Inference in Case Control Studies


Gary King, Langche Zeng, and Shein-Chung Chow. 2010. “Inference in Case Control Studies.” In Encyclopedia of Biopharmaceutical Statistics, 3rd ed. New York: Marcel Dekker. Copy at
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Inference in Case Control Studies


Classic (or "cumulative") case-control sampling designs do not admit inferences about quantities of interest other than risk ratios, and then only by making the rare events assumption. Probabilities, risk differences, and other quantities cannot be computed without knowledge of the population incidence fraction. Similarly, density (or "risk set") case-control sampling designs do not allow inferences about quantities other than the rate ratio. Rates, rate differences, cumulative rates, risks, and other quantities cannot be estimated unless auxiliary information about the underlying cohort such as the number of controls in each full risk set is available. Most scholars who have considered the issue recommend reporting more than just the relative risks and rates, but auxiliary population information needed to do this is not usually available. We address this problem by developing methods that allow valid inferences about all relevant quantities of interest from either type of case-control study when completely ignorant of or only partially knowledgeable about relevant auxiliary population information. This is a somewhat revised and extended version of Gary King and Langche Zeng. 2002. "Estimating Risk and Rate Levels, Ratios, and Differences in Case-Control Studies," Statistics in Medicine, 21: 1409-1427. You may also be interested in our related work in other fields, such as in international relations, Gary King and Langche Zeng. "Explaining Rare Events in International Relations," International Organization, 55, 3 (Spring, 2001): 693-715, and in political methodology, Gary King and Langche Zeng, "Logistic Regression in Rare Events Data," Political Analysis, Vol. 9, No. 2, (Spring, 2001): Pp. 137--63.

Last updated on 07/10/2013