Meta-analysis, Simpson's paradox, and the number needed to treat

被引：110

作者：

Altman D.G. ^{[1
]}

Deeks J.J. ^{[1
]}

机构：

[1] Centre for Statistics in Medicine, Institute of Health Sciences, Headington, Oxford, Old Road

来源：

BMC Medical Research Methodology | / 2卷 / 1期

关键词：

Secondary Care; Risk Difference; Baseline Risk; Median Placebo; Encourage Smoking Cessation;

D O I：

10.1186/1471-2288-2-3

中图分类号：

学科分类号：

摘要：

Background: There is debate concerning methods for calculating numbers needed to treat (NNT) from results of systematic reviews. Methods: We investigate the susceptibility to bias for alternative methods for calculating NNTs through illustrative examples and mathematical theory. Results: Two competing methods have been recommended: one method involves calculating the NNT from meta-analytical estimates, the other by treating the data as if it all arose from a single trial. The 'treat-as-one-trial' method was found to be susceptible to bias when there were imbalances between groups within one or more trials in the meta-analysis (Simpson's paradox). Calculation of NNTs from meta-analytical estimates is not prone to the same bias. The method of calculating the NNT from a meta-analysis depends on the treatment effect used. When relative measures of treatment effect are used the estimates of NNTs can be tailored to the level of baseline risk. Conclusions: The treat-as-one-trial method of calculating numbers needed to treat should not be used as it is prone to bias. Analysts should always report the method they use to compute estimates to enable readers to judge whether it is appropriate.

引用

页码：1 / 5

页数：4

共 16 条

[1]

Cates C., Simpson's paradox and calculation of number needed to treat from meta-analysis, BMC Medical Research Mathodology, 2, (2002)

[2]

Moore R.A., Gavaghan D.J., Edwards J.E., Wiffen P., McQuay H.J., Pooling data for number needed to treat: No problems for apples, BMC Medical Research Methodology, 2, (2002)

[3]

Deeks J.J., Altman D.G., Bradburn M.J., Statistical methods for examining heterogeneity and combining results from several studies in meta-analysis, Systematic Reviews in Health Care. Meta-analysis in Context, pp. 285-312, (2001)

[4]

Deeks J.J., Altman D.G., Effect measures for meta-analysis of trials with binary outcomes, Systematic Reviews in Health Care. Meta-analysis in Context, pp. 313-335, (2001)

[5]

Deeks J.J., Issues in selection of a summary statistic for meta-analysis of clinical trials with binary data, Stat Med

[6]

Engels E.A., Schmid C.H., Terrin N., Oikin I., Lau J., Heterogeneity and statistical significance in meta-analysis: An empirical study of 125 meta-analyses, Stat Med, 19, pp. 1707-1728, (2000)

[7]

Cook R.J., Sackett D.L., The number needed to treat: A clinically useful measure of treatment effect, BMJ, 310, pp. 452-454, (1995)

[8]

Ebrahim S., Numbers needed to treat derived from meta-analyses: Pitfalls and cautions, Systematic Reviews in Health Care. Meta-analysis in Context, pp. 386-399, (2001)

[9]

Silagy C., Lancaster T., Stead L., Mant D., Fowler G., Nicotine replacement therapy for smoking cessation, The Cochrane Library, (2001)

[10]

Rice V.H., Stead L.F., Nursing interventions for smoking cessation, The Cochrane Library, (2001)

← 1 2 →