Statistics in medicine
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Statistics in medicine · Apr 2017
Quantifying the bias in the estimated treatment effect in randomized trials having interim analyses and a rule for early stopping for futility.
In this paper, we consider the potential bias in the estimated treatment effect obtained from clinical trials, the protocols of which include the possibility of interim analyses and an early termination of the study for reasons of futility. In particular, by considering the conditional power at an interim analysis, we derive analytic expressions for various parameters of interest: (i) the underestimation or overestimation of the treatment effect in studies that stop for futility; (ii) the impact of the interim analyses on the estimation of treatment effect in studies that are completed, i.e. that do not stop for futility; (iii) the overall estimation bias in the estimated treatment effect in a single study with such a stopping rule; and (iv) the probability of stopping at an interim analysis. We evaluate these general expressions numerically for typical trial scenarios. ⋯ Because the probability of stopping early is small for many practical situations, the overall bias is often small, but a more serious issue is the potential for substantial underestimation of the treatment effect in studies that actually stop for futility. We also consider these ideas using data from an illustrative trial that did stop for futility at an interim analysis. Copyright © 2017 John Wiley & Sons, Ltd.
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Statistics in medicine · Apr 2017
Concordance correlation coefficients estimated by generalized estimating equations and variance components for longitudinal repeated measurements.
The concordance correlation coefficient (CCC) is a commonly accepted measure of agreement between two observers for continuous responses. This paper proposes a generalized estimating equations (GEE) approach allowing dependency between repeated measurements over time to assess intra-agreement for each observer and inter- and total agreement among multiple observers simultaneously. Furthermore, the indices of intra-, inter-, and total agreement through variance components (VC) from an extended three-way linear mixed model (LMM) are also developed with consideration of the correlation structure of longitudinal repeated measurements. ⋯ An application of optometric conformity study is used for illustration. In conclusion, the GEE approach allowing flexibility in model assumptions and correlation structures of repeated measurements gives satisfactory results with small mean square errors and nominal 95% coverage rates for large data sets, and when the assumption of the relationship between variances and covariances for the extended three-way LMM holds, the VC approach performs outstandingly well for all sample sizes. Copyright © 2017 John Wiley & Sons, Ltd.
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Statistics in medicine · Apr 2017
Tutorial on statistical considerations on subgroup analysis in confirmatory clinical trials.
Clinical trials target patients who are expected to benefit from a new treatment under investigation. However, the magnitude of the treatment benefit, if it exists, often depends on the patient baseline characteristics. It is therefore important to investigate the consistency of the treatment effect across subgroups to ensure a proper interpretation of positive study findings in the overall population. ⋯ This tutorial considers issues related to subgroup analyses and their impact on the interpretation of findings of completed trials that met their main objectives. In addition, we provide guidance on the design and analysis of clinical trials that account for the expected heterogeneity of treatment effects across subgroups by establishing treatment benefit in a pre-defined targeted subgroup and/or the overall population. Copyright © 2016 John Wiley & Sons, Ltd.