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)
SELECTED
PAPERS
Framework of
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]
Direct
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.
Censoring
by death.
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 :
http://www.biostat.jhsph.edu/~cfrangak/papers/case_crossover/crossover01_16.pdf
Propensity scores.
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.)).