The impact of selection bias in randomized multi-arm parallel group clinical trials

Intervention Tournaments: An Overview of Concept, Design, and Implementation

A large portion of research in the social sciences is devoted to using interventions to combat societal and social problems, such as prejudice, discrimination, and intergroup conflict. However, these interventions are often developed using the theories and/or intuitions of the individuals who developed them and evaluated in isolation without comparing their efficacy with other interventions. Here, we make the case for an experimental design that addresses such issues: an intervention tournament-that is, a study that compares several different interventions against a single control and uses the same standardized outcome measures during assessment and participants drawn from the same population. We begin by highlighting the utility of intervention tournaments as an approach that complements other, more commonly used approaches to addressing societal issues. We then describe various approaches to intervention tournaments, which include crowdsourced, curated, and in-house-developed intervention tournaments, and their unique characteristics. Finally, we discuss practical recommendations and key design insights for conducting such research, given the existing literature. These include considerations of intervention-tournament deployment, characteristics of included interventions, statistical analysis and reporting, study design, longitudinal and underlying psychological mechanism assessment, and theoretical ramifications.

Emerging biomaterial-based strategies for personalized therapeutic in situ cancer vaccines

Landmark successes in oncoimmunology have led to development of therapeutics boosting the host immune system to eradicate local and distant tumors with impactful tumor reduction in a subset of patients. However, current immunotherapy modalities often demonstrate limited success when involving immunologically cold tumors and solid tumors. Here, we describe the role of various biomaterials to formulate cancer vaccines as a form of cancer immunotherapy, seeking to utilize the host immune system to activate and expand tumor-specific T cells. Biomaterial-based cancer vaccines enhance the cancer-immunity cycle by harnessing cellular recruitment and activation against tumor-specific antigens. In this review, we discuss biomaterial-based vaccine strategies to induce lymphocytic responses necessary to mediate anti-tumor immunity. We focus on strategies that selectively attract dendritic cells via immunostimulatory gradients, activate them against presented tumor-specific antigens, and induce effective cross-presentation to T cells in secondary lymphoid organs, thereby generating immunity. We posit that personalized cancer vaccines are promising targets to generate long-term systemic immunity against patient- and tumor-specific antigens to ensure long-term cancer remission.

Randomization-based inference in the presence of selection bias

For the analysis of clinical trials, the study participants are usually assumed to be representative sample of a target population. This assumption is rarely fulfilled in clinical trials, and particularly not if the sample size is small. In addition, covariate imbalances may affect the trial. Randomization tests provide a nonparametric analysis method of the treatment effect that does not rely on population-based assumptions. We propose a nonparametric statistical model that yields a formal basis for randomization tests. We adapt the model for the presence of covariate imbalance in the form of selection bias and investigate the effects of bias on the rejection probability of the randomization test using Monte Carlo simulations. Finally, we show that ancillary statistics can be used to control for the influence of bias. We show that covariate imbalance leads to an inflation of the type I error probability. The proposed nonparametric model allows for the use of ancillary statistics that yield an unbiased adjusted randomization test.

Echocardiographic assessment of functional mitral regurgitation: opening Pandora's box?

Two recent trials of transcatheter mitral-valve repair in patients with functional mitral regurgitation (FMR) presented opposing results for the MitraClip® compared to medical therapy alone. The conflicting results gave rise to intensive discussions about assessment of mitral valve regurgitation (MR). A recent editorial viewpoint provided a potential explanation presenting a new pathophysiologic concept. However, the echocardiographic characterization of both trials' patients is inconsistent and the discussed concepts appear to suffer from plausibility weaknesses. It is well conceivable that limitations in the echocardiographic assessment of the trial patients introduced a bias regarding the selection of patients with severe (or less severe) MR that may be a more plausible explanation for the differences in outcome. We here illustrate our viewpoint regarding the two MitraClip trials and also illustrate the difficulties in assessing functional MR properly. It may indeed be "opening Pandora's box", but we will also make an attempt to provide a solution.

Randomization in survival studies: An evaluation method that takes into account selection and chronological bias

The random allocation of patients to treatments is a crucial step in the design and conduct of a randomized controlled trial. For this purpose, a variety of randomization procedures is available. In the case of imperfect blinding, the extent to which a randomization procedure forces balanced group sizes throughout the allocation process affects the predictability of allocations. As a result, some randomization procedures perform superior with respect to selection bias, whereas others are less susceptible to chronological bias. The choice of a suitable randomization procedure therefore depends on the expected risk for selection and chronological bias within the particular study in question. To enable a sound comparison of different randomization procedures, we introduce a model for the combined effect of selection and chronological bias in randomized studies with a survival outcome. We present an evaluation method to quantify the influence of bias on the test decision of the log-rank test in a randomized parallel group trial with a survival outcome. The effect of selection and chronological bias and the dependence on the study setting are illustrated in a sensitivity analysis. We conclude with a case study to showcase the application of our model for comparing different randomization procedures in consideration of the expected type I error probability.

Implementing Optimal Designs for Dose-Response Studies Through Adaptive Randomization for a Small Population Group

In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model and the amount of censored data. In practice, such designs can be implemented adaptively, by performing dose assignments according to updated knowledge of the dose-response curve at interim analysis. It is also essential that treatment allocation involves randomization-to mitigate various experimental biases and enable valid statistical inference at the end of the trial. In this work, we perform a comparison of several adaptive randomization procedures that can be used for implementing D-optimal designs for dose-response studies with time-to-event outcomes with small to moderate sample sizes. We consider single-stage, two-stage, and multi-stage adaptive designs. We also explore robustness of the designs to experimental (chronological and selection) biases. Simulation studies provide evidence that both the choice of an allocation design and a randomization procedure to implement the target allocation impact the quality of dose-response estimation, especially for small samples. For best performance, a multi-stage adaptive design with small cohort sizes should be implemented using a randomization procedure that closely attains the targeted D-optimal design at each stage. The results of the current work should help clinical investigators select an appropriate randomization procedure for their dose-response study.

Lessons learned from IDeAl - 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials

BACKGROUND

IDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers.

METHOD

The description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project's more than 60 publications to date in peer reviewed journals. In addition, we applied text mining to evaluate the publications and the IDeAl work packages' output in relation to the design and analysis terms derived from in the IRDiRC task force report on small population clinical trials.

RESULTS

The results are summarized, describing the developments from an applied viewpoint. The main result presented here are 33 practical recommendations drawn from the work, giving researchers a comprehensive guidance to the improved methodology. In particular, the findings will help design and analyse efficient clinical trials in rare diseases with limited number of patients available. We developed a network representation relating the hot topics developed by the IRDiRC task force on small population clinical trials to IDeAl's work as well as relating important methodologies by IDeAl's definition necessary to consider in design and analysis of small-population clinical trials. These network representation establish a new perspective on design and analysis of small-population clinical trials.

CONCLUSION

IDeAl has provided a huge number of options to refine the statistical methodology for small-population clinical trials from various perspectives. A total of 33 recommendations developed and related to the work packages help the researcher to design small population clinical trial. The route to improvements is displayed in IDeAl-network representing important statistical methodological skills necessary to design and analysis of small-population clinical trials. The methods are ready for use.