Closed MCP-Mod for Pairwise Comparisons of Several Doses With a Control.

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.

Optimal allocation strategies in platform trials with continuous endpoints.

Platform trials are randomized clinical trials that allow simultaneous comparison of multiple interventions, usually against a common control. Arms to test experimental interventions may enter and leave the platform over time. This implies that the number of experimental intervention arms in the trial may change as the trial progresses.

A novel approach to visualize clinical benefit of therapies for chronic graft versus host disease (cGvHD): the probability of being in response (PBR) applied to the REACH3 study.

Overall response rate (ORR) is commonly used as key endpoint to assess treatment efficacy of chronic graft versus host disease (cGvHD), either as ORR at week 24 or as best overall response rate (BOR) at any time point up to week 24 or beyond. Both endpoints as well as duration of response (DOR) were previously reported for the REACH3 study, a phase 3 open-label, randomized study comparing ruxolitinib (RUX) versus best available therapy (BAT). The comparison between RUX and BAT was performed on ORR and BOR using all randomized patients, while DOR was derived for the subgroup of responders only.

Familywise error rate control for block response-adaptive randomization.

Response-adaptive randomization allows the probabilities of allocating patients to treatments in a clinical trial to change based on the previously observed response data, in order to achieve different experimental goals. One concern over the use of such designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. To address this, Robertson and Wason (Biometrics, 2019) proposed methodology that guarantees familywise error rate control for a large class of response-adaptive designs by re-weighting the usual -test statistic.

Designing an exploratory phase 2b platform trial in NASH with correlated, co-primary binary endpoints.

Non-alcoholic steatohepatitis (NASH) is the progressive form of nonalcoholic fatty liver disease (NAFLD) and a disease with high unmet medical need. Platform trials provide great benefits for sponsors and trial participants in terms of accelerating drug development programs. In this article, we describe some of the activities of the EU-PEARL consortium (EU Patient-cEntric clinicAl tRial pLatforms) regarding the use of platform trials in NASH, in particular the proposed trial design, decision rules and simulation results.

Adjusting for treatment selection in phase II/III clinical trials with time to event data.

Phase II/III clinical trials are efficient two-stage designs that test multiple experimental treatments. In stage 1, patients are allocated to the control and all experimental treatments, with the data collected from them used to select experimental treatments to continue to stage 2. Patients recruited in stage 2 are allocated to the selected treatments and the control.

Geometric approaches to assessing the numerical feasibility for conducting matching-adjusted indirect comparisons.

We discuss how to handle matching-adjusted indirect comparison (MAIC) from a data analyst's perspective. We introduce several multivariate data analysis methods to assess the appropriateness of MAIC for a given set of baseline characteristics. These methods focus on comparing the baseline variables used in the matching of a study that provides the summary statistics or aggregated data (AD) and a study that provides individual patient level data (IPD).

Decision rules for identifying combination therapies in open-entry, randomized controlled platform trials.

Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies. Many statistical questions related to designing platform trials-such as the impact of decision rules, sharing of information across cohorts, and allocation ratios on operating characteristics and error rates-remain unanswered. In many platform trials, the definition of error rates is not straightforward as classical error rate concepts are not applicable.

Use of multivariate distance measures for high-dimensional data in tests for difference, superiority, equivalence and non-inferiority.

Tests based on pairwise distance measures for multivariate sample vectors are common in ecological studies but are usually restricted to two-sided tests for differences. In this paper, we investigate extensions to tests for superiority, equivalence and non-inferiority.

Connecting Instrumental Variable methods for causal inference to the Estimand Framework.

Causal inference methods are gaining increasing prominence in pharmaceutical drug development in light of the recently published addendum on estimands and sensitivity analysis in clinical trials to the E9 guideline of the International Council for Harmonisation. The E9 addendum emphasises the need to account for post-randomization or 'intercurrent' events that can potentially influence the interpretation of a treatment effect estimate at a trial's conclusion. Instrumental Variables (IV) methods have been used extensively in economics, epidemiology, and academic clinical studies for 'causal inference,' but less so in the pharmaceutical industry setting until now.

The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review.

Purpose: Recent years have seen a change in the way that clinical trials are being conducted. There has been a rise of designs more flexible than traditional adaptive and group sequential trials which allow the investigation of multiple substudies with possibly different objectives, interventions, and subgroups conducted within an overall trial structure, summarized by the term master protocol. This review aims to identify existing master protocol studies and summarize their characteristics.

Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection.

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two-stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment.

Adjusting for selection bias in assessing treatment effect estimates from multiple subgroups.

This paper discusses a number of methods for adjusting treatment effect estimates in clinical trials where differential effects in several subpopulations are suspected. In such situations, the estimates from the most extreme subpopulation are often overinterpreted. The paper focusses on the construction of simultaneous confidence intervals intended to provide a more realistic assessment regarding the uncertainty around these extreme results.

Power analysis for multivariable Cox regression models.

In power analysis for multivariable Cox regression models, variance of the estimated log-hazard ratio for the treatment effect is usually approximated by inverting the expected null information matrix. Because, in many typical power analysis settings, assumed true values of the hazard ratios are not necessarily close to unity, the accuracy of this approximation is not theoretically guaranteed. To address this problem, the null variance expression in power calculations can be replaced with one of the alternative expressions derived under the assumed true value of the hazard ratio for the treatment effect.

Estimating relative efficacy in acute postoperative pain: network meta-analysis is consistent with indirect comparison to placebo alone.

Network meta-analysis uses direct comparisons of interventions within randomized controlled trials and indirect comparisons across them. Network meta-analysis uses more data than a series of direct comparisons with placebo, and theoretically should produce more reliable results. We used a Cochrane overview review of acute postoperative pain trials and other systematic reviews to provide data to test this hypothesis.

Group sequential designs with robust semiparametric recurrent event models.

Robust semiparametric models for recurrent events have received increasing attention in the analysis of clinical trials in a variety of diseases including chronic heart failure. In comparison to parametric recurrent event models, robust semiparametric models are more flexible in that neither the baseline event rate nor the process inducing between-patient heterogeneity needs to be specified in terms of a specific parametric statistical model. However, implementing group sequential designs in the robust semiparametric model is complicated by the fact that the sequence of Wald statistics does not follow asymptotically the canonical joint distribution.

Group sequential designs for negative binomial outcomes.

Count data and recurrent events in clinical trials, such as the number of lesions in magnetic resonance imaging in multiple sclerosis, the number of relapses in multiple sclerosis, the number of hospitalizations in heart failure, and the number of exacerbations in asthma or in chronic obstructive pulmonary disease (COPD) are often modeled by negative binomial distributions. In this manuscript, we study planning and analyzing clinical trials with group sequential designs for negative binomial outcomes. We propose a group sequential testing procedure for negative binomial outcomes based on Wald statistics using maximum likelihood estimators.

Conditionally unbiased estimation in the normal setting with unknown variances.

To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz (, 8(3):273-278, 1989), who proposed an UMVCUE for the best performing candidate in the normal setting with a common variance.

A unified framework for weighted parametric multiple test procedures.

We describe a general framework for weighted parametric multiple test procedures based on the closure principle. We utilize general weighting strategies that can reflect complex study objectives and include many procedures in the literature as special cases. The proposed weighted parametric tests bridge the gap between rejection rules using either adjusted significance levels or adjusted p-values.

An approach to confirmatory testing of subpopulations in clinical trials.

In oncology studies with immunotherapies, populations of "super-responders" (patients in whom the treatment works particularly well) are often suspected to be related to biomarkers. In this paper, we explore various ways of confirmatory statistical hypothesis testing for joint inference on the subpopulation of putative "super-responders" and the full study population. A model-based testing framework is proposed, which allows to define, up-front, the strength of evidence required from both full and subpopulations in terms of clinical efficacy.

Connections between permutation and t-tests: relevance to adaptive methods.

A permutation test assigns a p-value by conditioning on the data and treating the different possible treatment assignments as random. The fact that the conditional type I error rate given the data is controlled at level α ensures validity of the test even if certain adaptations are made. We show the connection between permutation and t-tests, and use this connection to explain why certain adaptations are valid in a t-test setting as well.

Maximum type 1 error rate inflation in multiarmed clinical trials with adaptive interim sample size modifications.

Sample size modifications in the interim analyses of an adaptive design can inflate the type 1 error rate, if test statistics and critical boundaries are used in the final analysis as if no modification had been made. While this is already true for designs with an overall change of the sample size in a balanced treatment-control comparison, the inflation can be much larger if in addition a modification of allocation ratios is allowed as well. In this paper, we investigate adaptive designs with several treatment arms compared to a single common control group.

Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials.

The two-stage drop-the-loser design provides a framework for selecting the most promising of K experimental treatments in stage one, in order to test it against a control in a confirmatory analysis at stage two. The multistage drop-the-losers design is both a natural extension of the original two-stage design, and a special case of the more general framework of Stallard & Friede () (Stat. Med.