patterns that are commonly observed in AD symptomatic therapy and disease-modifying therapy trials. Data from the phase 3 donanemab trials are used to illustrate the application of the new CI methods.

Cluster randomized trials are widely used in healthcare research for the evaluation of intervention strategies. Beyond estimating the average treatment effect, it is often of interest to assess whether the treatment effect varies across subgroups. While conventional methods based on tests of interaction terms between treatment and covariates can be used to detect treatment effect heterogeneity in cluster randomized trials, they typically rely on parametric assumptions that may not hold in practice. Adapting existing permutation tests from individually randomized trials, however, requires conceptual clarification and modification due to the multiple possible interpretations of treatment effect heterogeneity in the cluster randomized trial context. In this work, we develop variations of permutation tests and clarify key causal definitions in order to assess treatment effect heterogeneity in cluster randomized trials. Our procedure enables investigators to simultaneously test for effect modification across a large number of covariates, while maintaining nominal type I error rates and reasonable power in simulation studies. In the Pain Program for Active Coping and Training (PPACT) study, the proposed methods are able to detect treatment effect heterogeneity that was not identified by conventional methods assessing treatment–covariate interactions.

In this article, we propose a new frailty model based on a mixture of inverse Gaussian distributions for multivariate lifetimes. This approach provides an advantage over previous models, as the weights are directly determined through parameterization of the mixture, removing the need for arbitrary guesswork in the weighting process. Moreover, the closed-form Laplace transform of the model facilitates the quantification of Kendall's tau measure of dependence. The frailty model's parametric and flexible parametric variants are examined. For parameter estimation, the expectation-maximization technique is employed, taking advantage of the hierarchical representation of the frailty distribution, providing a simpler and more stable method than directly maximizing the observed log-likelihood function. The performance of the estimators is assessed numerically using Monte Carlo simulations. We apply our methodology to two medical datasets on cancer. The results indicate the benefits of the proposed model over existing frailty models in the literature. The implementation of the procedure is added to the R package extrafrail.

Cluster randomized trials commonly employ multiple endpoints. When a single summary of treatment effects across endpoints is of primary interest, global methods represent a common analysis strategy. However, specification of the required joint distribution is non-trivial, particularly when the endpoints have different scales. We develop rank-based interval estimators for a global treatment effect referred to here as the "global win probability, or the mean of multiple Wilcoxon Mann-Whitney probabilities, and interpreted as the probability that a treatment individual responds better than a control individual on average. Using endpoint-specific ranks among the combined sample and within each arm, each individual-level observation is converted to a "win fraction" which quantifies the proportion of wins experienced over every observation in the comparison arm. An individual's multiple observations are then replaced with a single "global win fraction" by averaging win fractions across endpoints. A linear mixed model is applied directly to the global win fractions to obtain point, variance, and interval estimates adjusted for clustering. Simulation demonstrates our approach performs well concerning confidence interval coverage and type I error, and methods are easily implemented using standard software. A case study using public data is provided with corresponding R and SAS code.

Multiple efficacy endpoints are investigated in clinical trials, and selecting the appropriate primary endpoints is key to the study's success. The global test is an analysis approach that can handle multiple endpoints without multiplicity adjustment. This test, which aggregates the statistics from multiple primary endpoints into a single statistic using weights for the statistical comparison, has been gaining increasing attention. A key consideration in the global test is determination of the weights. In this study, we propose a novel global rank test in which the weights for each endpoint are estimated based on the current study data to maximize the test statistic, and the permutation test is applied to control the type I error rate. Simulation studies conducted to compare the proposed test with other global tests show that the proposed test can control the type I error rate at the nominal level, regardless of the number of primary endpoints and correlations between endpoints. Additionally, the proposed test offers higher statistical powers when the efficacy is considerably different between endpoints or when endpoints are moderately correlated, such as when the correlation coefficient is greater than or equal to 0.5.

Varying coefficient models are commonly used to capture intricate interaction effects among covariates in regression models, allowing for the modification of one covariate's effect by another. Although these models offer increased flexibility, they also introduce greater estimation and computational complexity as a trade-off. This complexity is particularly evident in genomic studies, where the covariates are often high-dimensional, rendering conventional estimation methods inapplicable. In this paper, we study a penalized estimation method for the varying coefficient additive hazards model. We adopt the group lasso penalty along with the kernel smoothing technique to estimate the varying coefficients. In contrast to existing kernel methods, which only use a "local" neighborhood of subjects to estimate the varying coefficient function at any given point, the proposed method takes a "global" approach that incorporates all subjects and is more efficient. Through extensive simulation studies, we demonstrate that the proposed method produces interpretable results with satisfactory predictive performance. We provide an application to a major cancer genomic study.

Simultaneously performing variable selection and inference in high-dimensional regression models is an open challenge in statistics and machine learning. The increasing availability of vast amounts of variables requires the adoption of specific statistical procedures to accurately select the most important predictors in a high-dimensional space, while controlling the false discovery rate (FDR) associated with the variable selection procedure. In this paper, we propose the joint adoption of the Mirror Statistic approach to FDR control, coupled with outcome randomisation to maximise the statistical power of the variable selection procedure, measured through the true positive rate. Through extensive simulations, we show how our proposed strategy allows us to combine the benefits of the two techniques. The Mirror Statistic is a flexible method to control FDR, which only requires mild model assumptions, but requires two sets of independent regression coefficient estimates, usually obtained after splitting the original dataset. Outcome randomisation is an alternative to data splitting that allows to generate two independent outcomes, which can then be used to estimate the coefficients that go into the construction of the Mirror Statistic. The combination of these two approaches provides increased testing power in a number of scenarios, such as highly correlated covariates and high percentages of active variables. Moreover, it is scalable to very high-dimensional problems, since the algorithm has a low memory footprint and only requires a single run on the full dataset, as opposed to iterative alternatives such as multiple data splitting.

Motivated by challenges in the analysis of biomedical data and observational studies, we develop statistical boosting for the general class of bivariate distributional copula regression with arbitrary marginal distributions, which is suited for binary, count, continuous or mixed outcomes. To arrive at a flexible model for the entire conditional distribution, not only the marginal distribution parameters but also the copula parameters are related to covariates through additive predictors. We suggest estimation by means of an adapted component-wise gradient boosting algorithm. A key benefit of boosting as opposed to classical likelihood or Bayesian estimation is the implicit data-driven variable selection mechanism as well as shrinkage. To the best of our knowledge, our implementation is the only one that combines a wide range of covariate effects, marginal distributions, copula functions, and implicit data-driven variable selection. We showcase the versatility of our approach to data from genetic epidemiology, healthcare utilization and childhood undernutrition. Our developments are implemented in the R package gamboostLSS, fostering transparent and reproducible research.