Defining exposure in case-control studies: a new approach

Abstract

Many epidemiologists conducting case-control studies choose to dichotomize their exposure data to make the analysis of the data easier and its presentation more straightforward. The choice of a specific rule for dichotomization can have a large effect on the outcome measure, the odds ratio, although this effect is rarely studied. The authors present a graphic approach for exploring this effect, the quantile-quantile (Q-Q) plot. By examining a Q-Q plot, an investigator simultaneously gains information about the distribution of exposures among cases, the distribution of exposures among controls, and odds ratios at all possible cutpoints and their standard errors. In addition, by finding the slope at each point along the Q-Q curve, it is possible to estimate the rate ratios for each possible value of exposure. The authors view the use of the Q-Q plot as an exploratory tool. It enables the investigator to become more familiar with the data and check for irregularities such as outliers, nonlinearities, or nonmonotonic dose-response curves, and idiosyncratic variations of the odds ratio. The authors present an example evaluating the risk of low birth weight as a function of mother's age for Boston births in 1984.