The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients

It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more exposures and also for several confounders from a single logistic regression. This can lead to mistaken interpretations of these estimates. We use causal diagrams to display the sources of the problems. Presentation of exposure and confounder effect estimates from a single model may lead to several interpretative difficulties, inviting confusion of direct-effect estimates with total-effect estimates for covariates in the model. These effect estimates may also be confounded even though the effect estimate for the main exposure is not confounded.…

• NI
  National Institutes of Health

  Awards: 2P30 AI064518, AI064518
• DG
  Duke Global Health Institute, Duke University
• UO
  University of California, Los Angeles