1,721,168 research outputs found
Correction : A conditional error function approach for subgroup selection in adaptive clinical trials
No full textIt has recently come to our attention that some of the results presented in Table II in ’A conditional error function approach for subgroup selection in adaptive clinical trials’ (Statistics in Medicine 2012; 31:4309–4320) are not correct. An R software coding issue resulted in numerical errors in the reported results for the conditional error function approach (CEF) and for the combination test approach by Spiessens and Debois (CT-SD). We have corrected these errors in the following table. We also, for reasons of consistency with the CEF approach, now present type I error rates for the CT-SD approach based purely on rejection of the intersection hypothesis inline image rather than as previously on rejection of both intersection and one or the other of the elementary hypotheses inline image and inline image. It is now clear that type I error rates are controlled at the nominal 2.5% level for both approaches, and our previous assertion that the CEF methodology was uniformly, although only marginally, more powerful than the CT-SD methodology is no longer supported by the simulation results for the selected scenarios. Correction of coding errors produced such small changes to data presented in Figures 1–3 as to be indistinguishable from normal simulation error; updated data underlying these plots are available on request from the authors. We apologize for any inconvenience this error has caused
A conditional error function approach for subgroup selection in adaptive clinical trials
No full textGrowing interest in personalised medicine and targeted therapies is leading to an increase in the importance of subgroup analyses. If it is planned to view treatment comparisons in both a predefined subgroup and the full population as co-primary analyses, it is important that the statistical analysis controls the familywise type I error rate. Spiessens and Debois (Cont. Clin. Trials, 2010, 31, 647–656) recently proposed an approach specific for this setting, which incorporates an assumption about the correlation based on the known sizes of the different groups, and showed that this is more powerful than generic multiple comparisons procedures such as the Bonferroni correction. If recruitment is slow relative to the length of time taken to observe the outcome, it may be efficient to conduct an interim analysis. In this paper, we propose a new method for an adaptive clinical trial with co-primary analyses in a predefined subgroup and the full population based on the conditional error function principle. The methodology is generic in that we assume test statistics can be taken to be normally distributed rather than making any specific distributional assumptions about individual patient data. In a simulation study, we demonstrate that the new method is more powerful than previously suggested analysis strategies. Furthermore, we show how the method can be extended to situations when the selection is not based on the final but on an early outcome. We use a case study in a targeted therapy in oncology to illustrate the use of the proposed methodology with non-normal outcomes. Copyright © 2012 John Wiley & Sons, Ltd
Statistical methods for clinical trials interrupted by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic: A review
No full textCancellation or delay of non-essential medical interventions, limitation of face-to-face assessments or outpatient attendance due to lockdown restrictions, illness or fear of hospital or healthcare centre visits, and halting of research to allow diversion of healthcare resources to focus on the pandemic led to the interruption of many clinical trials during the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic. Appropriate analysis approaches are now required for these interrupted trials. In trials with long follow-up and longitudinal outcomes, data may be available on early outcomes for many patients for whom final, primary outcome data were not observed. A natural question is then how these early data can best be used in the trial analysis. Although recommendations are available from regulators, funders, and methodologists, there is a lack of a review of recent work addressing this problem. This article reports a review of recent methods that can be used in the setting of the analysis of interrupted clinical trials with longitudinal outcomes with monotone missingness. A search for methodological papers published during the period 2020–2023 identified 43 relevant publications. We categorised these articles under the four broad themes of missing value imputation, modelling and covariate adjustment, simulation and estimands. Although motivated by the interruption due to SARS-CoV-2 and the resulting disease, the papers reviewed and methods discussed are also relevant to clinical trials interrupted for other reasons, with follow-up discontinued.Medical Research Council https://doi.org/10.13039/50110000026
Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection : methods, simulation model and their implementation in R
Full text linkAdaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Here, we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate interim analyses informed by either the primary outcome or an early outcome. The R package asd, previously developed to simulate designs with treatment selection, was extended to include subgroup selection (so‐called adaptive enrichment designs). Here, we describe the functionality of the R package asd and use it to present some worked‐up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples both illustrate various features of the R package and provide insights into the operating characteristics of adaptive seamless studies
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Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data
Full text linkSeamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow-up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods. © 2014 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd
Adaptive designs for confirmatory clinical trials with subgroup selection
No full textGrowing interest in stratified medicine is leading to increasing importance of subgroup analyses in confirmatory clinical trials. Conventionally, confirmatory clinical trials either focus on a subgroup identified in advance or assess subgroup effects once the trial is completed. The focus of this article is methodology for adaptive clinical trials that both identify whether a treatment is particularly effective in a predefined subgroup, potentially enabling alteration of recruitment, and assess the effectiveness in the subgroup and/or whole population. Methods for such adaptive trials are described and compared, and the logistical and regulatory issues associated with such approaches are discussed
Software tools for implementing simulation studies in adaptive seamless designs : introducing R package ASD
Full text linkAdaptive designs for clinical trials have the potential to
improve the efficiency of clinical research by, for instance,
seamlessly combining different stages of a clinical development programme
Refinement of the Clinical Scenario Evaluation Framework for Assessment of Competing Development Strategies With an Application to Multiple Sclerosis
No full textWhen competing strategies for development programs, clinical trial designs, or data analysis methods exist, the alternatives need to be evaluated in a systematic way to facilitate informed decision making. Here we describe a refinement of the recently proposed clinical scenario evaluation framework for the assessment of competing strategies. The refinement is achieved by subdividing key elements previously proposed into new categories, distinguishing between quantities that can be estimated from preexisting data and those that cannot and between aspects under the control of the decision maker from those that are determined by external constraints. The refined framework is illustrated by an application to a design project for an adaptive seamless design for a clinical trial in progressive multiple sclerosis
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A comparison of methods for treatment selection in seamless phase II/III clinical trials incorporating information on short-term endpoints
Full text linkIn an adaptive seamless phase II/III clinical trial interim
analysis data are used for treatment selection, enabling resources to be focussed on comparison of more effective treatment(s) with a control. In this paper we compare two methods recently proposed to enable use of short-term endpoint data for decision-making at the interim analysis. The comparison focusses on the power and the probability of correctly identifying the most promising treatment. We show that the choice of method depends on how well short-term data predict the best treatment, which may be measured by the correlation between treatment effects on short-term and long-term endpoints
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