Statistics in medicine
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Statistics in medicine · Apr 2013
Comparative StudyComparing ROC curves derived from regression models.
In constructing predictive models, investigators frequently assess the incremental value of a predictive marker by comparing the ROC curve generated from the predictive model including the new marker with the ROC curve from the model excluding the new marker. Many commentators have noticed empirically that a test of the two ROC areas often produces a non-significant result when a corresponding Wald test from the underlying regression model is significant. ⋯ In this article, we demonstrate that both the test statistic and its estimated variance are seriously biased when predictions from nested regression models are used as data inputs for the test, and we examine in detail the reasons for these problems. Although it is possible to create a test reference distribution by resampling that removes these biases, Wald or likelihood ratio tests remain the preferred approach for testing the incremental contribution of a new marker.
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Statistics in medicine · Apr 2013
Comparative StudyAdaptive clinical trial designs with pre-specified rules for modifying the sample size: understanding efficient types of adaptation.
Adaptive clinical trial design has been proposed as a promising new approach that may improve the drug discovery process. Proponents of adaptive sample size re-estimation promote its ability to avoid 'up-front' commitment of resources, better address the complicated decisions faced by data monitoring committees, and minimize accrual to studies having delayed ascertainment of outcomes. We investigate aspects of adaptation rules, such as timing of the adaptation analysis and magnitude of sample size adjustment, that lead to greater or lesser statistical efficiency. ⋯ Our results build on others' prior research by demonstrating in realistic settings that simple and easily implemented pre-specified adaptive designs provide only very small efficiency gains over group sequential designs with the same number of analyses. In addition, we describe optimal rules for modifying the sample size, providing efficient adaptation boundaries on a variety of scales for the interim test statistic for adaptation analyses occurring at several different stages of the trial. We thus provide insight into what are good and bad choices of adaptive sampling plans when the added flexibility of adaptive designs is desired.
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Statistics in medicine · Mar 2013
ReviewAnalysis of multicentre trials with continuous outcomes: when and how should we account for centre effects?
In multicentre trials, randomisation is often carried out using permuted blocks stratified by centre. It has previously been shown that stratification variables used in the randomisation process should be adjusted for in the analysis to obtain correct inference. For continuous outcomes, the two primary methods of accounting for centres are fixed-effects and random-effects models. ⋯ With small samples sizes, random-effects models maintained nominal coverage rates when a degree-of-freedom (DF) correction was used. We assessed the robustness of random-effects models when assumptions regarding the distribution of the centre effects were incorrect and found this had no impact on results. We conclude that random-effects models offer many advantages over fixed-effects models in certain situations and should be used more often in practice.
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The problem for assessing biosimilarity and drug interchangeability of follow-on biologics (biosimilar products) is studied. Unlike the generic products, the development of biosimilar products is much more complicated because of fundamental differences in functional structures and manufacturing processes. ⋯ In this article, we provide some scientific considerations for criteria, design, and analysis regarding the assessment of biosimilarity and drug interchangeability of biosimilar products. In addition, we discuss scientific and practical issues raised at the 2010 FDA public hearing and the 2011 FDA public meeting on biosimilar products.