A tutorial on sensitivity analyses in clinical trials: The what, why, when and how

BMC Medical Research Methodology

Volumn 13, Issue 1, 2013, Pages

  Thabane, Lehana ^a,b,c,d   Mbuagbaw, Lawrence ^a,c   Zhang, Shiyuan ^a,c   Samaan, Zainab ^a,e   Marcucci, Maura ^a,c   Ye, Chenglin ^a,c   Thabane, Marroon ^a,f   Giangregorio, Lora ^g   Dennis, Brittany ^a,c   Kosa, Daisy ^a,c,h   Debono, Victoria Borg ^a,c   Dillenburg, Rejane ^a   Fruci, Vincent ⁱ   Bawor, Monica ^a   Lee, Juneyoung ^j   Wells, George ^k   Goldsmith, Charles H ^a,c,l  

^a MCMASTER UNIVERSITY   (Canada)

^b Center for Evaluation of Medicines   (Canada)

^c MCMASTER UNIVERSITY   (Canada)

^d MCMASTER UNIVERSITY   (Canada)

^e MCMASTER UNIVERSITY   (Canada)

^f GLAXOSMITHKLINE   (Canada)

^g UNIVERSITY OF WATERLOO   (Canada)

^h TORONTO GENERAL HOSPITAL   (Canada)

ⁱ MCMASTER UNIVERSITY   (Canada)

^j Korea University   (South Korea)

^k UNIVERSITY OF OTTAWA   (Canada)

^l SIMON FRASER UNIVERSITY   (Canada)

more..

Author keywords

Clinical trials; Robustness; Sensitivity analysis

Indexed keywords

ARTICLE; CLINICAL TRIAL (TOPIC); HUMAN; METHODOLOGY; SENSITIVITY AND SPECIFICITY; STANDARD; TREATMENT OUTCOME;

CLINICAL TRIALS AS TOPIC; HUMANS; OUTCOME AND PROCESS ASSESSMENT (HEALTH CARE); RESEARCH DESIGN; SENSITIVITY AND SPECIFICITY;

EID: 84880099876     PISSN: None     EISSN: 14712288     Source Type: Journal    
DOI: 10.1186/1471-2288-13-92     Document Type: Review

References (62)

1. A tutorial on pilot studies: the what, why and how. Thabane L, Ma J, Chu R, Cheng J, Ismaila A, Rios LP, Robson R, Thabane M, Giangregorio L, Goldsmith CH, BMC Med Res Methodol 2010 10 1 10.1186/1471-2288-10-1 20053272
2. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Schneeweiss S, Pharmacoepidemiol Drug Saf 2006 15 5 291 303 10.1002/pds.1200 16447304
3. Incidence of leukaemia in young people around the La Hague nuclear waste reprocessing plant: a sensitivity analysis. Viel JF, Pobel D, Carre A, Stat Med 1995 14 21-22 2459 2472 8711281
4. Goldsmith CH, Gafni A, Drummond MF, Torrance GW, Stoddart GL, Sensitivity Analysis and Experimental Design: The Case of Economic Evaluation of Health Care Programmes. Proceedings of the Third Canadian Conference on Health Economics 1986 Winnipeg MB: The University of Manitoba Press 1987
5. Saltelli A, Tarantola S, Campolongo F, Ratto M, Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models New York, NY: Willey 2004
6. Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S, Global Sensitivity Analysis: The Primer New York, NY: Wiley-Interscience 2008
7. Hunink MGM, Glasziou PP, Siegel JE, Weeks JC, Pliskin JS, Elstein AS, Weinstein MC, Decision Making in Health and Medicine: Integrating Evidence and Values. Cambridge: Cambridge University Press 2001
8. International Conference on Harmonisation; Guidance on Statistical Principles for Clinical Trials. Guideline E9. Statistical principles for clinical trials. Federal Register, 16 September 1998, Vol. 63, No. 179, p. 49583. USFDA, [ http://www.fda.gov/downloads/RegulatoryInformation/Guidances/ UCM129505.pdf ]
9. Guide to the methods of technology appraisal. NICE, [ http://www.nice.org.uk/media/b52/a7/tamethodsguideupdatedjune2008.pdf ]
10. Comparison of Bayesian and classical methods in the analysis of cluster randomized controlled trials with a binary outcome: the Community Hypertension Assessment Trial (CHAT). Ma J, Thabane L, Kaczorowski J, Chambers L, Dolovich L, Karwalajtys T, Levitt C, BMC Med Res Methodol 2009 9 37 10.1186/1471-2288-9-37 19531226
11. Comparison of methods for analysing cluster randomized trials: an example involving a factorial design. Peters TJ, Richards SH, Bankhead CR, Ades AE, Sterne JA, Int J Epidemiol 2003 32 5 840 846 10.1093/ije/dyg228 14559762
12. Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: a simulation study. Chu R, Thabane L, Ma J, Holbrook A, Pullenayegum E, Devereaux PJ, BMC Med Res Methodol 2011 11 21 10.1186/1471-2288-11-21 21338524
13. Kleinbaum DG, Klein M, Survival Analysis-A-Self Learning Text Springer 3 2012 23875173
14. Barnett V, Lewis T, Outliers in Statistical Data John Wiley & Sons 3 1994
15. Procedures for detecting outlying observations in samples. Grubbs FE, Technometrics 1969 11 1 21 10.1080/00401706.1969.10490657
16. Guidelines for reporting descriptive statistics in health research. Thabane L, Akhtar-Danesh N, Nurse Res 2008 15 2 72 81 18459488
17. Cost-utility analysis of osteopathy in primary care: results from a pragmatic randomized controlled trial. Williams NH, Edwards RT, Linck P, Muntz R, Hibbs R, Wilkinson C, Russell I, Russell D, Hounsome B, Fam Pract 2004 21 6 643 650 10.1093/fampra/cmh612 15531626
18. Evaluating the Angina Plan in Patients Admitted to Hospital with Angina: A Randomized Controlled Trial. Zetta S, Smith K, Jones M, Allcoat P, Sullivan F, Cardiovascular Therapeutics 2011 29 2 112 124 10.1111/j.1755-5922.2009.00109.x
19. Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study. Morden JP, Lambert PC, Latimer N, Abrams KR, Wailoo AJ, BMC Med Res Methodol 2011 11 4 10.1186/1471-2288-11-4 21223539
20. Impact of treatment changes on the interpretation of the Concorde trial. White IR, Walker S, Babiker AG, Darbyshire JH, AIDS 1997 11 8 999 1006 10.1097/00002030-199708000-00008 9223734
21. The assessment, monitoring, and enhancement of treatment fidelity in public health clinical trials. Borrelli B, J Public Health Dent 2011 71 Suppl 1 52 S63 21656954
22. Challenges of maintaining research protocol fidelity in a clinical care setting: a qualitative study of the experiences and views of patients and staff participating in a randomized controlled trial. Lawton J, Jenkins N, Darbyshire JL, Holman RR, Farmer AJ, Hallowell N, Trials 2011 12 108 10.1186/1745-6215-12- 108 21542916
23. Data withdrawal in randomized controlled trials: Defining the problem and proposing solutions: a commentary. Ye C, Giangregorio L, Holbrook A, Pullenayegum E, Goldsmith CH, Thabane L, Contemp Clin Trials 2011 32 3 318 322 10.1016/j.cct.2011.01.016 21300179
24. Adherence to treatment and health outcomes. Horwitz RI, Horwitz SM, Arch Intern Med 1993 153 16 1863 1868 10.1001/archinte.1993.00410160017001 8250647
25. Analysis as-randomized and the problem of non-adherence: an example from the Veterans Affairs Randomized Trial of Coronary Artery Bypass Surgery. Peduzzi P, Wittes J, Detre K, Holford T, Stat Med 1993 12 13 1185 1195 10.1002/sim.4780121302 8210821
26. Intention-to-treat principle. Montori VM, Guyatt GH, CMAJ 2001 165 10 1339 1341 11760981
27. Intention-to-treat analysis in randomized trials: who gets counted? Gibaldi M, Sullivan S, J Clin Pharmacol 1997 37 8 667 672 10.1002/j.1552-4604. 1997.tb04353.x 9378838
28. Porta M, A dictionary of epidemiology Oxford: Oxford University Press, Inc 5 2008
29. Everitt B, Medical statistics from A to Z Cambridge: Cambridge University Press 2 2006
30. Making sense of intention-to-treat. Sainani KL, PM R 2010 2 3 209 213 10.1016/j.pmrj.2010.01.004 20359686
31. Effectiveness of a proactive mail-based alcohol internet intervention for university students: dismantling the assessment and feedback components in a randomized controlled trial. Bendtsen P, McCambridge J, Bendtsen M, Karlsson N, Nilsen P, J Med Internet Res 2012 14 5 142 10.2196/jmir.2062 23113955
32. Four-year follow-up of surgical versus non-surgical therapy for chronic low back pain. Brox JI, Nygaard OP, Holm I, Keller A, Ingebrigtsen T, Reikeras O, Ann Rheum Dis 2010 69 9 1643 1648 10.1136/ard.2009.108902 19635718
33. McKnight PE, McKnight KM, Sidani S, Figueredo AJ, Missing Data: A Gentle Introduction New York, NY: Guilford 2007
34. Missing data analysis: making it work in the real world. Graham JW, Annu Rev Psychol 2009 60 549 576 10.1146/annurev.psych.58.110405.085530 18652544
35. The Prevention and Treatment of Missing Data in Clinical Trials. Little RJ, D'Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, Frangakis C, Hogan JW, Molenberghs G, Murphy SA, et al. New England Journal of Medicine 2012 367 14 1355 1360 10.1056/NEJMsr1203730 23034025
36. Little RJA, Rubin DB, Statistical Analysis with Missing Data New York NY: Wiley 2 2002
37. Rubin DB, Multiple Imputation for Nonresponse in Surveys John Wiley & Sons, Inc: New York NY 1987
38. Schafer JL, Analysis of Incomplete Multivariate Data New York: Chapman and Hall 1997
39. Application of pattern mixture models to address missing data in longitudinal data analysis using SPSS. Son H, Friedmann E, Thomas SA, Nursing research 2012 61 3 195 203 10.1097/NNR.0b013e3182541d8c 22551994
40. Multiple imputation of missing repeated outcome measurements did not add to linear mixed-effects models. Peters SA, Bots ML, den Ruijter HM, Palmer MK, Grobbee DE, Crouse JR 3rd, O'Leary DH, Evans GW, Raichlen JS, Moons KG, et al. J Clin Epidemiol 2012 65 6 686 695 10.1016/j.jclinepi.2011.11.012 22459429
41. Handling missing responses in generalized linear mixed model without specifying missing mechanism. Zhang H, Paik MC, J Biopharm Stat 2009 19 6 1001 1017 10.1080/10543400903242761 20183461
42. Estimation of average treatment effect with incompletely observed longitudinal data: application to a smoking cessation study. Chen HY, Gao S, Statistics in medicine 2009 28 19 2451 2472 10.1002/sim.3617 19462416
43. Imputation strategies for missing binary outcomes in cluster randomized trials. Ma J, Akhtar-Danesh N, Dolovich L, Thabane L, BMC Med Res Methodol 2011 11 18 10.1186/1471-2288-11-18 21324148
44. A randomized placebo-controlled trial of methotrexate in psoriatic arthritis. Kingsley GH, Kowalczyk A, Taylor H, Ibrahim F, Packham JC, McHugh NJ, Mulherin DM, Kitas GD, Chakravarty K, Tom BD, et al. Rheumatology (Oxford) 2012 51 8 1368 1377 10.1093/rheumatology/kes001
45. 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. de Pauw BE, Sable CA, Walsh TJ, Lupinacci RJ, Bourque MR, Wise BA, Nguyen BY, DiNubile MJ, Teppler H, Transpl Infect Dis 2006 8 1 31 37 10.1111/j.1399-3062.2006.00127.x 16623818
46. A randomized, double-blind, futility clinical trial of creatine and minocycline in early Parkinson disease. Neurology 2006 66 5) 664 671 16481597
47. Song P-K, Correlated Data Analysis: Modeling, Analytics and Applications New York, NY: Springer Verlag 2007
48. Pintilie M, Competing Risks: A Practical Perspective New York, NY: John Wiley 2006
49. On the importance of accounting for competing risks in pediatric brain cancer: II. Regression modeling and sample size. Tai BC, Grundy R, Machin D, Int J Radiat Oncol Biol Phys 2011 79 4 1139 1146 10.1016/j.ijrobp.2009.12.024 20472355
50. Lansoprazole for children with poorly controlled asthma: a randomized controlled trial. Holbrook JT, Wise RA, Gold BD, Blake K, Brown ED, Castro M, Dozor AJ, Lima JJ, Mastronarde JG, Sockrider MM, et al. JAMA 2012 307 4 373 381 22274684
51. Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. Holbrook A, Thabane L, Keshavjee K, Dolovich L, Bernstein B, Chan D, Troyan S, Foster G, Gerstein H, CMAJ: Canadian Medical Association journal = journal de l'Association medicale canadienne 2009 181 1-2 37 44
52. Hilbe JM, Negative Binomial Regression Cambridge: Cambridge University Press 2 2011
53. Competing-risk analysis of ESRD and death among patients with type 1 diabetes and macroalbuminuria. Forsblom C, Harjutsalo V, Thorn LM, Waden J, Tolonen N, Saraheimo M, Gordin D, Moran JL, Thomas MC, Groop PH, J Am Soc Nephrol 2011 22 3 537 544 10.1681/ASN.2010020194 21335512
54. Vascular disease, ESRD, and death: interpreting competing risk analyses. Grams ME, Coresh J, Segev DL, Kucirka LM, Tighiouart H, Sarnak MJ, Clin J Am Soc Nephrol 2012 7 10 1606 1614 10.2215/CJN.03460412 22859747
55. Methods of competing risks analysis of end-stage renal disease and mortality among people with diabetes. Lim HJ, Zhang X, Dyck R, Osgood N, BMC Med Res Methodol 2010 10 97 10.1186/1471-2288-10-97 20964855
56. Assessment and implication of prognostic imbalance in randomized controlled trials with a binary outcome-a simulation study. Chu R, Walter SD, Guyatt G, Devereaux PJ, Walsh M, Thorlund K, Thabane L, PLoS One 2012 7 5 36677 10.1371/journal.pone.0036677 22629322
57. Effectiveness of enhanced communication therapy in the first four months after stroke for aphasia and dysarthria: a randomised controlled trial. Bowen A, Hesketh A, Patchick E, Young A, Davies L, Vail A, Long AF, Watkins C, Wilkinson M, Pearl G, et al. BMJ 2012 345 4407 10.1136/bmj.e4407 22797843
58. Spiegelhalter DJ, Best NG, Lunn D, Thomas A, Bayesian Analysis using BUGS: A Practical Introduction New York, NY: Chapman and Hall 2009
59. Application of negative binomial modeling for discrete outcomes: a case study in aging research. Byers AL, Allore H, Gill TM, Peduzzi PN, J Clin Epidemiol 2003 56 6 559 564 10.1016/S0895-4356(03)00028-3 12873651
60. Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials. Yusuf S, Wittes J, Probstfield J, Tyroler HA, JAMA: the journal of the American Medical Association 1991 266 1 93 98 10.1001/jama.1991.03470010097038
61. Better reporting of randomised controlled trials: the CONSORT statement. Altman DG, BMJ 1996 313 7057 570 571 10.1136/bmj.313.7057.570 8806240
62. Principles of good practice for budget impact analysis: report of the ISPOR Task Force on good research practices-budget impact analysis. Mauskopf JA, Sullivan SD, Annemans L, Caro J, Mullins CD, Nuijten M, Orlewska E, Watkins J, Trueman P, Value Health 2007 10 5 336 347 10.1111/j.1524-4733.2007.00187.x 17888098