Roderick J. Little
Professor and Chair
Department of Biostatistics
School of Public Health
University of Michigan
Other Appointments:
Professor, Department of Statistics, University of Michigan
Senior Research Scientist, Institute for Social Research, University of Michigan
Vice President, Datametrics Research, Inc.
Telephone: 313-936-1003
Mailing Address:
Rod Little
Department of Biostatistics-SPH
1420 Washington Heights
Ann Arbor, MI 48109-2029
E-Mail: rlittle@umich.edu
Missing Data
Survey Sampling
Applications of Statistics
Missing Data
My main methodological research interest is the analysis of data with missing values. See for example Little and Rubin, (1987) or review articles (Little 1992, 1997; Little and Schenker 1994). Chen and Little (1999) discuss methods for analyzing survival data with missing covariates. For non-randomly missing data, Little (1993) discusses pattern-mixture models, a broad class of models that they do not require precise specification of the missing-data mechanism. Little and Wang (1996) extends the simple pattern-mixture model developed in Little (1994) to repeated-measures data with covariates. Little (1995) develops a model-based framework for repeated-measures data with drop-outs, and places existing literature within this framework. A paper with Linda Yau (Little and Yau 1996) develops a multiple imputation method for intent-to-treat analysis of repeated measures data with drop-outs. The SAS code used to generate the multiple imputes in the example in this paper can be accessed by clicking on Little and Yau intent-to-treat code. Ezzati-Rice et al. (1995) discusses a recent large-scale application of multiple imputation to a national survey.
References
Chen, H. Y. and Little, R.J.A. (1999). Proportional Hazards Regression with Missing Covariates. Journal of the American Statistical Association, 94, 896-908.
Ezzati-Rice,T., Johnson, W., Khare, M., Little, R., Rubin, D. and Schafer, J. (1995). A Simulation Study To Evaluate The Performance Of Model-Based Multiple Imputations In NCHS Health Examination Surveys. Invited Paper. Annual Research Conference, U.S. Bureau of the Census.
Little, R.J.A. (1992). Regression with missing X's: a review. Journal of the American Statistical Association, 87, 1227-1237.
Little, R.J.A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association, 88, 125-134.
Little, R.J.A. (1994). A class of pattern-mixture models for normal missing data. Biometrika 81, 3, 471-483.
Little, R.J.A. (1995). Modeling the Drop-Out Mechanism in Longitudinal Studies. Journal of the American Statistical Association, 90, 1112-1121.
Little, R.J.A. (1997). Biostatistical Analysis with Missing Data. Article for Encyclopedia of Biostatistics, P. Armitage and T.Colton, eds., Wiley: London.
Little, R.J.A. and Rubin, D.B. (1987). Statistical Analysis with Missing Data. New York: John Wiley.
Little, R.J.A., and Schenker, N. (1994) Missing data. In: Handbook for Statistical Modeling in the Social and Behavioral Sciences. G. Arminger, C.C. Clogg and M.E. Sobel, eds., pp. 39-75, Plenum, New York.
Little, R.J.A., and Wang, Y.-X. (1996) Pattern-mixture models for multivariate incomplete data with covariates. Biometrics , 52, 98-111.
Little, R.J.A. and Yau, L. (1996). Intent-to-Treat Analysis in Longitudinal Studies with Drop-Outs. Biometrics , 52, 1324-1333
Lazzeroni, L.C. and Little, R.J.A. (1998). Random-Effects Models for Smoothing Post-Stratification Weights. Journal of Official Statistics, 14, 1, 61-78.
Little, R.J.A. and Yau, L. (1998). Statistical Techniques for Analyzing Data from Prevention Trials: Treatment of No-Shows Using Rubin's Causal Model. Psychological Methods, 3, 2, 147-159.
Survey Sampling
My research in survey sampling focuses on model-based methods for complex survey designs that are robust to misspecification, and comparing the resulting inferences to classical methods based on the randomization distribution. Examples of this work include research on survey nonresponse (Little 1982, 1988), inference with survey weights (Little 1983, 1991; Little et al. 1997) and post-stratification (Little 1993; Lazzeroni and Little 1998).
References
Lazzeroni, L.C. and Little, R.J.A. (1998). Random-Effects Models for Smoothing Post-Stratification Weights. Journal of Official Statistics 14,1, 61-78.
Little, R.J.A. (1982). Models for nonresponse in sample surveys. Journal of the American Statistical Association, 77, 237-250.
Little, R.J.A. (1983). Estimating a finite population mean from unequal probability samples. Journal of the American Statistical Association, 78, 596-604.
Little, R.J.A. (1988). Missing data in large surveys. Journal of Business and Economic Statistics, 6, 287-301 (with discussion).
Little, R.J.A. (1991). Inference with survey weights. Journal of Official Statistics, 7, 405-424.
Little, R.J.A. (1993). Post-stratification: a modeler's perspective. Journal of the American Statistical Association 88, 1001-1012.
Applications of Statistics
My early collaborative research focused on the environmental statistics (e.g. Little 1982), demography, specifically my work for the World Fertility Survey (e.g. Hobcraft and Little 1984), and economics, in particular missing-data adjustments for economic surveys such as the Income Supplement of the Current Population Survey (Little 1985; David, Little, Samuhel and Triest 1986).
In 1982 Don Rubin and I formed a statistical consulting company, Datametrics Research Inc., which provides statistical consulting for Government Statistical Agencies and corporations.
While at UCLA I collaborated with Janet Sinsheimer and Jim Lake on Bayesian analyses of the structure of evolutionary trees using DNA sequences, based on the method of evolutionary parsimony and extensions (Sinsheimer, Lake and Little 1996, 1997). I have collaborated in psychiatric epidemiology studies with Ronald Kessler at the University of Michigan Institute for Social Research (Little et al. 1997). Current interests include research on Alzheimer's disease -- I am currently director of the Biostatistics Core of the University of Michigan Alzheimer's Disease Research Center -- and smoking cessation studies. I also collaborate with Richard Price at ISR on intervention studies concerned with job training. As part of that collaboration, Little and Yau (1998) describes an application of Angrist, Imbens and Rubin's (1996) methods for estimating the complier-average causal effect of interventions with non-compliance in the treatment arm. For SAS code, click on Little and Yau CACE code. Yau and Little (1998) extends this work to longitudinal data with missing values and treatment drop-outs. Finally, I collaborate with Maryfran Sowers on the SWAN study of women of menopausal age.
References
Angrist, J., Imbens, G.W., and Rubin, D.B. (1996). Identification of Causal Effects using Instrumental Variables, with discussion. Journal of the American Statistical Association, 91, 444-472.
David, M., Little, R.J.A., Samuhel, M.E. and Triest, R.K. (1986). Alternative methods for CPS income imputation. Journal of the American Statistical Association, 81, 29-41.
Hobcraft, J. and Little, R.J.A. (1984). Fertility exposure analysis: a new method for assessing the contribution of proximate determinants to fertility differentials. Population Studies, 38, 21-46.
Little, R.J.A. (1982). The statistical analysis of low-level radioactivity in the presence of background counts. Health Physics, 43, 693-703.
Little, R.J.A., Lewitzky, S. Heeringa, S., Lepkowski, J. and Kessler, R.C. (1997). An Assessment of Weighting Methodology for the National Comorbidity Study. American Journal of Epidemiology, 146, 439-449.
Little, R.J.A. and Yau, L. (1998). Statistical Techniques for Analyzing Data from Prevention Trials: Treatment of No-Shows Using Rubin's Causal Model. Psychological Methods, 3, 2, 147-159.
Sinsheimer, J., Lake, J. and Little, R.J.A. (1996). Bayesian hypothesis testing for four-taxon topologies using molecular sequence data. Biometrics, 52, 193-210.
Sinsheimer, J., Lake, J. and Little, R.J.A. (1997). Inference for Phylogenies Under a Hybrid Parsimony Method: Evolutionary/Symmetric Transversion Parsimony. Biometrics, 53, 23-38.
Yau, L. and Little, R.J.A. (1998). Inference for the Complier-Average Causal Effect from Longitudinal Data Subject to Noncompliance and Missing Data, with Application to a Job Training Assessment for the Unemployed. Submitted for publication .
Other Selected Publications
Beale, E.M.L. and R.J.A. Little (1975). Missing values in multivariate analysis. Journal of the Royal Statistical Society, Series B, 37, 129-145.
Little, R.J.A. (1978). Consistent regression methods for discriminant analysis with incomplete data. Journal of the American Statistical Association, 73, 319-322.
Little, R.J.A. (1985). A note about models for selectivity bias. Econometrica, 53, 1469-1474.
Little, R.J.A. and Schluchter, M.D. (1985). Maximum likelihood estimation for mixed continuous and categorical data with missing values. Biometrika, 72, 497-512.
Little, R.J.A. and Smith, P.J. (1987). Editing and imputation for quantitative data. Journal of the American Statistical Association, 82, 58-69.
Little, R.J.A. (1988b). Robust estimation of the mean and covariance matrix from data with missing values. Applied Statistics, 37, 23-38.
Lange, K., Little, R.J.A. and Taylor, J.M.G. (1989). Robust statistical inference using the t distribution. Journal of the American Statistical Association, 84, 881-896.
Date last revised: November 1999