STATISTICAL METHODS FOR PARTIALLY CONTROLLED STUDIES
Principal Investigator: Constantine E. Frangakis
Co-Investigator: Donald B. Rubin
Sponsoring Agency: National Eye Institute
This grant's objective is to
develop methods for evaluating treatments in studies with no direct control of
the treatments, but with control of other factors useful to assess those
treatments. Applications of
interest include evaluating needle exchange using distance, and
evaluating surrogate endpoints in ophthalmology and cancer trials.
PROGRESS REPORTS: 2003-2004 (doc)
principal stratification for causal inference in partially controlled studies:
Frangakis, CE, and Rubin, DB (2002) Principal stratification in causal inference. Biometrics, 58, 21-29.
School Choice Voucher
Evaluation using principal stratification:
Barnard, J, Frangakis, CE, Hill, JL, Rubin, DB. (2003). A principal stratification approach to broken randomized experiments: a case study of School Choice vouchers in
Evaluation of needle
exchange using principal stratification:
Frangakis, CE, Brookmeyer, RS, Varadhan, R, Mahboobeh, S, Vlahov, D, and Strathdee, SA. (2004). Methodology for evaluating a partially controlled longitudinal treatment using principal stratification, with application to a Needle Exchange Program. Forthcoming in the Journal of the American Statistical Association (with discussion). [ Full text - PDF]
and indirect effects.
Rubin (2004). Direct and Indirect Causal Effects Via Potential Outcomes. To appear in the Scandinavian Journal of Statistics, with discussion and reply.
Zhang, J. L. Rubin, D. B. (2003). Estimation of Causal Effects via Principal Stratification When Some Outcomes Are Truncated By ‘Death’. Journal of Educational and Behavioral Statistics, 28, 353-368.
Length Bias and Efficiency of case-crossover designs.
Varadhan, R, and Frangakis, C. E. (2004). Revealing and addressing length-bias and heterogeneous effects in frequency case-crossover studies. To appear in the American Journal of Epidemiology. Available at :
Rubin, D.B. (2003). Taking
causality seriously: propensity score methodology applied to estimate the
effects of marking interventions.
Machine Learning: ECMC 2003.
14th European Conference on Machine
Learning. (N. Lavrae,
D. Gamberger, H. Blockeel,
and L. Todorovski (eds.)).