Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome: An Alternative Approach

Biometrics

Volumn 67, Issue 1, 2011, Pages 319-323

  Baker, Stuart G ^a  

^a NATIONAL CANCER INSTITUTE   (United States)

Author keywords

All or none compliance; Causal effect; Multinomial outcomes; Noncompliance; Perfect fit; Principal stratification; Randomized trials

Indexed keywords

BIOMETRICS; CONVEX OPTIMIZATION; IMAGE SEGMENTATION; MAXIMUM PRINCIPLE; RANDOM PROCESSES;

ALL-OR-NONE COMPLIANCE; CAUSAL EFFECT; MAXIMUM-LIKELIHOOD ESTIMATION; MULTINOMIAL OUTCOME; MULTINOMIALS; NONCOMPLIANCE; PERFECT FIT; PRINCIPAL STRATIFICATION; RANDOMISATION; RANDOMIZED TRIAL;

MAXIMUM LIKELIHOOD ESTIMATION;

ALGORITHM; COMPLIANCE; ESTIMATION METHOD; MAXIMUM LIKELIHOOD ANALYSIS; NUMERICAL MODEL; RANDOM WALK METHOD;

EID: 79952591546     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2010.01451_1.x     Document Type: Article

References (16)

1. Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91, 444-455.
2. Baker, S. G. (1994). The Multinomial-Poisson transformation. The Statistician 43, 495-504.
3. Baker, S. G. (1997). Compliance, all-or-none. In The Encyclopedia of Statistical Science, Update Volume, S. Kotz, C. R. Read, and D. L. Banks (eds), 134-138. New York John Wiley and Sons, Inc.
4. Baker, S. G. and Kramer, B. S. (2005). Simple maximum likelihood estimates of efficacy in randomized trials and before-and-after studies, with implications for meta-analysis. Statistical Methods in Medical Research 14, 1-19, correction 14, 349.
5. Baker, S. G. and Lindeman, K. S. (1994). The paired availability design: A proposal for evaluating epidural analgesia during labor. Statistics in Medicine 13, 2269-2278, correction 14, 1841.
6. Bloom, H. S. (1984). Accounting for no-shows in experimental evaluation designs. Evaluation Review 8, 225-246.
7. Cheng, J. (2009). Estimation and inference for the causal effect of receiving treatment on a multinomial outcome. Biometrics 65, 96-103.
8. Cheng, J., Small, D. S., Tan, Z., and Ten Have, T. R. (2009). Efficient nonparametric estimation of causal effects in randomized trials with noncompliance. Biometrika 96, 19-36.
9. Connor, R., Prorok, P. C., and Weed, D. L. (1991). The case-control design and the assessment of the efficacy of cancer screening. Journal of Clinical Epidemiology 44, 1215-1221.
10. Frangakis, C. E. and Rubin, D. B. (2002). Principal stratification in causal inference. Biometrics 58, 21-29.
11. Imbens, G. W. and Rubin, D. B. (1997). Estimating outcome distributions for compliers in instrumental variables models. Review of Economic Studies 64, 555-574.
12. Peng, R. D. (2009). Reproducible research and biostatistics. Biostatistics 10, 405-408.
13. Rohatgi, V. K. (1976). An Introduction to Probability Theory and Mathematical Statistics. New York John Wiley and Sons, Inc.
14. Rubin, D. B. (1980). Comment on "Randomization analysis of experimental data: The Fisher randomization test" by D. Basau. Journal of the American Statistical Association 75, 591-593.
15. Sommer, A. and Zeger, S. L. (1991). On estimating efficacy from clinical trials. Statistics in Medicine 10, 45-52.
16. Wolfram Research, Inc. (2008). Mathematica, Version 7.0. Champaign, Illinois Wolfram Research, Inc.