Manisha Desai, PhD
Section Chief of Biostatistics
Director of the Quantitative Sciences Unit

Dr. Desai is Professor of Medicine, Professor of Biomedical Data Science, Professor of Epidemiology and Population Health; and Associate Dean of Research at Stanford. She joined Stanford in 2009 after spending 9 years on the faculty of the Department of Biostatistics at Columbia University. At Stanford she serves as the founding Director of the Quantitative Sciences Unit (QSU), a data science unit consisting of faculty and staff who practice data science to address critical biomedical questions. Now with over 30 faculty-, Ph.D.-, and Master’s-level members, the QSU serves as the foundation for the Biostatistics Shared Resource for the Stanford Cancer Institute as well as for the Biostatistics Epidemiology and Research Design (BERD) Program of the Clinical and Translational Science Award (CTSA), which Dr. Desai also leads. She recently served as PI of the Data Coordinating Center for the Apple Heart Study, a pragmatic, siteless trial that enrolled over 400,000 participants, in which the goal was to characterize performance of an app to identify atrial fibrillation in the general population. She is also PI of a Data Coordinating Center for a large multi-center trial investigating a therapy for the hospitalized pneumonia patient (ARREST Trial), and of an R01 to develop new methods for the analysis of high dimensional accelerometer data. Her methodological areas of interest include the handling of missing data, translation of trial findings to real-world target populations; and integration of real-world data, like mobile health and electronic health records data, into clinical trials.

Stanford CAP Profile


Methodology Area of Interest:  missing data, correlated data, longitudinal data analysis, design of clinical trials, and the processing and analysis of accelerometry studies. 

Clinical Area of Interest: oncology, physical activity, immunology and cardiovascular disease

Selected Publications: 

Desai M, Emond MJ.  A new mixture model approach to analyzing allelic-loss data using Bayes factors.  BMC Bioinformatics 2004 Nov 24;5:182. 

Desai M, Begg MD.  A comparison of regression approaches for analyzing clustered data.  Am J Public Health  2008 Aug; 98(8):1425-9.  

Desai M, Kubo J, Esserman D, Terry MB.  The handling of missing data in molecular epidemiology studies.  Cancer Epidemiol Biomarkers Prev 2011 Aug; 20(8).

Desai M, Bryson SW, Robinson T.  On the use of robust estimators for standard errors in the presence of clustering when clustering membership is misspecified. Contemp Clin Trials 2013; 34(2):248-56.

Bavinger C, Bendavid E, Niehaus K, Olshen RA, Olkin I, Sundaram V, Wein N, Holodniy M, Hou N, Owens DK, Desai M.  Risk of cardiovascular disease from antiretroviral therapy for HIV: a systematic review. PLoS ONE2013;  8(3):e59551.  PMCID:PMC3608726

Montez-Rath ME, Winkelmayer WC, Desai M.  Addressing missing data in clinical studies of kidney disease.  Clin J Am Soc Nephrol. 2014 Feb 7. 

Desai M, Pieper K, Mahaffey K. Challenges and Solutions to Pre- and Post-Randomization Subgroup Analyses. Current Cardiology Reports.  Curr Cardiol Rep.  2014 Oct;16(10):531.

Desai M, Joyce V, Bendavid E, Olshen RA, Hlatky M, Chow A, Holodniy M, Barnett P, Owens DK. Risk of cardiovascular events associated with current exposure to HIV antiretroviral therapies in a US veteran population. Clin Infect Dis. 2015 Aug 1;61(3):445-52.

Desai M, Mitani A, Bryson SW, Robinson T. Multiple imputation when rate of change is the outcome of interest.  Journal of Modern Applied Statistical Methods. In Press.  

Mitani A, Kurian A, Das A, Desai M.  Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects.  Statistical Methodology.  In Press.