A taxonomy of estimands for regulatory clinical trials with discontinuations

Statistics in Medicine

Volumn 35, Issue 17, 2016, Pages 2865-2875

  Permutt, Thomas ^a  

^a CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

Author keywords

benefit and risk; causal inference; missing data

Indexed keywords

CLINICAL TRIAL; DRUG WITHDRAWAL; GOVERNMENT; HUMAN; TAXONOMY;

EID: 84977567926     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6841     Document Type: Article

References (16)

1. National Research Council. The Prevention and Treatment of Missing Data in Clinical Trials. National Academies Press: Washington, 2010.
2. Neyman J. On the application of probability theory to agricultural experiments: essay on principles (with discussion): Section 9 (translated). Statistical Science. 1990(1923); 5: 465–480.
3. Fisher RA. Statistical Methods for Research Workers. Oliver & Boyd: London, 1925.
4. Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology. 1974; 66: 688–701.
5. Kenward MG. The handling of missing data in clinical trials. Clinical Investigation. 2013; 3(3):241–250.
6. Gould AL. A new approach to the analysis of clinical drug trials with withdrawals. Biometrics. 1980; 36(4):721–727.
7. Pincus T, Amara I, Koch GG. Continuous indices of core data set measures in rheumatoid arthritis clinical trials: lower responses to placebo than seen with categorical responses with the American College of rheumatology 20% criteria. Arthritis & Rheumatism. 2005; 52(4):1031–1036.
8. Brown MB. A test for the difference between two treatments in a continuous measure of outcome when there are dropouts. Controlled Clinical Trials. 1992; 13: 213–225.
9. Farrar JT, Dworkin RH, Max MB. Use of the cumulative proportion of responders analysis graph to present pain data over a range of cut-off points: making clinical trial data more understandable. Journal of Pain and Symptom Management. 2006; 31(4):369–377.
10. Federal Food, Drug and Cosmetic Act. Section 505(d).
11. Permutt T. A note on stratification in clinical trials. Drug Information Journal. 2007; 41(6):719–722.
12. Robins J, Richardson T, Spirtes P. On identification and inference for direct effects. In 563, Department of Statistics, University of Washington, 2009.
13. Robins J, Richardson T. Alternative graphical causal models and the identification of direct effects. In 100, Center for Statistics and the Social Sciences, University of Washington, 2010.
14. Hubbard AE, Jewell NP, van der Laan MJ. 2011. Direct effects and effects among the treated. In Targeted Learning: Causal Inference for Observational and Experimental Data, van der Laan MJ, Rose S (eds). Springer: New York; 133–144.
15. Zhou Y. Some Topics in missing data and adaptive confidence intervals. PhD Thesis, University of Michigan, 2009.
16. van der Laan MJ, Rose S. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer: New York, 2011.