Statistical methods
depressive disorder, depression, depressive
REGRESSION
Based on our sample size, the present study had 80% power to detect relationships between patient and household member measures that were ≥ 0.19. As a first step in analysis, we drew from baseline interviews to generate descriptive statistics that characterized patients and household members. This included frequencies for discrete variables and means and standard deviations for continuous variables. The chief interest in this study was the prevalence of depression symptoms (PHQ-9 score > 0) and the prevalence of depressive disorder (PHQ-9 score > 9) among household participants.Next, we conducted multivariable linear regression to assess correlates of depressive symptom severity among household members, including health and sociodemographic characteristics as independent variables. We separately configured this analysis as a multivariable logistic regression, with the outcome structured as whether the household member had an indication of depressive disorder, based on a PHQ-9 score greater than nine (1=yes; 0=no).