scholarly journals A new conditional performance score for the evaluation of adaptive group sequential designs with sample size recalculation

2020 ◽  
Vol 39 (15) ◽  
pp. 2067-2100
Author(s):  
Carolin Herrmann ◽  
Maximilian Pilz ◽  
Meinhard Kieser ◽  
Geraldine Rauch
Keyword(s):  
Sample Size ◽  
Performance Score ◽  
Sequential Designs ◽  
Group Sequential ◽  
Conditional Performance ◽  
Group Sequential Designs

scholarly journals Smoothing Corrections for Improving Sample Size Recalculation Rules in Adaptive Group Sequential Study Designs

2021 ◽  
Author(s):  
Carolin Herrmann ◽  
Geraldine Rauch
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Evaluation Criteria ◽  
High Variability ◽  
Conditional Power ◽  
Performance Score ◽  
Group Sequential ◽  
Conditional Performance ◽  
Sequential Study ◽  
Study Designs

Abstract Background An adequate sample size calculation is essential for designing a successful clinical trial. One way to tackle planning difficulties regarding parameter assumptions required for sample size calculation is to adapt the sample size during the ongoing trial.This can be attained by adaptive group sequential study designs. At a predefined timepoint, the interim effect is tested for significance. Based on the interim test result, the trial is either stopped or continued with the possibility of a sample size recalculation. Objectives Sample size recalculation rules have different limitations in application like a high variability of the recalculated sample size. Hence, the goal is to provide a tool to counteract this performance limitation. Methods Sample size recalculation rules can be interpreted as functions of the observed interim effect. Often, a “jump” from the first stage's sample size to the maximal sample size at a rather arbitrarily chosen interim effect size is implemented and the curve decreases monotonically afterwards. This jump is one reason for a high variability of the sample size. In this work, we investigate how the shape of the recalculation function can be improved by implementing a smoother increase of the sample size. The design options are evaluated by means of Monte Carlo simulations. Evaluation criteria are univariate performance measures such as the conditional power and sample size as well as a conditional performance score which combines these components. Results We demonstrate that smoothing corrections can reduce variability in conditional power and sample size as well as they increase the performance with respect to a recently published conditional performance score for medium and large standardized effect sizes. Conclusion Based on the simulation study, we present a tool that is easily implemented to improve sample size recalculation rules. The approach can be combined with existing sample size recalculation rules described in the literature.

scholarly journals Group Sequential Designs: A Tutorial

2021 ◽  
Author(s):  
Daniel Lakens ◽  
Friedrich Pahlke ◽  
Gernot Wassmer
Keyword(s):  
Sample Size ◽  
Type I Error ◽  
A Priori ◽  
R Package ◽  
Error Rates ◽  
Type I ◽  
Sequential Designs ◽  
Group Sequential ◽  
Shiny App ◽  
Group Sequential Designs

This tutorial illustrates how to design, analyze, and report group sequential designs. In these designs, groups of observations are collected and repeatedly analyzed, while controlling error rates. Compared to a fixed sample size design, where data is analyzed only once, group sequential designs offer the possibility to stop the study at interim looks at the data either for efficacy or futility. Hence, they provide greater flexibility and are more efficient in the sense that due to early stopping the expected sample size is smaller as compared to the sample size in the design with no interim look. In this tutorial we illustrate how to use the R package 'rpact' and the associated Shiny app to design studies that control the Type I error rate when repeatedly analyzing data, even when neither the number of looks at the data, nor the exact timing of looks at the data, is specified. Specifically for *t*-tests, we illustrate how to perform an a-priori power analysis for group sequential designs, and explain how to stop the data collection for futility by rejecting the presence of an effect of interest based on a beta-spending function. Finally, we discuss how to report adjusted effect size estimates and confidence intervals. The recent availability of accessible software such as 'rpact' makes it possible for psychologists to benefit from the efficiency gains provided by group sequential designs.

Exact inference for adaptive group sequential designs

2013 ◽  
Vol 32 (23) ◽  
pp. 3991-4005 ◽  
Author(s):  
Ping Gao ◽  
Lingyun Liu ◽  
Cyrus Mehta
Keyword(s):  
Sequential Designs ◽  
Group Sequential ◽  
Exact Inference ◽  
Group Sequential Designs

Group sequential designs with robust semiparametric recurrent event models

2018 ◽  
Vol 28 (8) ◽  
pp. 2385-2403 ◽  
Author(s):  
Tobias Mütze ◽  
Ekkehard Glimm ◽  
Heinz Schmidli ◽  
Tim Friede
Keyword(s):  
Error Rate ◽  
Joint Distribution ◽  
Recurrent Events ◽  
Type I Error ◽  
Type I ◽  
Sequential Designs ◽  
Group Sequential ◽  
Type I Error Rate ◽  
Sequential Procedures ◽  
Group Sequential Designs

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. In this manuscript, we propose two types of group sequential procedures for a robust semiparametric analysis of recurrent events. The first group sequential procedure is based on the asymptotic covariance of the sequence of Wald statistics and it guarantees asymptotic control of the type I error rate. The second procedure is based on the canonical joint distribution and does not guarantee asymptotic type I error rate control but is easy to implement and corresponds to the well-known standard approach for group sequential designs. Moreover, we describe how to determine the maximum information when planning a clinical trial with a group sequential design and a robust semiparametric analysis of recurrent events. We contrast the operating characteristics of the proposed group sequential procedures in a simulation study motivated by the ongoing phase 3 PARAGON-HF trial (ClinicalTrials.gov identifier: NCT01920711) in more than 4600 patients with chronic heart failure and a preserved ejection fraction. We found that both group sequential procedures have similar operating characteristics and that for some practically relevant scenarios, the group sequential procedure based on the canonical joint distribution has advantages with respect to the control of the type I error rate. The proposed method for calculating the maximum information results in appropriately powered trials for both procedures.

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