Statistics in medicine
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The number needed to treat (NNT) is a popular measure to describe the absolute effect of a new treatment compared with a standard treatment or placebo in clinical trials with binary outcome. For use of NNT measures in epidemiology to compare exposed and unexposed subjects, the terms 'number needed to be exposed' (NNE) and 'exposure impact number' (EIN) have been proposed. Additionally, in the framework of logistic regression a method was derived to perform point and interval estimation of NNT measures with adjustment for confounding by using the adjusted odds ratio (OR approach). ⋯ NNE is the average number of unexposed persons needed to be exposed to observe one extra case; EIN is the average number of exposed persons among one case can be attributed to the exposure. By means of simulations it is shown that the ARD approach is better than the OR approach in terms of bias and coverage probability, especially if the confounder distribution is wide. The proposed method is illustrated by application to data of a cohort study investigating the effect of smoking on coronary heart disease.
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Statistics in medicine · Dec 2007
Comparative StudyA competing risks analysis of bloodstream infection after stem-cell transplantation using subdistribution hazards and cause-specific hazards.
After peripheral blood stem-cell transplantation, patients treated for severe haematologic diseases enter a critical phase (neutropenia). Analysis of bloodstream infection during neutropenia has to account for competing risks. ⋯ Proportional subdistribution hazards modelling of the subdistribution of the CIF is establishing itself as an interpretation-friendly alternative. We apply both methods and discuss their relative merits.
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Statistics in medicine · Dec 2007
Frequentist evaluation of group sequential clinical trial designs.
Group sequential stopping rules are often used as guidelines in the monitoring of clinical trials in order to address the ethical and efficiency issues inherent in human testing of a new treatment or preventive agent for disease. Such stopping rules have been proposed based on a variety of different criteria, both scientific (e.g. estimates of treatment effect) and statistical (e.g. frequentist type I error, Bayesian posterior probabilities, stochastic curtailment). ⋯ Thus, the basis used to initially define a stopping rule is relatively unimportant so long as the operating characteristics of the stopping rule are fully investigated. In this paper we describe how the frequentist operating characteristics of a particular stopping rule might be evaluated to ensure that the selected clinical trial design satisfies the constraints imposed by the many different disciplines represented by the clinical trial collaborators.
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Statistics in medicine · Nov 2007
Implementing a decision-theoretic design in clinical trials: why and how?
This paper addresses two main questions: first, why should Bayesian and other innovative, data-dependent design models be put into practice and, secondly, given the past dearth of actual applications, how might one example of such a design be implemented in a genuine example trial? Clinical trials amalgamate theory, practice and ethics, but this last point has become relegated to the background, rather than taking often a more appropriate primary role. Trial practice has evolved but has its roots in R. A. ⋯ For comparison, a fixed sample size trial, with standard 5 per cent level of significance and 80 per cent power to detect a 10 per cent difference, requires treating over 700 patients in two groups, with the half allocated to inferior treatment considerably outnumbering the 68 expected under the decision-theoretic design, and the overall number simply too high for realistic application. In brief, the keys to answering the above 'why?' and 'how?' questions are ethics and software, respectively. Wider implications, both pros and cons, of implementing the particular method described will be discussed, with the overall conclusion that, where appropriate, clinical trials are now ready to undergo modernization from the agricultural age to the information age.
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Statistics in medicine · Oct 2007
Statistical education for medical students--concepts are what remain when the details are forgotten.
Teaching statistics to medical students is a challenging and often unrewarding task. However, few would argue the need for statistics in the medical school curriculum. In recent years, there has been a growing call for teaching only statistical concepts in medical schools. ⋯ In this article, we present our experience in teaching statistics to medical students at the Sackler School of Medicine, Tel-Aviv University, Israel. We then present the results of a recently held survey regarding the long-term contribution of the statistical curriculum to our students in different phases of their studies. We conclude by suggesting a new integrative statistical program, which incorporates the study of statistics into the entire medical curriculum.