Statistics in medicine
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Statistics in medicine · Jun 2002
Exploring sources of heterogeneity in systematic reviews of diagnostic tests.
It is indispensable for any meta-analysis that potential sources of heterogeneity are examined, before one considers pooling the results of primary studies into summary estimates with enhanced precision. In reviews of studies on the diagnostic accuracy of tests, variability beyond chance can be attributed to between-study differences in the selected cutpoint for positivity, in patient selection and clinical setting, in the type of test used, in the type of reference standard, or any combination of these factors. In addition, heterogeneity in study results can also be caused by flaws in study design. ⋯ Application of regression techniques in meta-analysis of diagnostic tests can provide relevant additional information. Results of such analyses will help understand problems with the transferability of diagnostic tests and to point out flaws in primary studies. As such, they can guide the design of future studies.
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Statistics in medicine · May 2002
Study control, violators, inclusion criteria and defining explanatory and pragmatic trials.
Important differences between explanatory and pragmatic studies were originally argued by Schwartz and Lellouch. Three important differences between the two types of study involve study control, study violators and inclusion criteria. It was originally argued that explanatory studies are highly controlled, and pragmatic studies may be looser and more like 'real life'. ⋯ Routine criticism of randomized controlled trials for being unrepresentative is unwarranted. We should accept that most trials in humans are 'explanatory'. The division line should be moved, so that pragmatic studies are in the domain of non-therapeutics and complex treatments.
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Statistics in medicine · Apr 2002
Non-parametric confidence interval estimation for competing risks analysis: application to contraceptive data.
Non-parametric maximum likelihood estimation of the cause specific failure probability, and of its standard error, in the presence of competing risks is discussed with reference to some contraceptive use dynamics data from Bangladesh. The cause specific incidence function provides an intuitively appealing summary curve for failure rates and probabilities, such as probabilities of discontinuation of different kinds of contraception, based on right-censored data of the particular event. ⋯ The accuracy of these intervals, as well as those based on the log(-log) transformation and the arcsine transformation, are compared by simulations. We find that Dinse and Larson's formula, used in conjuction with a log(-log) transform, yields reliable standard error estimates and accurate coverage in samples of small and large size, and can be recommended for use in this situation.
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Statistics in medicine · Apr 2002
Medical students' perspective on the teaching of medical statistics in the undergraduate medical curriculum.
Two undergraduate medical students at the University of Bristol commented on their experiences of learning medical statistics. In general, medical students' focus is on acquiring skills needed to practice clinical medicine, and great care must be taken to explain why disciplines such as statistics and epidemiology are relevant to this. Use of real examples and an emphasis on the need for evidence has meant that medical students are increasingly aware of the pressure on clinicians to justify their treatment decisions, and the associated need to be able to understand and critically appraise medical research. ⋯ Medical statistics should be taught early in the curriculum, but there is a need to reinforce such skills throughout the course. Teaching and assessment methods should recognize that what is being taught is a practical skill of clinical relevance. This means that problem based small groups, data interpretation exercises and objective structured clinical examinations will be more productive than traditional teaching and examination methods.
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Statistics in medicine · Dec 2001
Comparative StudyImpact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.
Many cohort studies and clinical trials are designed to compare rates of change over time in one or more disease markers in several groups. One major problem in such longitudinal studies is missing data due to patient drop-out. The bias and efficiency of six different methods to estimate rates of changes in longitudinal studies with incomplete observations were compared: generalized estimating equation estimates (GEE) proposed by Liang and Zeger (1986); unweighted average of ordinary least squares (OLSE) of individual rates of change (UWLS); weighted average of OLSE (WLS); conditional linear model estimates (CLE), a covariate type estimates proposed by Wu and Bailey (1989); random effect (RE), and joint multivariate RE (JMRE) estimates. ⋯ Thus, the GEE method may not be appropriate for analysing such longitudinal marker data. The potential biases due to incomplete data require greater recognition in reports of longitudinal studies. Sensitivity analyses to assess the effect of drop-outs on inferences about the target parameters are important.