Should observational studies be designed to allow lack of balance in covariate distributions across treatment groups?

Statistics in Medicine

Volumn 28, Issue 9, 2009, Pages 1420-1423

  Rubin, Donald B ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS OF COVARIANCE; BAYES THEOREM; COVARIANCE; DATA ANALYSIS; EXPERIMENTAL DESIGN; LETTER; MATHEMATICAL COMPUTING; OBSERVATIONAL STUDY; STATISTICAL ANALYSIS; STATISTICAL DISTRIBUTION; STATISTICAL SIGNIFICANCE; THEORETICAL SAMPLE;

EID: 69949106881     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3565     Document Type: Letter

References (12)

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10. Raghunathan TE. Nonparametric analysis of randomized experiments with missing covariate and outcome data: comment. Journal of the American Statistical Association 2000; 95(449):87.
11. Rubin DB. Direct and indirect causal effects via potential outcomes. The Scandinavian Journal of Statistics 2004; 31:161-170; 196-198. With discussion and reply.
12. Rubin DB. Author's reply to letter by I. Shrier re: The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials 2007; Statistics in Medicine 2008; 27:2741-2742.