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When risk factors for an infectious disease are unknown, a method commonly employed is to investigate parallels with known infections (covariate infections). Data sets of value here are those for specified populations in which the seroprevalence of antibodies for multiple infections has been ascertained. The use of markers of covariate infections in multivariable analyses is problematic when the covariate infection is not itself an independent risk factor for the outcome of interest. In the performance of these analyses, the authors recommend the following strategy: 1) For estimates of the effects of measured risk factors on the outcome, adjustment for the covariate infection should not be done; this will avoid problems of overadjustment. 2) After control for the measured risk factors, an estimate of the "effect" of the covariate infection may be used as an indicator of the presence of unmeasured shared risk factors. 3) When shared, measured risk factors exist, the authors propose the use of methods developed for analysis of repeated measures of categorical variables to assist in inference about shared mechanisms of action of these risk factors. This analytic strategy takes advantage of the method of analogy for building understanding of transmission of new agents through their parallels with better known ones and is useful in the development of hypotheses.