Bias Adjustment with Polychotomous Logistic Regression in Matched Case‐Control Studies with Two Control Groups

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Abstract
Relative risk estimation in case‐control studies is based on the premise that the control group represents the underlying population. Often more than one control group is collected in order to minimize the possibility of accepting a false result.In this paper it is assumed that a case is matched individually to two different controls and that one control group may lack representativeness with respect to some risk factors. It is discussed whether this group may be used for relative risk estimation and a polychotomous logistic regression model is suggested in which these differences between the control groups are taken into account. A practical method for model search is given.Data of two case‐control studies are used to demonstrate the method. In a simulation study its efficiency is investigated. Some computations illustrate the effect of ignoring a bias in one control group.