scholarly journals Group sequential designs applied in psychological research

Methodology ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 75-91
Author(s):  
Klemens Weigl ◽  
Ivo Ponocny
Keyword(s):  
Pharmaceutical Research ◽  
Psychological Research ◽  
Statistical Testing ◽  
Early Stopping ◽  
Sequential Designs ◽  
Real World Data ◽  
Group Sequential ◽  
Data Set ◽  
Group Sequential Designs ◽  
Core Idea

Psychological research is confronted with ever-increasing demands to save resources such as time and money while assuring high ethical standards. In medical and pharmaceutical research, group sequential designs have fundamentally changed traditional statistical testing approaches featuring only one analysis at the end of a single-stage study. They enable early stopping at an interim stage, after a group of observations, for efficacy or futility in case of an overwhelmingly large or small effect, respectively. Otherwise, the trial is continued to the next stage. On average over many studies time and money are saved and more ethical trials are facilitated by diminishing the risk of patients' exposure to inferior treatments. We provide an easy-to-use tutorial for psychological research replete with easily understandable figures highlighting the core idea of different group sequential designs, a workflow chart, an empirical real-world data set, and the annotated R code. Finally, we demonstrate the application of early stopping for efficacy.

scholarly journals Choosing Interim Sample Sizes in Group Sequential Designs

2021 ◽  
Author(s):  
Sergey Tarima ◽  
Nancy Flournoy
Keyword(s):  
Interim Analysis ◽  
Specific Information ◽  
Distributed Data ◽  
Early Stopping ◽  
Sample Sizes ◽  
Sequential Designs ◽  
Group Sequential ◽  
Interim Analyses ◽  
Group Sequential Designs ◽  
Power Properties

This manuscript investigates sample sizes for interim analyses in group sequential designs. Traditional group sequential designs (GSD) rely on “information fraction” arguments to define the interim sample sizes. Then, interim maximum likelihood estimators (MLEs) are used to decide whether to stop early or continue the data collection until the next interim analysis. The possibility of early stopping changes the distribution of interim and final MLEs: possible interim decisions on trial stopping excludes some sample space elements. At each interim analysis the distribution of an interim MLE is a mixture of truncated and untruncated distributions. The distributional form of an MLE becomes more and more complicated with each additional interim analysis. Test statistics that are asymptotically normal without a possibly of early stopping, become mixtures of truncated normal distributions under local alternatives. Stage-specific information ratios are equivalent to sample size ratios for independent and identically distributed data. This equivalence is used to justify interim sample sizes in GSDs. Because stage-specific information ratios derived from normally distributed data differ from those derived from non-normally distributed data, the former equivalence is invalid when there is a possibility of early stopping. Tarima and Flournoy [3] have proposed a new GSD where interim sample sizes are determined by a pre-defined sequence of ordered alternative hypotheses, and the calculation of information fractions is not needed. This innovation allows researchers to prescribe interim analyses based on desired power properties. This work compares interim power properties of a classical one-sided three stage Pocock design with a one-sided three stage design driven by three ordered alternatives.

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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