This application seeks support for a team of statisticians and mental health scientists to collaborate on the development and validation of regression methods for multiple outcomes collected in longitudinal studies of mental health services. The specific methods to be developed will address the needs of ongoing health services research but also will have application in the basic mental health disciplines such as genetics, clinical trials and epidemiology. The methodologic advances from this research will enable mental health scientists in these disciplines to more efficiently investigate mental disorders and their patterns of care. The three specific aims are:1. To develop, validate, and disseminate new regression methods for multiple outcome measurements collected in longitudinal studies. The specific methods to be developed are for: multiple outcomes observed repeatedly through time; multiple survival (time-to-event) outcomes; and combinations of longitudinal and survival data.2. To compare the application of new and existing methods to mental health services data sets to assess their relative advantages and disadvantages and to disseminate our findings. 3. To develop stand- alone statistical software for new and existing methods unifying survival and longitudinal data analyses and also to integrate this software in Splus, a widely available statistical package with bridges to other popular software including SAS and SPSS. The co- investigators will work as a team that will: analyze diverse health services data sets using existing methods; identify and disseminate common methodologic barriers to scientific inferences; propose improved statistical methods; investigate their theoretical properties; implement the new procedures in computer software; compare the new and existing methods through analyses of our data sets; and disseminate the results to both the mental health services and statistical communities. The new methodologies can contribute to our understanding of mental disorders and patterns of care and ultimately to the improvement of mental health status.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH056639-03
Application #
2890899
Study Section
Services Research Review Committee (SER)
Program Officer
Hohmann, Ann A
Project Start
1997-08-15
Project End
2002-07-31
Budget Start
1999-08-01
Budget End
2000-07-31
Support Year
3
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Biochemistry
Type
Schools of Public Health
DUNS #
045911138
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Shardell, Michelle; Scharfstein, Daniel O; Vlahov, David et al. (2008) Sensitivity analysis using elicited expert information for inference with coarsened data: illustration of censored discrete event times in the AIDS Link to Intravenous Experience (ALIVE) Study. Am J Epidemiol 168:1460-9
Shardell, Michelle; Scharfstein, Daniel O; Vlahov, David et al. (2008) Inference for cumulative incidence functions with informatively coarsened discrete event-time data. Stat Med 27:5861-79
Shardell, Michelle; Scharfstein, Daniel O; Bozzette, Samuel A (2007) Survival curve estimation for informatively coarsened discrete event-time data. Stat Med 26:2184-202
Dominici, Francesca; Zeger, Scott L (2005) Smooth quantile ratio estimation with regression: estimating medical expenditures for smoking-attributable diseases. Biostatistics 6:505-19
Chen, Ying Qing; Wang, Mei-Cheng; Huang, Yijian (2004) Semiparametric regression analysis on longitudinal pattern of recurrent gap times. Biostatistics 5:277-90
Scharfstein, Daniel O; Manski, Charles F; Anthony, James C (2004) On the construction of bounds in prospective studies with missing ordinal outcomes: application to the good behavior game trial. Biometrics 60:154-64
Scharfstein, Daniel O; Daniels, Michael J; Robins, James M (2003) Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes. Biostatistics 4:495-512
Ziai, Wendy C; Varelas, Panayiotis N; Zeger, Scott L et al. (2003) Neurologic intensive care resource use after brain tumor surgery: an analysis of indications and alternative strategies. Crit Care Med 31:2782-7
Xue, Qian-Li; Bandeen-Roche, Karen (2002) Combining complete multivariate outcomes with incomplete covariate information: a latent class approach. Biometrics 58:110-20
Garrett, Elizabeth S; Eaton, William W; Zeger, Scott (2002) Methods for evaluating the performance of diagnostic tests in the absence of a gold standard: a latent class model approach. Stat Med 21:1289-307

Showing the most recent 10 out of 21 publications