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Mental Health


The Methods research area develops and applies innovative qualitative and quantitative methods for public mental health research, with a focus on statistical methods and economic models. These methods, applied across other program areas, are crucial for generating accurate answers to research questions. Faculty in the methods area address complications regarding missing data and non-adherence in randomized trials, as well as teach students how to analyze complex data such as DNA or complex longitudinal data, how to measure and model variables that are not directly observable and how to model the cost and benefit trade-offs of preventive interventions. There are strong links between the methods research area and other groups in the Department, such as the substance use research group, the Center for Prevention and Early Intervention and the Center for the Prevention of Youth Violence.

There are three particular research areas within the Methods area: statistics, economics and latent variables and measurement. The statistics area, led by Dr. Elizabeth Stuart, focuses on the development of statistical methods for estimating causal effects.This includes methods for non-experimental studies, such as estimating the long-term consequences of adolescent drug use, as well as methods for designing and analyzing randomized experiments, such as of school-based preventive interventions. An additional area of emphasis involves statistical genetics. The economics area, led by Dr. Alexandre, focuses on addiction economics, specifically the economics of drug and alcohol abuse and mental disorders and the evaluation of treatment programs for these disorders. A third area, led by Dr. Furr-Holden and Dr. Leoutsakos, examines methods for measuring concepts related to mental health, such as measures of the built environment and alcohol use among drivers, and for modeling relationships between observed variables and variables that we not directly observe (latent variables), such as cognitive decline.

The Methods research area also has strong links with other departments and centers in the school. This includes joint appointments with the Department of Biostatistics, as well as links to methods-related groups such as the causal inference and health economics working groups. Student involvement in the Methods area consists of research assistance opportunities, as well as advising by faculty members in statistical and economic methods. Relevant coursework includes term-long and summer institute courses in the Department of Mental Health, such as the Methods seminar, courses in the design of cluster-randomized trials, and a two-term sequence on statistics for psychosocial research. Courses in the Biostatistics department are also relevant, including a causal inference course taught by Dr. Stuart. Many students interested in this program area also do a concurrent MHS in Biostatistics.

Mixed Methods Research Training Program for the Health Sciences

Growing awareness of the need to employ mixed methods to address population and behavioral health has resulted in an exponential increase in mixed methods studies. The Mixed Methods Research Training Program for the Health Sciences (MMRTP) is a yearlong training program for researchers in the health sciences funded by several Institutes of the National Institutes of Health under the auspices of the Office of Behavioral and Social Science Research. The overarching goal of the MMRTP is to provide a state-of-the-art methods training program to enhance the mixed methods skills of NIH investigators. Scholars must have a doctoral degree (details of eligibility requirements can be found at our website). While the prototypical participant may be in the middle years of an NIH mentored K award, participation in the program will not be limited to K awardees but will welcome qualified applicants who have achieved other sources of significant research support (e.g., R03s, R01s, institutional career development awards, foundation funding). More advanced scientists who wish a grounding in mixed methods may also apply.