Effect decomposition and Table 2 fallacy
What? To show multiple adjusted effect estimates shown in a single table from a single regression model. What are the consequences? 1. Interpretative complexities- create confusion in the interpretation of direct effect estimates to total effect estimates for covariates in the model. 2. Though the effect estimate of the main exposure are not confounded but overall effect estimate is confounded due to including all the covarites in the same model 3. Effect estimate may complicate further by heterogeneity (variation, modification) of the exposure effect estimate are presented Why we need effect decomposition?: To identify the interventional factors and understand the pathways to design for intervention to improve health and prevent diseases. Few term before explaining the concept of effect decomposition: Primary effect- effect of primary exposure of interest in the initial adjustment model Secondary effect- effect of covariate (confounder or effect modifier) not of primary interest ...