The proposed research project is a first submission of an R01 application by a young investigator. The goal of the proposed project is to bridge statistical advances and mental health research practice by developing and investigating new models to account for heterogeneity among unobserved (underlying) subpopulations. A research question often raised in mental health research is whether there are subgroups within the target population that differ in outcome distributions, background characteristics, developmental trajectories, and response to intervention treatments. Considering subpopulation differences often leads to major differences in the interpretation of research findings. Statistical challenges arise when subpopulation membership is completely or partly unobserved. Statistical methods to account for heterogeneity among latent subpopulations (latent classes) can be further complicated due to co-existing statistical challenges. The proposed project will investigate broader statistical modeling frameworks that can reflect more realistic settings while accounting for heterogeneity among unobserved subpopulations. General latent variable (GLV) modeling will be utilized as a flexible classification tool that captures both the continuous and the discrete spectrum of heterogeneity. The proposal is organized around three specific aims formulated in response to common complications that arise in mental health research: First, investigate methods to estimate differential effects of treatments for unobserved subpopulations. Second, investigate methods to model missing-data mechanisms using information on heterogeneity among unobserved subpopulations. Third, investigate methods to model heterogeneity among unobserved subpopulations accounting for multilevel data structures. Three strategies will be employed in pursuing these aims: First, perform mathematical investigations of new statistical models. Second, evaluate the fidelity of these models through intensive simulation studies. Finally, demonstrate applicability and practicality of new models through empirical examples in mental health research. Statistical modeling features demonstrated in empirical examples will have implications not on y in outcomes analysis, but also in study design strategies for mental health research.
| Stuart, Elizabeth A; Jo, Booil (2015) Assessing the sensitivity of methods for estimating principal causal effects. Stat Methods Med Res 24:657-74 |
| Wang, Chen-Pin; Jo, Booil; Brown, C Hendricks (2014) Causal inference in longitudinal comparative effectiveness studies with repeated measures of a continuous intermediate variable. Stat Med 33:3509-27 |
| Jo, Booil; Stuart, Elizabeth A; Mackinnon, David P et al. (2011) The Use of Propensity Scores in Mediation Analysis. Multivariate Behav Res 46:425-452 |
| Jo, Booil; Vinokur, Amiram D (2011) Sensitivity Analysis and Bounding of Causal Effects With Alternative Identifying Assumptions. J Educ Behav Stat 36:415-440 |
| Jo, Booil; Ginexi, Elizabeth M; Ialongo, Nicholas S (2010) Handling missing data in randomized experiments with noncompliance. Prev Sci 11:384-96 |
| Jo, Booil; Stuart, Elizabeth A (2009) On the use of propensity scores in principal causal effect estimation. Stat Med 28:2857-75 |
| Jo, Booil; Wang, Chen-Pin; Ialongo, Nicholas S (2009) Using latent outcome trajectory classes in causal inference. Stat Interface 2:403-412 |
| Brown, C Hendricks; Ten Have, Thomas R; Jo, Booil et al. (2009) Adaptive designs for randomized trials in public health. Annu Rev Public Health 30:1-25 |
| Jo, Booil (2008) Causal inference in randomized experiments with mediational processes. Psychol Methods 13:314-36 |
| Jo, Booil; Asparouhov, Tihomir; Muthen, Bengt O et al. (2008) Cluster randomized trials with treatment noncompliance. Psychol Methods 13:1-18 |
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