Sample size importantly limits the usefulness of instrumental variable methods, depending on instrument strength and level of confounding

Journal of Clinical Epidemiology

Volumn 67, Issue 11, 2014, Pages 1258-1264

  Boef, Anna G C ^a   Dekkers, Olaf M ^a   Vandenbroucke, Jan P ^a   Le Cessie, Saskia ^a  

^a LEIDEN UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

Confounding; Instrumental variable; Observational studies; Simulation study; Therapeutic effects; Variance

Indexed keywords

ARTICLE; BINOMIAL DISTRIBUTION; CALCULATION; HUMAN; INSTRUMENTAL VARIABLE ANALYSIS; POPULATION SIZE; SAMPLE SIZE; SIMULATION; VARIANCE; MENDELIAN RANDOMIZATION ANALYSIS; METHODOLOGY; OBSERVATIONAL STUDY; RANDOMIZED CONTROLLED TRIAL (TOPIC); REGRESSION ANALYSIS; STANDARDS; STATISTICAL BIAS; STATISTICAL MODEL;

BIAS (EPIDEMIOLOGY); HUMANS; LEAST-SQUARES ANALYSIS; MENDELIAN RANDOMIZATION ANALYSIS; MODELS, STATISTICAL; OBSERVATIONAL STUDY AS TOPIC; RANDOMIZED CONTROLLED TRIALS AS TOPIC; REGRESSION ANALYSIS; RESEARCH DESIGN; SAMPLE SIZE;

EID: 84908300029     PISSN: 08954356     EISSN: 18785921     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2014.05.019     Document Type: Article

References (23)

1. M.A. Brookhart, J.A. Rassen, and S. Schneeweiss Instrumental variable methods in comparative safety and effectiveness research Pharmacoepidemiol Drug Saf 19 2010 537 554
2. J.A. Rassen, M.A. Brookhart, R.J. Glynn, M.A. Mittleman, and S. Schneeweiss Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships J Clin Epidemiol 62 2009 1226 1232
3. S.A. Swanson, and M.A. Hernan Commentary: how to report instrumental variable analyses (suggestions welcome) Epidemiology 24 2013 370 374
4. N.M. Davies, G. Davey Smith, F. Windmeijer, and R.M. Martin Issues in the reporting and conduct of instrumental variable studies: a systematic review Epidemiology 24 2013 363 369
5. M.A. Hernan, and J.M. Robins Instruments for causal inference: an epidemiologist's dream? Epidemiology 17 4 2006 360 372
6. T.A. Stukel, E.S. Fisher, D.E. Wennberg, D.A. Alter, D.J. Gottlieb, and M.J. Vermeulen Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods JAMA 297 2007 278 285
7. M.A. Brookhart, P.S. Wang, D.H. Solomon, and S. Schneeweiss Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable Epidemiology 17 2006 268 275
8. S. Schneeweiss, J.D. Seeger, J. Landon, and A.M. Walker Aprotinin during coronary-artery bypass grafting and risk of death N Engl J Med 358 2008 771 783
9. S. Schneeweiss, S. Setoguchi, A. Brookhart, C. Dormuth, and P.S. Wang Risk of death associated with the use of conventional versus atypical antipsychotic drugs among elderly patients CMAJ 176 2007 627 632
10. D.A. Lawlor, R.M. Harbord, J.A. Sterne, N. Timpson, and G. Davey Smith Mendelian randomization: using genes as instruments for making causal inferences in epidemiology Stat Med 27 2008 1133 1163
11. E.P. Martens, W.R. Pestman, A. de Boer, S.V. Belitser, and O.H. Klungel Instrumental variables: application and limitations Epidemiology 17 2006 260 267
12. R. Ionescu-Ittu, J.A. Delaney, and M. Abrahamowicz Bias-variance trade-off in pharmacoepidemiological studies using physician-preference-based instrumental variables: a simulation study Pharmacoepidemiol Drug Saf 18 2009 562 571
13. W.H. Crown, H.J. Henk, and D.J. Vanness Some cautions on the use of instrumental variables estimators in outcomes research: how bias in instrumental variables estimators is affected by instrument strength, instrument contamination, and sample size Value Health 14 2011 1078 1084
14. M. Abrahamowicz, M.E. Beauchamp, R. Ionescu-Ittu, J.A. Delaney, and L. Pilote Reducing the variance of the prescribing preference-based instrumental variable estimates of the treatment effect Am J Epidemiol 174 2011 494 502
15. G. Freeman, B.J. Cowling, and C.M. Schooling Power and sample size calculations for Mendelian randomization studies using one genetic instrument Int J Epidemiol 42 2013 1157 1163
16. S. Burgess, and S.G. Thompson Improving bias and coverage in instrumental variable analysis with weak instruments for continuous and binary outcomes Stat Med 31 2012 1582 1600
17. J.A. Rassen, M.A. Brookhart, R.J. Glynn, M.A. Mittleman, and S. Schneeweiss Instrumental variables II: instrumental variable application-in 25 variations, the physician prescribing preference generally was strong and reduced covariate imbalance J Clin Epidemiol 62 2009 1233 1241
18. K.F. Huybrechts, M.A. Brookhart, K.J. Rothman, R.A. Silliman, T. Gerhard, and S. Crystal Comparison of different approaches to confounding adjustment in a study on the association of antipsychotic medication with mortality in older nursing home patients Am J Epidemiol 174 2011 1089 1099
19. J. Hadley, K.R. Yabroff, M.J. Barrett, D.F. Penson, C.S. Saigal, and A.L. Potosky Comparative effectiveness of prostate cancer treatments: evaluating statistical adjustments for confounding in observational data J Natl Cancer Inst 102 2010 1780 1793
20. J.A. Rassen, M.A. Mittleman, R.J. Glynn, B.M. Alan, and S. Schneeweiss Safety and effectiveness of bivalirudin in routine care of patients undergoing percutaneous coronary intervention Eur Heart J 31 2010 561 572
21. S. Greenland Basic methods for sensitivity analysis of biases Int J Epidemiol 25 1996 1107 1116
22. M.J. Brion, K. Shakhbazov, and P.M. Visscher Calculating statistical power in Mendelian randomization studies Int J Epidemiol 42 2013 1497 1501
23. J.A. Rassen, S. Schneeweiss, R.J. Glynn, M.A. Mittleman, and M.A. Brookhart Instrumental variable analysis for estimation of treatment effects with dichotomous outcomes Am J Epidemiol 169 2009 273 284