观察研究
置信区间
优势比
医学
无效假设
描述性统计
队列研究
荟萃分析
统计
内科学
数学
作者
David A. Grimes,Kenneth F. Schulz
出处
期刊:The Lancet
[Elsevier BV]
日期:2002-01-01
卷期号:359 (9300): 57-61
被引量:693
标识
DOI:10.1016/s0140-6736(02)07283-5
摘要
Many clinicians report that they cannot read the medical literature critically. To address this difficulty, we provide a primer of clinical research for clinicians and researchers alike. Clinical research falls into two general categories: experimental and observational, based on whether the investigator assigns the exposures or not. Experimental trials can also be subdivided into two: randomised and non-randomised. Observational studies can be either analytical or descriptive. Analytical studies feature a comparison (control) group, whereas descriptive studies do not. Within analytical studies, cohort studies track people forward in time from exposure to outcome. By contrast, case-control studies work in reverse, tracing back from outcome to exposure. Cross-sectional studies are like a snapshot, which measures both exposure and outcome at one time point. Descriptive studies, such as case-series reports, do not have a comparison group. Thus, in this type of study, investigators cannot examine associations, a fact often forgotten or ignored. Measures of association, such as relative risk or odds ratio, are the preferred way of expressing results of dichotomous outcomes-eg, sick versus healthy. Confidence intervals around these measures indicate the precision of these results. Measures of association with confidence intervals reveal the strength, direction, and a plausible range of an effect as well as the likelihood of chance occurrence. By contrast, p values address only chance. Testing null hypotheses at a p value of 0.05 has no basis in medicine and should be discouraged.
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