Dr. Little chaired the Biostatistics Department from January 2007 to December 2009, and from 1993 to 2001. Prior to that he was Professor in the Department of Biomathematics at the University of California at Los Angeles; Research Fellow at the U.S. Bureau of the Census (1982-83); Expert Consultant at the United States Environmental Protection Agency; Scientific Associate at the World Fertility Survey; and Research Associate (Assistant Professor) in the Department of Statistics, University of Chicago. Active editorially, he was Coordinating and Applications Editor of the Journal of the American Statistical Association from 1992-1994. From Jan 2010-Dec 2012 he was a Vice President of the American Statistical Association. Since his fellowship at the Census Bureau he has been interested in federal statistical issues such as the census undercount, and he has served as a member of the Committee on National Statistics and a number of other National Research Council committees. He recently chaired an NRC study on the prevention and treatment of missing data in clinical trials. An ISI highly cited researcher, he has over 180 refereed publications, notably on methods for the analysis of data with missing values and model-based survey inference, and the application of statistics to diverse scientific areas, including medicine, demography, economics, psychiatry, aging and the environment. He has chaired or co-chaired 26 doctoral committees. In 2005 Dr. Little received the Wilks' Memorial Award from the American Statistical Association for his research contributions. From Sep 2010- Jan 2013 Professor Little served as the inaugural Associate Director for Research and Methodology, and Chief Scientist at the U.S. Census Bureau.
BIOSTAT880: Statistical Analysis With Missing Data
Ph.D., Statistics, London University, 1974
M.Sc., Statistics and Operational Research, London University, 1972
B.A., Mathematics, Cambridge University, 1971
Research Interests & Projects
A primary research interest is the analysis of data sets with missing values. Many statistical techniques are designed for complete, rectangular data sets, but in practice biostatistical data sets contain missing values, either by design or accident. As detailed in my book with Rubin, initial statistical approaches were relatively ad-hoc, such as discarding incomplete cases or substituting means, but modern methods are increasingly based on models for the data and missing-data mechanism, using likelihood-based inferential techniques.
Another interest is the analysis of data collected by complex sampling designs involving stratification and clustering of units. Since working as a statistician for the World Fertility Survey, I have been interested in the development of model-based methods for survey analysis that are robust to misspecification, reasonably efficient, and capable of implementation in applied settings. Statistics is philosophically fascinating and diverse in application. My inferential philosophy is model-based and Bayesian, although the effects of model misspecification need careful attention. My applied interests are broad, including mental health, demography, environmental statistics, biology, economics and the social sciences as well as biostatistics.
Search PubMed for publications by Roderick Little >>
Little, R.J. (2013). In Praise of Simplicity, Not Mathematistry! Simple, Powerful Ideas for the Applied Statistician. Journal of the American Statistical Association, 108, 359-370.
Little, R.J., D’Agostino, R., Cohen, M.L., Dickersin, K., Emerson, S.S., Farrar, J.T., Frangakis, C., Hogan, J.W., Molenberghs, G., Murphy, S.A., Rotnitsky, A., Scharfstein, D., Neaton, J.D., Shih, W., Siegel, J.P., Stern, H. (2012). Special Report: The Prevention and Treatment of Missing Data in Clinical Trials. New England Journal of Medicine, 367(14), 1355-1360.
Little, R.J. (2012). Calibrated Bayes: an Alternative Inferential Paradigm for Official Statistics (with discussion and rejoinder) Journal of Official Statistics, 28(3), 309-372.
Little, R.J. & Zhang, N. (2011). Subsample Ignorable Likelihood for Regression Analysis with Missing Data. Journal of the Royal Statistical Society, Ser. C: Applied Statistics, 60, 591–605.
Little, R.J., Yosef, M., Nan, B., & Harlow, S. (2011). A method for the longitudinal prospective evaluation of markers of a subsequent event. (With discussion and rejoinder) American Journal of Epidemiology, 173(12), 1380-1387.
Chen, Q., Elliott, M.R., Little, R.J. (2010). Bayesian Penalized Spline Model-Based Estimation of the Finite Population Proportion for Probability-Proportional-to-Size Samples Survey Methodology, 36, 23-34.
Guo, Y., Harel, O., Little, R.J. (2009). How well quantified is the limit of quantification? Epidemiology, 21, 4, S10-16.
Long, Q., Little, R.J., Lin, X. (2007). Causal Inference in Hybrid Intervention Studies Involving Treatment Choice Journal of the American Statistical Association, 103, 474-484.
Little, R.J.A. (2004). To Model or Not to Model? Competing Modes of Inference for Finite Population Sampling. Journal of the American Statistical Association, 99, 546-556.
Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing Data, 2nd Edition.
For more on publications see Dr. Little's google scholar page:
1985 - Present: Fellow, American Statistical Association
Member, International Biometrics Society
Fellow, Royal Statistical Society
Member, International Statistical Institute
2010 - Present: Fellow, American Academy of Arts and Sciences
2011 - Present: Member, Institute of Medicine of the National Academy of Sciences