Statistics in medicine
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Statistics in medicine · Jul 1996
Comparative StudyA method for determining the size of internal pilot studies.
The assessment of sample size in clinical trials comparing population means requires a variance estimate of the main efficacy variable. When this variance estimate has a low precision, it may be appropriate to use the data from the first patients entered in the trial ('internal pilot study') to estimate the sample size. We suggest a method for determining the size of internal pilot studies, which aims at ensuring that this size is as large as possible, but not larger than 'the optimal size' of the planned study. Advantages and limitations of the method are discussed.
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Statistics in medicine · Jul 1996
Maximum likelihood estimation of the kappa coefficient from bivariate logistic regression.
We propose a maximum likelihood estimator (MLE) of the kappa coefficient from a 2 x 2 table when the binary ratings depend on patient and/or clinician effects. We achieve this by expressing the logit of the probability of positive rating as a linear function of the subject-specific and the rater-specific covariates. We investigate the bias and variance of the MLE in small and moderate size samples through Monte Carlo simulation and we provide the sample size calculation to detect departure from the null hypothesis H0: kappa = kappa 0 in the direction of H1: kappa > kappa 0.
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Statistics in medicine · Jun 1996
ReviewMeta-analysis and meta-analytic monitoring of clinical trials.
Randomized trials are effective and usually unbiased for showing the average results in a selected outcome variable for treatment A versus treatment B, and meta-analyses produce an average of these averages. The results of both the trials and meta-analyses are often pragmatically unsatisfactory, however, because they do not reflect cogent distinctions desired by practising clinicians in the heterogeneous subgroups formed by diverse components in the patients' baseline states, in proficiency of therapy, and in additional outcome phenomena. If the inadequacies of previous trials have led to performance of a suitable new trial, it should not be stopped by the numbers emerging from meta-analyses of prior non-pertinent results.
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Statistics in medicine · Jun 1996
ReviewThe role of external evidence in data monitoring of a clinical trial.
Data monitoring of interim results from a randomized clinical trial should take into consideration evidence from other trials. This article presents both scientific and practical issues regarding the pros and cons of formally incorporating such external evidence into the decision making process for the current trial. Guidelines on how to use other trials' data are presented, along with cautiously sceptical comments on the impracticality of using formal meta-analyses in data monitoring. The arguments are illustrated by recent examples from specific trials, and the article concludes with some general recommendations.
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Results from external studies often play an important role in many aspects of a clinical trial. Their incorporation into the decision making process of a trial, however, is rarely conducted in a formal manner. This conference will address what formal role, if any, meta-analytic summaries of external results should have in the design and monitoring of an ongoing trial. This introductory presentation describes in detail the example from obstetric research which motivated the conference topic, and, in a Bayesian framework, summarizes the general implications of formally incorporating meta-analytic results into the design and analysis of a new trial.