2.5. Statistical analyses
REGRESSION, SECONDARY, REGRESSIONS
To assess the conditional increase of household wealth, we calculated the conditional gains of household wealth index between two single-time points, which was the standardized residual of regressing household wealth index at prior time point on household wealth index at later (We took adolescent HAZ, BAZ, FSIQ, and scores of total behavioral problems, externalizing and internalizing behavioral problems as primary outcomes, and other aspects of cognitive development and emotional and behavioral problems as secondary outcomes, respectively. We performed generalized linear regressions with Gaussian distribution and identity link to examine the associations of household wealth mobility indicators with adolescent health outcomes separately. The adjusted mean differences with their 95% CIs were estimated after including the covariables above. In addition, we performed stratified analyses by parental education (low and high educational level) and adolescent sex (male and female) after obtaining the interaction For sensitive analyses addressing the lost to follow-up, we conducted inverse probability weighting, and randomly sampled the lowest and highest 80% wealth households at pregnancy and repeated the analyses for primary outcomes. The weight of each participant is given by the inverse of the predicted probability for followed participant in a logistic regression model, which included parental education, occupation and age, maternal parity and mid-upper arm circumference, randomized regimens by duration, small-for-gestational age, and adolescent sex and age. In addition, we used E-value approach to assess the impact extent of potential unmeasured confounder on affecting the estimates above (