Bounding the per-protocol effect in randomized trials: An application to colorectal cancer screening

Trials

Volumn 16, Issue 1, 2015, Pages

  Swanson, Sonja A ^a,b   Holme, Øyvind ^c,d   Løberg, Magnus ^c,f   Kalager, Mette ^b,c,e   Bretthauer, Michael ^b,c,f   Hoff, Geir ^c,e,g   Aas, Eline ^c   Hernán, Miguel A ^b,h  

^a ERASMUS MEDICAL CENTER   (Netherlands)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^c UNIVERSITY OF OSLO   (Norway)

^d Sorlandet Hospital Kristiansand   (Norway)

^e TELEMARK HOSPITAL   (Norway)

^f OSLO UNIVERSITY HOSPITAL   (Norway)

^g CANCER REGISTRY OF NORWAY   (Norway)

^h HARVARD MIT DIVISION OF HEALTH SCIENCES AND TECHNOLOGY   (United States)

Author keywords

Colorectal cancer; Instrumental variable; Partial identification; Per protocol effect; Screening

Indexed keywords

ADULT; ARTICLE; CANCER INCIDENCE; CANCER MORTALITY; CANCER PREVENTION; CANCER RISK; CANCER SCREENING; CAUSE OF DEATH; CLINICAL PROTOCOL; COLORECTAL CANCER; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; RANDOMIZED CONTROLLED TRIAL (TOPIC); RISK ASSESSMENT; SIGMOIDOSCOPY; COLORECTAL TUMOR; CONTROLLED STUDY; EARLY CANCER DIAGNOSIS; METHODOLOGY; MORTALITY; NORWAY; PATHOLOGY; PATIENT SELECTION; PREDICTIVE VALUE; PROCEDURES; PROGNOSIS; RANDOMIZED CONTROLLED TRIAL; RISK FACTOR; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA; TIME FACTOR;

CLINICAL PROTOCOLS; COLORECTAL NEOPLASMS; DATA INTERPRETATION, STATISTICAL; EARLY DETECTION OF CANCER; FEMALE; HUMANS; MALE; MIDDLE AGED; NORWAY; PATIENT SELECTION; PREDICTIVE VALUE OF TESTS; PROGNOSIS; RESEARCH DESIGN; RISK ASSESSMENT; RISK FACTORS; SIGMOIDOSCOPY; TIME FACTORS;

EID: 84955347036     PISSN: None     EISSN: 17456215     Source Type: Journal    
DOI: 10.1186/s13063-015-1056-8     Document Type: Article

References (28)

1. Hernán MA, Hernandez-Diaz S, Robins JM. Randomized trials analyzed as observational studies. Ann Intern Med. 2013;159(8):560-2. doi: 10.7326/0003-4819-159-8-201310150-00709.
2. Hernán MA, Hernandez-Diaz S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials (London, England). 2012;9(1):48-55. doi: 10.1177/1740774511420743.
3. Balke A, Pearl J. Bounds on treatment effects for studies with imperfect compliance. J Am Stat Assoc. 1997;92(439):1171-6.
4. Hernan MA, Hernandez-Diaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol. 2002;155(2):176-84.
5. Richardson T, Robins JM. Analysis of the binary instrumental variable model. In: Dechter R, Geffner H, Halpern JY, editors. Heuristics, probability, and causality: a tribute to Judea Pearl. London: College Publications; 2010: 415-44.
6. Robins JM. The analysis of randomized and nonrandomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In: Sechrest L, Freeman H, Mulley A, editors. Health service research methodology: a focus on AIDS. Washington, DC: US Public Health Service; 1989. p. 113-59.
7. Robins JM. Correcting for non-compliance in randomized trials using structural nested mean models. Commun Stat. 1994;23:2379-412.
8. Manski CF. Nonparametric bounds on treatment effects. Am Econ Rev. 1990;80(2):319-23
9. Bretthauer M, Gondal G, Larsen K, Carlsen E, Eide TJ, Grotmol T, et al. Design, organization and management of a controlled population screening study for detection of colorectal neoplasia: attendance rates in the NORCCAP study (Norwegian Colorectal Cancer Prevention). Scand J Gastroenterol. 2002;37(5):568-73.
10. Holme O, Loberg M, Kalager M, Bretthauer M, Hernán MA, Aas E et al. Colorectal cancer incidence and mortality after flexible sigmoidoscopy screening - First population-based randomized trial. JAMA. 2014;312(6):606-615
11. Hoff G, Grotmol T, Skovlund E, Bretthauer M. Norwegian Colorectal Cancer Prevention Study G. Risk of colorectal cancer seven years after flexible sigmoidoscopy screening: randomised controlled trial. BMJ. 2009;338:b1846. doi: 10.1136/bmj.b1846.
12. Richardson T, Robins JM. ACE bounds; SEMs with equilibrium conditions. Stat Sci. 2014;29(3):363-366.
13. Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002;58(1):21-9.
14. VanderWeele TJ. Principal stratification - uses and limitations. Int J Biostat. 2011;7(1):1-14.
15. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444-55.
16. Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Epidemiology (Cambridge, Mass). 2006;17(4):360-72. doi: 10.1097/01.ede.0000222409.00878.37.
17. Swanson SA, Hernán MA. Think globally, act globally: an epidemiologist's perspective on instrumental variable estimation. Stat Sci. 2014;29(3):371-4.
18. Pearl J. Imperfect experiments: bounding effects and counterfactuals. Causality. New York City: Cambridge University Press; 2009. p. 259-81.
19. Little RJ, D'Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, et al. The prevention and treatment of missing data in clinical trials. New Engl J Med. 2012;367(14):1355-60.
20. Robins JM. Structural nested failure time models. In: Anderson PK, Keiding N, editors. The encyclopedia of biostatistics. Chichester, UK: Wiley; 1998. p. 4372-89.
21. Tamer E. Partial identification in econometrics. Annu Rev Econ. 2010;2(1):167-95.
22. Chernozhukov V, Hong H, Tamer E. Estimation and confidence regions for parameter sets in econometric models. Econometrica. 2007;75(5):1243-84.
23. Horowitz JL, Manski CF. Nonparametric analysis of randomized experiments with missing covariate and outcome data. J Am Stat Assoc. 2000;95(449):77-84.
24. Manski CF, Sandefur GD, McLanahan S, Powers D. Alternative estimates of the effect of family structure during adolescence on high school graduation. J Am Stat Assoc. 1992;87(417):25-37.
25. Ramsahai R, Lauritzen S. Likelihood analysis of the binary instrumental variable model. Biometrika. 2011;98(4):987-994.
26. Romano JP, Shaikh AM. Inference for identifiable parameters in partially identified econometric models. J Stat Plann Infer. 2008;138(9):2786-807.
27. Imbens GW, Manski CF. Confidence intervals for partially identified parameters. Econometrica. 2004;72(6):1845-57.
28. Vansteelandt S, Goetghebeur E, Kenward MG, Molenberghs G. Ignorance and uncertainty regions as inferential tools in a sensitivity analysis. 2006;16(3):953-979.