Subgroup Analysis Using Multiple Linear Regression: Confounding, Interaction, Synergism
When the size of the study permits, important demographic or baseline value-defined subgroups of patients can be studied for unusually large or small efficacy responses; e.g. comparison of effects by age, sex; by severity or prognostic groups. Naturally, such analyses are not intended to “salvage” an otherwise negative study, but may be may be helpful in refining patient or dose selection for subsequent studies.1Most studies have insufficient size to assess efficacy meaningfully in subgroups of patients. Instead a regression model for the primary or secondary efficacy-variables can be used to evaluate whether specific variables are confounders for the treatment effect, and whether the treatment effect interacts with specific covariates. The particular (statistical) regression model chosen, depends on the nature of the efficacy variables, and the covariates to be considered should be meaningful according to the current state of knowledge. In particular, when studying interactions, the results of the regression analysis are more valid when complemented by additional exploratory analyses within relevant subgroups of patients or within strata defined by the covariates.
KeywordsRegression Weight Linear Regression Line Efficacy Variable Residual Term Residual Standard Deviation
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