Choosing sensitivity analyses for randomised trials: principles

BMC Medical Research Methodology

Ian R White³  Brennan C Kahan¹  Tim P Morris² 

[1] Pragmatic Clinical Trials Unit, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK;Hub for Trials Methodology Research, MRC Clinical Trials Unit at UCL, Aviation House, 125 Kingsway, London WC2B 6NH, UK;MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK

关键词: Missing data;    RCT;    Clinical trials;    Randomised trials;    Sensitivity analysis;   

Others  :  866486
DOI  :  10.1186/1471-2288-14-11

 received in 2013-10-10, accepted in 2014-01-21,  发布年份 2014

【 摘 要 】

Background

Sensitivity analyses are an important tool for understanding the extent to which the results of randomised trials depend upon the assumptions of the analysis. There is currently no guidance governing the choice of sensitivity analyses.

Discussion

We provide a principled approach to choosing sensitivity analyses through the consideration of the following questions: 1) Does the proposed sensitivity analysis address the same question as the primary analysis? 2) Is it possible for the proposed sensitivity analysis to return a different result to the primary analysis? 3) If the results do differ, is there any uncertainty as to which will be believed? Answering all of these questions in the affirmative will help researchers to identify relevant sensitivity analyses. Treating analyses as sensitivity analyses when one or more of the answers are negative can be misleading and confuse the interpretation of studies. The value of these questions is illustrated with several examples.

Summary

By removing unreasonable analyses that might have been performed, these questions will lead to relevant sensitivity analyses, which help to assess the robustness of trial results.

【 授权许可】

   
2014 Morris et al.; licensee BioMed Central Ltd.

【 图 表 】

【 参考文献 】

• [1]Freeman PR: The performance of the two-stage analysis of two-treatment, two-period crossover trials. Stat Med 1989, 8(12):1421-1432.
• [2]Raab GM, Day S, Sales J: How to select covariates to include in the analysis of a clinical trial. Control Clin Trials 2000, 21:330-342.
• [3]Campbell H, Dean CB: The consequences of proportional hazards based model selection. Stat Med 2013.
• [4]Kahan BC: Bias in randomised factorial trials. Stat Med 2013, 32(26):4540-4549.
• [5]Thabane L, Mbuagbaw L, Zhang S, Samaan Z, Marcucci M, Ye C, Thabane M, Giangregorio L, Dennis B, Kosa D, Debono V, Dillenburg R, Fruci V, Bawor M, Lee J, Wells G, Goldsmith C: A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med Res Methodol 2013, 1(13):92.
• [6]Ashton HA, Buxton MJ, Day NE, Kim LG, Marteau TM, Scott RA, Thompson SG, Walker NM: Multicentre aneurysm screening study group: the Multicentre Aneurysm Screening Study (MASS) into the effect of abdominal aortic aneurysm screening on mortality in men: a randomised controlled trial. Lancet 2002, 360(9345):1531-1539.
• [7]Imbens GW, Rubin DB: Bayesian inference for causal effects in randomized experiments with noncompliance. Ann Stat 1997, 25(1):305-327.
• [8]Bendtsen P, McCambridge J, Bendtsen M, Karlsson N, Nilsen P: Effectiveness of a proactive mail-based alcohol internet intervention for university students: dismantling the assessment and feedback components in a randomized controlled trial. J Med Internet Res 2012, 14(5):142.
• [9]Ghofrani HA, Galiè N, Grimminger F, Grünig E, Humbert M, Jing ZC, Keogh AM, Langleben D, Kilama MO, Fritsch A, Neuser D, Rubin LJ: Riociguat for the treatment of pulmonary arterial hypertension. N Engl J Med 2013, 369(4):330-340.
• [10]White IR: Uses and limitations of randomization-based efficacy estimators. Stat Methods Med Res 2005, 14(4):327-347.
• [11]Zheng G, Li M, Lan X, Yan X, Lin Q, Chen L, Tao J, Zheng X, Li J, Chen B, Fang Q: The effect of baduanjin exercise for physical and psychological wellbeing of college students: study protocol for a randomized controlled trial. Trials 2013, 14(1):422. BioMed Central Full Text
• [12]Schafer JL: Multiple imputation: a primer. Stat Methods Med Res 1999, 8(1):3-15.
• [13]Kahan BC, Morris TP: Assessing potential sources of clustering in individually randomised trials. BMC Med Res Methodol 2013, 13(1):58. BioMed Central Full Text
• [14]Hu FB, Goldberg J, Hedeker D, Flay BR, Pentz MA: Comparison of population-averaged and subject-specific approaches for analyzing repeated binary outcomes. Am J Epidemiol 1998, 147(7):694-703.
• [15]Perkins GD, Woollard M, Cooke MW, Deakin C, Horton J, Lall R, Lamb SE, McCabe C, Quinn T, Slowther A, Gates S, PARAMEDIC trial collaborators: Prehospital randomised assessment of a mechanical compression device in cardiac arrest (paramedic) trial protocol. Scand J Trauma Resusc Emerg Med 2010, 18:58. BioMed Central Full Text
• [16]White IR, Horton NJ, Carpenter J, Pocock SJ: Strategy for intention to treat analysis in randomised trials with missing outcome data. BMJ 2011, 342:910-912.
• [17]de Pauw BE, Sable CA, Walsh TJ, Lupinacci RJ, Bourque MR, Wise BA, Nguyen B-YY, DiNubile MJ, Teppler H: Impact of alternate definitions of fever resolution on the composite endpoint in clinical trials of empirical antifungal therapy for neutropenic patients with persistent fever: analysis of results from the caspofungin empirical therapy study. Transpl Infect Dis: Official J Transpl Soc 2006, 8(1):31-37.
• [18]Carnes D, Taylor SJC, Homer K, Eldridge S, Bremner S, Pincus T, Rahman A, Underwood M: Effectiveness and cost-effectiveness of a novel, group self-management course for adults with chronic musculoskeletal pain: study protocol for a multicentre, randomised controlled trial (copers). BMJ Open 2013, 3(1):002492.