Basing intention-to-treat on cause and effect criteria

Drug Information Journal

Volumn 38, Issue 4, 2004, Pages 361-369

  Stewart, William H ^a  

^a IQVIA   (United States)

Author keywords

Cause and effect; Counterfactual; Finite population; Full analysis set; Imputation; Intention to treat; Per protocol; Randomization

Indexed keywords

CLINICAL TRIAL; DATA ANALYSIS; DRUG RESEARCH; HEALTH BEHAVIOR; MEASUREMENT; POPULATION; PRIORITY JOURNAL; RANDOMIZATION; REVIEW; STATISTICAL ANALYSIS; STATISTICAL PARAMETERS;

EID: 8644261017     PISSN: 00928615     EISSN: None     Source Type: Journal    
DOI: 10.1177/009286150403800407     Document Type: Review

References (11)

1. Peto R, Pike MC, Armitage P, Breslow NE, Cox DR, Howard SV, Mantel N, McPherson K, Peto J, Smith PG. Design and analysis of randomized clinical trials requiring prolonged observation of each patient. Br J Cancer. 1976;34:585-612.
2. Armitage P. The analysis of data from clinical trials. Statistician. 1979;179-183.
3. Armitage P. Exclusions, losses to follow-up, and withdrawals in clinical trials. In: Shapiro SH and Louis TA, ed. Clinical Trials. New York, NY: Marcel Dekker, Inc.; 1983:99-113.
4. Gail MH. Eligibility exclusions, losses to follow-up, removal of randomized patients, and uncounted events in cancer clinical trials. Cancer Treat Rep. 1985;69:1107-1113.
5. Fisher LD, Dixon LO, Herson J, Frankowski RK, Hearron MS, Peace KE. Intention to treat in clinical trials. In: Peace KE, ed. Statistical issues in Drug Research and Development. New York, NY: Marcel Dekker, Inc.; 1990.
6. International Conference on Harmonisation; Guidance on statistical principles for clinical trials. Federal Register. 1998;69:49583-49598.
7. Phillips A, Ebbutt A, France L, Morgan D. The International Conference on Harmonisation guideline "Statistical Principles for Clinical Trials": Issues in applying the guideline in practice. Drug Inf J. 2000;34:337-348.
8. Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Education Psychol. 1974;66:688-701.
9. Rubin DB. Bayesian inference for causal effects: the role of randomization. Ann Stat. 1978;6:34-58.
10. Ludbrook J, Dudley H. Why permutation tests are superior to t and F tests in biomedical research. Am Statistician. 1998;52:127-132.
11. Stewart WH. Groundhog day: cause and effect and the primary importance of the finite population induced by randomization. J Biopharmaceutical Stat. 2002;12:93-105.