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Description
Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
ContributorsCham, Hei Ning (Author) / Tein, Jenn-Yun (Thesis advisor) / Enders, Stephen G (Thesis advisor) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013
Description
It is well-established that maternal depression is significantly related to internalizing and externalizing behavioral problems and psychopathology in general. However, research suggests maternal depression does not account for all the variance of these outcomes and that other family contextual factors should be investigated. The role of fathers beyond their simple presence or absence is one factor that needs to be further investigated in the context of maternal depression. The proposed study used prospective and cross-sectional analyses to examine father effects (i.e., paternal depression, alcohol use, involvement, and familism) on youth internalizing and externalizing symptoms within the context of maternal depression. The sample consisted of 405 Mexican-American families who had a student in middle school. Data were collected when the students were in 7th and 10th grade. Results from path analyses revealed that maternal depression significantly predicted concurrent youth internalizing symptoms in 7th and 10th grade and externalizing symptoms in 10th grade. In contrast, paternal depression was not related to adolescent symptomatology at either time point, nor was paternal alcoholism, and analyses failed to support moderating effects for any of the paternal variables. However, paternal involvement (father-report) uniquely predicted youth internalizing and externalizing symptoms over and above maternal depression in 7th grade. Youth report of paternal involvement uniquely predicted both internalizing and externalizing in 7th and 10th grade. Paternal familism uniquely predicted youth externalizing symptoms in 7th grade. The present findings support that maternal depression, but not paternal depression, is associated with concurrent levels of youth symptomatology in adolescence. The study did not support that fathers adjustment moderated (exacerbate or buffer) maternal depression effects. However, paternal involvement and paternal familism showed compensatory effects on youth symptomatology in concurrent analyses.
ContributorsMontano, Zorash (Author) / Gonzales, Nancy A. (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Roosa, Mark (Committee member) / Arizona State University (Publisher)
Created2013
Description
Understanding how adherence affects outcomes is crucial when developing and assigning interventions. However, interventions are often evaluated by conducting randomized experiments and estimating intent-to-treat effects, which ignore actual treatment received. Dose-response effects can supplement intent-to-treat effects when participants are offered the full dose but many only receive a partial dose due to nonadherence. Using these data, we can estimate the magnitude of the treatment effect at different levels of adherence, which serve as a proxy for different levels of treatment. In this dissertation, I conducted Monte Carlo simulations to evaluate when linear dose-response effects can be accurately and precisely estimated in randomized experiments comparing a no-treatment control condition to a treatment condition with partial adherence. Specifically, I evaluated the performance of confounder adjustment and instrumental variable methods when their assumptions were met (Study 1) and when their assumptions were violated (Study 2). In Study 1, the confounder adjustment and instrumental variable methods provided unbiased estimates of the dose-response effect across sample sizes (200, 500, 2,000) and adherence distributions (uniform, right skewed, left skewed). The adherence distribution affected power for the instrumental variable method. In Study 2, the confounder adjustment method provided unbiased or minimally biased estimates of the dose-response effect under no or weak (but not moderate or strong) unobserved confounding. The instrumental variable method provided extremely biased estimates of the dose-response effect under violations of the exclusion restriction (no direct effect of treatment assignment on the outcome), though less severe violations of the exclusion restriction should be investigated.
ContributorsMazza, Gina L (Author) / Grimm, Kevin J. (Thesis advisor) / West, Stephen G. (Thesis advisor) / Mackinnon, David P (Committee member) / Tein, Jenn-Yun (Committee member) / Arizona State University (Publisher)
Created2018
Description
Time-to-event analysis or equivalently, survival analysis deals with two variables simultaneously: when (time information) an event occurs and whether an event occurrence is observed or not during the observation period (censoring information). In behavioral and social sciences, the event of interest usually does not lead to a terminal state such as death. Other outcomes after the event can be collected and thus, the survival variable can be considered as a predictor as well as an outcome in a study. One example of a case where the survival variable serves as a predictor as well as an outcome is a survival-mediator model. In a single survival-mediator model an independent variable, X predicts a survival variable, M which in turn, predicts a continuous outcome, Y. The survival-mediator model consists of two regression equations: X predicting M (M-regression), and M and X simultaneously predicting Y (Y-regression). To estimate the regression coefficients of the survival-mediator model, Cox regression is used for the M-regression. Ordinary least squares regression is used for the Y-regression using complete case analysis assuming censored data in M are missing completely at random so that the Y-regression is unbiased. In this dissertation research, different measures for the indirect effect were proposed and a simulation study was conducted to compare performance of different indirect effect test methods. Bias-corrected bootstrapping produced high Type I error rates as well as low parameter coverage rates in some conditions. In contrast, the Sobel test produced low Type I error rates as well as high parameter coverage rates in some conditions. The bootstrap of the natural indirect effect produced low Type I error and low statistical power when the censoring proportion was non-zero. Percentile bootstrapping, distribution of the product and the joint-significance test showed best performance. Statistical analysis of the survival-mediator model is discussed. Two indirect effect measures, the ab-product and the natural indirect effect are compared and discussed. Limitations and future directions of the simulation study are discussed. Last, interpretation of the survival-mediator model for a made-up empirical data set is provided to clarify the meaning of the quantities in the survival-mediator model.
ContributorsKim, Han Joe (Author) / Mackinnon, David P. (Thesis advisor) / Tein, Jenn-Yun (Thesis advisor) / West, Stephen G. (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2017
Description
Mediation analysis is integral to psychology, investigating human behavior’s causal mechanisms. The diversity of explanations for human behavior has implications for the estimation and interpretation of statistical mediation models. Individuals can have similar observed outcomes while undergoing different causal processes or different observed outcomes while receiving the same treatment. Researchers can employ diverse strategies when studying individual differences in multiple mediation pathways, including individual fit measures and analysis of residuals. This dissertation investigates the use of individual residuals and fit measures to identify individual differences in multiple mediation pathways. More specifically, this study focuses on mediation model residuals in a heterogeneous population in which some people experience indirect effects through one mediator and others experience indirect effects through a different mediator. A simulation study investigates 162 conditions defined by effect size and sample size for three proposed methods: residual differences, delta z, and generalized Cook’s distance. Results indicate that analogs of Type 1 error rates are generally acceptable for the method of residual differences, but statistical power is limited. Likewise, neither delta z nor gCd could reliably distinguish between contrasts that had true effects and those that did not. The outcomes of this study reveal the potential for statistical measures of individual mediation. However, limitations related to unequal subpopulation variances, multiple dependent variables, the inherent relationship between direct effects and unestimated indirect effects, and minimal contrast effects require more research to develop a simple method that researchers can use on single data sets.
ContributorsSmyth, Heather Lynn (Author) / MacKinnon, David (Thesis advisor) / Tein, Jenn-Yun (Committee member) / McNeish, Daniel (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Internalizing symptoms are prevalent among adolescents, especially among Latinos, and can have negative consequences on health and development. Understanding the risk and protective factors leading to internalizing difficulties among Latino youth is critical. The current study sought to assess the effects of family risk and peer social rejection in the seventh grade on internalizing symptoms in the tenth grade, and the potential buffering effects of social support from family and from friends, among a sample of 749 Mexican American youth. Structural equation modeling was used to examine pathways from seventh grade family risk and peer social rejection to internalizing symptoms in the tenth grade. Perceived social support from family and perceived social support from friends were tested as moderators of these relations. Gender differences in these pathways were also assessed. Results showed that family risk did not predict tenth grade internalizing symptoms, but that peer social rejection predicted increased internalizing symptoms for girls. Furthermore, buffering effects were not confirmed; rather social support from both friends and family had no effect on the relation between family risk and internalizing symptoms, and high levels of social support from both sources amplified the effect of peer social rejection on internalizing symptoms. Secondary analyses suggested that at low levels of social support from both sources, peer social rejection predicted decreased internalizing symptoms for males. Limitations and implications for prevention and future research are discussed.
ContributorsJenchura, Emily C (Author) / Gonzales, Nancy (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Luecken, Linda (Committee member) / Arizona State University (Publisher)
Created2015