Obesity rates continue to exhibit an upward trajectory, particularly in the US, and is the underlying cause of several comorbidities, including but not limited to high blood pressure, high cholesterol, diabetes, heart disease, stroke, and cancers. To monitor obesity, body mass index (BMI) and proportion body fat (PBF) are two commonly used measurements. Although BMI and PBF changes over time in an individual’s lifespan and their relationship may also change dynamically, existing work has mostly remained cross-sectional, or separately modeling BMI and PBF. A combined longitudinal assessment is expected to be more effective in unravelling their complex interplay. To mitigate this, we consider Bayesian cross-domain latent growth curve models within a structural equation modeling framework, which simultaneously handles issues such as individually varying time metrics, proportion data, and potential missing not at random data for joint assessment of the longitudinal changes of BMI and PBF. Through simulation studies, we observe that our proposed models and estimation method yielded parameter estimates with small bias and mean squared error in general, however, a mis-specified missing data mechanism may cause inaccurate and inefficient parameter estimates. Furthermore, we demonstrate application of our method to a motivating longitudinal obesity study, controlling for both time-invariant (such as, sex), and time-varying (such as diastolic and systolic blood pressure, biceps skinfold, bioelectrical impedance, and waist circumference) covariates in separate models. Under time-invariance, we observe that the initial BMI level and the rate of change in BMI influenced PBF. However, in presence of time-varying covariates, only the initial BMI level influenced the initial PBF. The added-on selection model estimation indicated that observations with higher PBF values were less likely to be missing.
For large observational studies lacking a control group (unlike randomized controlled trials, RCT), propensity scores (PS) are often the method of choice to account for pre-treatment confounding in baseline characteristics, and thereby avoid substantial bias in treatment estimation. A vast majority of PS techniques focus on average treatment effect estimation, without any clear consensus on how to account for confounders, especially in a multiple treatment setting. Furthermore, for time-to event outcomes, the analytical framework is further complicated in presence of high censoring rates (sometimes, due to non-susceptibility of study units to a disease), imbalance between treatment groups, and clustered nature of the data (where, survival outcomes appear in groups). Motivated by a right-censored kidney transplantation dataset derived from the United Network of Organ Sharing (UNOS), we investigate and compare two recent promising PS procedures, (a) the generalized boosted model (GBM), and (b) the covariate-balancing propensity score (CBPS), in an attempt to decouple the causal effects of treatments (here, study subgroups, such as hepatitis C virus (HCV) positive/negative donors, and positive/negative recipients) on time to death of kidney recipients due to kidney failure, post transplantation. For estimation, we employ a 2-step procedure which addresses various complexities observed in the UNOS database within a unified paradigm. First, to adjust for the large number of confounders on the multiple sub-groups, we fit multinomial PS models via procedures (a) and (b). In the next stage, the estimated PS is incorporated into the likelihood of a semi-parametric cure rate Cox proportional hazard frailty model via inverse probability of treatment weighting, adjusted for multi-center clustering and excess censoring, Our data analysis reveals a more informative and superior performance of the full model in terms of treatment effect estimation, over sub-models that relaxes the various features of the event time dataset.