Statistics in medicine
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Conventional wisdom suggests that for small data sets having substantial skew, one should attempt to determine the correct distributional form, if possible, and apply statistical methods appropriate for that distribution. Transformations such as the log or square root are often used. If an appropriate distributional form cannot be determined, a distribution-free procedure such as a rank transformation or a randomization test procedure can be used. ⋯ The UMP test, that is, LOG(X), produced the highest power. There was very little power lost for the SQRT t-test. The loss in power varied between 2 per cent and 5 per cent for the RANK test.(ABSTRACT TRUNCATED AT 400 WORDS)
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Statistics in medicine · Jan 1994
Comparative StudyStatistics in Medicine: citations of papers in the first ten years.
All papers from Volume 1 of Statistics in Medicine were followed up in the Science Citation Index. There were 6.7 citations per paper in medical journals as opposed to 1.5 citations per paper in statistical journals, and overall there were 8.7 citations per paper. The average citation rate was lower than that of the first issue of Biometrics, published in the same year, but this was partly because of a greater proportion of zero-cited papers. ⋯ Volumes 2-6 of Statistics in Medicine and Volumes 29-33 of Biometrics, for the years 1983-87, were followed up for statistical/medical source of the citations. Citations for Volumes 2-4 of Statistics in Medicine were relatively low, but picked up by Volume 5. Biometrics had more citations from statistical sources than from medical, by contrast to Statistics in Medicine which had far more citations from medical sources than from statistical.
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This paper describes a Bayesian approach to the design and analysis of clinical trials, and compares it with the frequentist approach. Both approaches address learning under uncertainty. But they are different in a variety of ways. ⋯ For example, when choosing or modifying the design of a clinical trial, Bayesians use all available information, including that which comes from the trial itself. The ability to calculate predictive probabilities for future observations is a distinct advantage of the Bayesian approach to designing clinical trials and other decisions. An important difference between Bayesian and frequentist thinking is the role of randomization.
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In addition to the safety, it is essential to establish the causal efficacy of extant and new treatments, and well-designed clinical trials are thought by most to be the 'gold standard' to accomplish this. Contrary to most statisticians' and regulators' views, however, I will argue that the concept of causation involved in clinical trials is not all that clear. ⋯ I characterize 'epidemiological causation' as probabilistic and formulated at a population level, and dependent on certain general criteria for causation as well as study-design considerations. I then attempt to clarify the connections between these concepts of causation and Cartwright's views on complexity and causality, a 'Bayesian' framework proposed by Rubin and further elaborated by Holland, and Glymour and his colleagues' recent directed graphical causal modelling approach.
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Statistics in medicine · Apr 1993
P-values after repeated significance testing: a simple approximation method.
The P-value after a repeated significance test is a useful measure of the strength of evidence against the null hypothesis. Its computation, however, requires a computer-intensive numerical integration method. The P-value is not conceptually straightforward, because it depends on how the sample space is ordered, which can be arbitrary. ⋯ In this paper we present a simple method of approximating P-values. We provide tables to implement the method for two to ten stages with alpha = 0.1, 0.05 and 0.01 for the Pocock and O'Brien-Fleming procedures. The proposed method can be applied to both orderings.