Analysis of matched case-control data

Journal of Clinical Epidemiology 56 (2003) 814

LETTER TO THE EDITOR

Analysis of matched case-control data

Analysis of matched case-control data: Author reply

Rahman et al. [1] performed a Medline search to study the use of conditional and unconditional logistic regression in “matched” case-control studies. They concluded that their study “indicates that a good number of articles used an unconditional maximum likelihood approach for matched data” and the odds ratios generated are therefore biased. However, their literature review did not distinguish “frequency matching” from “individual matching” [2]. Hence, their analysis did not support their conclusion. Frequency-matched data do not require conditional logistic regression; individually matched data do. As Rahman et al. themselves pointed out, the unconditional logistic regression is a suitable analytic method if the number of parameters is small in relation to the number of subjects. This is usually the case in the analysis of frequency-matched casecontrol data. As long as the matching variables are included in the unconditional logistic regression model as covariates, the odds ratios will not be biased by the procedure of frequency matching. To avoid suspicion, frequency-matched case-control studies should always report whether they have included the matching variables in the analysis.

I apologize for not mentioning about the frequency matching and individual matching separately in our previous report [1]. Thanks to Dr. Cheung for pointing it out. I have gone through the abstracts of the articles (48 abstracts), which were identified as matched studies with unconditional logistic regression. Twenty studies among the total 48 (41.7%) had frequency matching; the rest used individual matching (58.3%). Thus, it can be said that 5.3% (27 out of 507) of the case-control studies (instead of 9.5% as we mention in our previous article) with individually matched data actually used an unconditional maximum likelihood approach for their logistic regression model—still a good number to ponder.

Yin-Bun Cheung Clinical Trials and Epidemiological Sciences National Cancer Centre, Singapore References

Reference [1] Rahman M, Sakamoto J, Fukui T. Conditional versus unconditional logistic regression in the medical literature. J Clin Epidemiol 2003;56: 101–2.

doi: 10.1016/S0895-4356(03)00127-6

[1] Rahman M, Sakamoto J, Fukui T. Conditional versus unconditional logistic regression in the medical literature. J Clin Epidemiol 2003;56: 101–2. [2] Rothman KJ. Modern epidemiology. Boston: Little, Brown and Company; 1986. doi: 10.1016/S0895-4356(03)00180-X

0895-4356/03/$ – see front matter

Mahbubur Rahman Department of Epidemiological and Clinical Research Information Management Kyoto University School of Public Health Kyoto, Japan

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