Two postestimation commands for assessing confounding effects in medical and epidemiological studies

Controversy exists regarding proper methods for the selection of variables in confounder control in epidemiological studies. Various approaches have been proposed for selecting a subset of confounders among many possible subsets. This paper describes the use of two practical tools, Stata postestimation commands written by the author, to identify the presence and direction of confounding. One command, confall, plots all possible effect estimates against a statistical value such as the p-value or Akaike information criterion. This computing-intensive procedure allows researchers to inspect the variability of effect estimates from different possible models. Another command, confnd, uses a stepwise approach to identify confounders that have caused substantial changes in the effect measurement. Using three examples, the author illustrates the use of those programs in different situations. When all possible effect estimates are similar, indicating little confounding, the investigator can confidently report the presence and direction of the association between exposure and disease regardless of which variable selection method is used. On the other hand, when all possible effect estimates vary substantially, indicating the presence of confounding, a change-in-estimate plot and its corresponding table are helpful for identifying important confounders. Both commands can be used after most commonly used estimation commands for epidemiological data. The commands are available in the SSC Archive via the ssc command.