This Mentored Research Scientist Development Award will support the candidate in establishing an independent research career as a methodologist who develops cutting-edge statistical methodology with direct applications in alcoholism and substance abuse prevention. Career development training areas include: 1) Development psychopathology;2) Alcoholism and substance abuse prevention research and methodology;3) Modern computational techniques;and 4) Modeling longitudinal data using nonparametric regression.
The specific aims of the proposed research plan are: 1) To develop a new Bayesian latent variable model for assessing dynamic childhood risk for alcoholism in multiple dimensions;2) To develop a new semi-parametric regression model for validating the association between childhood risk and adolescent substance use development;and 3) To apply the methodologies developed in Specific Aims 1 and 2 to test hypotheses based on the data collected from an ongoing 21-year long prospective study of families at high risk for alcoholism. The methodology developed in this study will be useful in establishing construct validity of commonly used measures of childhood risk. Two statistical programs will be developed and distributed to prevention scientists. The derived longitudinal model based on the empirical data should be useful in projecting a new child's developmental trajectory of adolescent substance use based on his/her childhood risk measures. Thus, it has the potential to provide crucial information about the timing and dose an individual child needs for early intervention.
Alcohol use, alcohol problems and alcohol symptomatology are all developmental phenomena. The applicant's work would develop new methodology which allows for more accurate description of these developmental relationships than is allowed by currently available methods and takes account of the special statistical properties of the measures most commonly used by the field.
|Yang, Hanyu; Li, Runze; Zucker, Robert A et al. (2016) Two-stage model for time varying effects of zero-inï¬‚ated count longitudinal covariates with applications in health behaviour research. J R Stat Soc Ser C Appl Stat 65:431-444|
|Yang, James J; Li, Jia; Williams, L Keoki et al. (2016) An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function. BMC Bioinformatics 17:19|
|Yang, Guangren; Yu, Ye; Li, Runze et al. (2016) Feature Screening in Ultrahigh Dimensional Cox's Model. Stat Sin 26:881-901|
|Yang, Hanyu; Cranford, James A; Li, Runze et al. (2015) Two-stage model for time-varying effects of discrete longitudinal covariates with applications in analysis of daily process data. Stat Med 34:571-81|
|Buu, Anne; Dabrowska, Agata; Heinze, Justin E et al. (2015) Gender differences in the developmental trajectories of multiple substance use and the effect of nicotine and marijuana use on heavy drinking in a high-risk sample. Addict Behav 50:6-12|
|Jester, Jennifer M; Wong, Maria M; Cranford, James A et al. (2015) Alcohol expectancies in childhood: change with the onset of drinking and ability to predict adolescent drunkenness and binge drinking. Addiction 110:71-9|
|Dziak, John J; Li, Runze; Zimmerman, Marc A et al. (2014) Time-varying effect models for ordinal responses with applications in substance abuse research. Stat Med 33:5126-37|
|Buu, Anne; Dabrowska, Agata; Mygrants, Marjorie et al. (2014) Gender differences in the developmental risk of onset of alcohol, nicotine, and marijuana use and the effects of nicotine and marijuana use on alcohol outcomes. J Stud Alcohol Drugs 75:850-8|
|Buu, Anne; Li, Runze; Walton, Maureen A et al. (2014) Changes in substance use-related health risk behaviors on the timeline follow-back interview as a function of length of recall period. Subst Use Misuse 49:1259-69|
|Yang, James J; Li, Jia; Buu, Anne et al. (2013) Efficient inference of local ancestry. Bioinformatics 29:2750-6|
Showing the most recent 10 out of 14 publications