Caregiver compliance to disclosure
HIV, mental health, overall health
REGRESSION
The least absolute shrinkage and selection operator (LASSO) penalized regression was used to select the best subset of predictors of disclosure for the 60 caregiver-child dyads who were randomized into the intervention group. We defined disclosure as a binary response of “disclosed” vs. “not disclosed,” with disclosure defined as caregiver-reported disclosure at any point within the 24-month follow-up period. Caregiver-child dyads who participated in the intervention were analyzed to select the best subset of multi-level predictors (HIV, mental health, overall health, economic, household, or community-related factors) of disclosure. LASSO selects a subset of predictors by shrinking the coefficients of the least contributive variables to zero, thereby excluding them from the model. LASSO combines the benefits of multiple regularizations and is particularly useful in studies such as this one where the number of observations is less than the number of variables and there are groups of correlated variables.To identify relevant predictors, all explanatory variables available in the data set were entered into the LASSO procedure. To calculate a reliable measure for the model validity, we used the conventional validation technique and randomly split the data into two data sets: 50% as the training data on which variable selection via the LASSO was done and 50% as the test data on which the logistic regression and corresponding pseudo R-squared were calculated. The dependent variable was disclosure after baseline. Independent variables included 80 demographic and clinical covariates described above. In the testing data, the most important predictors were selected using validation alpha standard error (ASE). To reduce the random effect arising out of the random split of the data, we used the bootstrapping method to repeat the random partition 1,000 times with replacement, and the average value of the obtained ASEs was calculated to produce the best regularization model (