Abstract

The Methodology Core will provide project specific biostatistical and design consultation at the predesign phase, analysis and consultation during implementation, and Center-wide education. Dr. Mary Amanda Dew will direct the Core. Drs. Mazumdar, Professor of Biostatistics (20 percent) and Pilkonis (7 percent) will bring their own statistical and psychometric expertise to the Center. Two half-time masters level statisticians and a recent Ph.D. graduate (Dr. Kim) will staff this Core. The unit consists of experienced and well respected faculty who are knowledgeable about psychiatric research and Center operations. This Core?s section of the application outlines several objectives within the Center. Objectives include providing statistical consultation and advice on data analysis, developing new statistical methodologies, and taking a strong educational role. The Core also works with investigators from the very beginning of research projects and offers guidance on study design. The faculty will have a research agenda to support the IRC overall objectives. This Core is also responsible for training IRC investigators and fellows in statistical methods and research design.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Center Core Grants (P30)
Project #
3P30MH052247-08S1
Application #
6662178
Study Section
Special Emphasis Panel (ZMH1)
Project Start
2002-03-01
Project End
2003-02-28
Budget Start
Budget End
Support Year
8
Fiscal Year
2002
Total Cost
Indirect Cost

  Institution

Name
University of Pittsburgh
Department
Type
DUNS #
053785812
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213

  Publications

Smagula, Stephen F; Beach, Scott; Rosso, Andrea L et al. (2017) Brain Structural Markers and Caregiving Characteristics as Interacting Correlates of Caregiving Strain. Am J Geriatr Psychiatry 25:582-591
Smagula, Stephen F; Wallace, Meredith L; Anderson, Stewart J et al. (2016) Combining moderators to identify clinical profiles of patients who will, and will not, benefit from aripiprazole augmentation for treatment resistant late-life major depressive disorder. J Psychiatr Res 81:112-8
Khalaf, Alexander; Edelman, Kathryn; Tudorascu, Dana et al. (2015) White Matter Hyperintensity Accumulation During Treatment of Late-Life Depression. Neuropsychopharmacology 40:3027-35
Patel, Meenal J; Andreescu, Carmen; Price, Julie C et al. (2015) Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry 30:1056-67
Joel, Ian; Begley, Amy E; Mulsant, Benoit H et al. (2014) Dynamic prediction of treatment response in late-life depression. Am J Geriatr Psychiatry 22:167-76
Diniz, Breno S; Reynolds 3rd, Charles F; Butters, Meryl A et al. (2014) The effect of gender, age, and symptom severity in late-life depression on the risk of all-cause mortality: the Bambuí Cohort Study of Aging. Depress Anxiety 31:787-95
Andreescu, Carmen; Teverovsky, Esther; Fu, Bo et al. (2014) Old worries and new anxieties: behavioral symptoms and mild cognitive impairment in a population study. Am J Geriatr Psychiatry 22:274-84
Chisholm, Denise; Toto, Pamela; Raina, Ketki et al. (2014) Evaluating capacity to live independently and safely in the community: Performance Assessment of Self-care Skills. Br J Occup Ther 77:59-63
Gallo, Joseph J; Morales, Knashawn H; Bogner, Hillary R et al. (2013) Long term effect of depression care management on mortality in older adults: follow-up of cluster randomized clinical trial in primary care. BMJ 346:f2570
Sherer, Eric A; Sale, Mark E; Pollock, Bruce G et al. (2012) Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building. J Pharmacokinet Pharmacodyn 39:393-414

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