Application of marginal structural models in pharmacoepidemiologic studies: A systematic review

Pharmacoepidemiology and Drug Safety

Volumn 23, Issue 6, 2014, Pages 560-571

  Yang, Shibing ^a   Eaton, Charles B ^b,c   Lu, Juan ^a   Lapane, Kate L ^d  

^a VIRGINIA COMMONWEALTH UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b MEMORIAL HOSPITAL OF RHODE ISLAND   (United States)

^c BROWN UNIVERSITY   (United States)

^d UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL   (United States)

Author keywords

Inverse probability weighting; Marginal structural models; Pharmacoepidemiology

Indexed keywords

COHORT ANALYSIS; CONTRACEPTIVE BEHAVIOR; DATA EXTRACTION; FOLLOW UP; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTED PATIENT; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; INTENTION TO TREAT ANALYSIS; INVERSE PROBABILITY WEIGHTED ESTIMATION; MARGINAL STRUCTURAL MODEL; MEDLINE; OBSERVATIONAL STUDY; PHARMACOEPIDEMIOLOGY; PRIORITY JOURNAL; PROBABILITY; PROTEIN METABOLISM; RANDOMIZED CONTROLLED TRIAL (TOPIC); REVIEW; RISK FACTOR; SELECTION BIAS; STATISTICAL ANALYSIS; SYSTEMATIC REVIEW; TREATMENT DURATION; TREATMENT FAILURE; VALIDITY; ANIMAL; PROCEDURES; STATISTICAL MODEL;

ANIMALS; COHORT STUDIES; HUMANS; MODELS, STATISTICAL; PHARMACOEPIDEMIOLOGY; RANDOMIZED CONTROLLED TRIALS AS TOPIC;

EID: 84901844484     PISSN: 10538569     EISSN: 10991557     Source Type: Journal    
DOI: 10.1002/pds.3569     Document Type: Review

References (61)

1. Platt RW, Schisterman EF, Cole SR. Time-modified confounding. Am J Epidemiol 2009; 170(6): 687-694.
2. Robins JM. Association, causation, and marginal structural models. Synthese 1999; 121: 151-179.
3. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000; 11(5): 550-560.
4. Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology 2004; 15(5): 615-625.
5. Robins JM, Finkelstein DM. Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics 2000; 56(3): 779-788.
6. Hernán MA, Taubman SL. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes 2008; 32(Suppl 3): S8-14.
7. Petersen M, Porter K, Gruber S, et al. Diagnosing and responding to violations in the positivity assumption. Stat Methods Med Res 2012; 21(1): 31-54.
8. Brumback B, Hernán MA, Haneuse SJ, et al. Sensitivity analyses for unmeasured confounding assuming a marginal structural model for repeated measures. Stat Med 2004; 23(5): 749-767.
9. Cole SR, Hernán MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol 2008; 168(6): 656-664.
10. Lefebvre G, Delaney JA, Platt RW. Impact of mis-specification of the treatment model on estimates from a marginal structural model. Stat Med 2008; 27(18): 3629-3642.
11. Mortimer K, Neugebauer R, van der Laan M, et al. An application of model-fitting procedures for marginal structural models. Am J Epidemiol 2005; 162(4): 382-388.
12. Moodie EE. Risk factor adjustment in marginal structural model estimation of optimal treatment regimes. Biom J 2009; 51(5): 774-788.
13. Suarez D, Borràs R, Basagaña X. Differences between marginal structural models and conventional models in their exposure effect estimates: a systematic review. Epidemiology 2011; 22(4): 586-588.
14. Hernán MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000; 11(5): 561-570.
15. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 2009; 339: b2535.
16. Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology 2003; 14(3): 300-306.
17. Danaei G, Rodriguez LA, Cantero OF, et al. Observational data for comparative effectiveness research: an emulation of randomised trials of statins and primary prevention of coronary heart disease. Stat Methods Med Res 2013; 22(1): 70-96.
18. Toh S, Hernán MA. Causal inference from longitudinal studies with baseline randomization. Int J Biostat 2008; 4(1): Article 22.
19. Hernán MA, Robins JM. Estimating causal effects from epidemiological data. J Epidemiol Community Health 2006; 60(7): 578-586.
20. Cole SR, Frangakis CE. The consistency statement in causal inference: a definition or an assumption? Epidemiology 2009; 20(1): 3-5.
21. Westreich D, Cole SR. Invited commentary: positivity in practice. Am J Epidemiol 2010; 171(6): 674-677.
22. Platt RW, Brookhart MA, Cole SR, et al. An information criterion for marginal structural models. Stat Med 2013; 32(8): 1383-1393
23. Lee TA, Pickard AS. Exposure definition and measurement. In Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide, Velentgas P, Dreyer NA, Nourjah P (eds.). Agency for Healthcare Research and Quality: Rockville, MD, 2013: 45-57.
24. Neugebauer R, Fireman B, Roy JA, et al. Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes. Pharmacoepidemiol Drug Saf 2012; 21: 99-113.
25. Shortreed SM, Moodie EE. Estimating the optimal dynamic antipsychotic treatment regime: evidence from the sequential multiple-assignment randomized clinical antipsychotic trials of intervention and effectiveness schizophrenia study. J R Stat Soc Ser C Appl Stat 2012; 61(4): 577-599.
26. Rosthoj S, Keiding N, Schmiegelow K. Estimation of dynamic treatment strategies for maintenance therapy of children with acute lymphoblastic leukaemia: an application of history-adjusted marginal structural models. Stat Med 2012; 31(5): 470-488.
27. Cain LE, Robins JM, Lanoy E, et al. When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data. Int J Biostat 2010; 6(2): Article 18.
28. Cook NR, Cole SR, Buring JE. Aspirin in the primary prevention of cardiovascular disease in the women's health study: effect of noncompliance. Eur J Epidemiol 2012; 27(6): 431-438.
29. Desai RJ, Ashton CM, Deswal A, et al. Comparative effectiveness of individual angiotensin receptor blockers on risk of mortality in patients with chronic heart failure. Pharmacoepidemiol Drug Saf 2012; 21(3): 233-240.
30. Gerhard T, Delaney JA, Cooper-Dehoff RM, et al. Comparing marginal structural models to standard methods for estimating treatment effects of antihypertensive combination therapy. BMC Med Res Methodol 2012; 12: 119.
31. Gsponer T, Petersen M, Egger M, et al. The causal effect of switching to second-line ART in programmes without access to routine viral load monitoring. AIDS 2012; 26(1): 57-65.
32. Haukka J, Niskanen L, Partonen T, et al. Statin usage and all-cause and disease-specific mortality in a nationwide study. Pharmacoepidemiol Drug Saf 2012; 21(1): 61-69.
33. HCV working group of the Collaboration of Observational HIV Epidemiological Research in Europe (COHERE) in EuroCoord. Effect of hepatitis C treatment on CD4+ T-cell counts and the risk of death in HIV-HCV-coinfected patients: the COHERE collaboration. Antivir Ther 2012; 17(8): 1541-1550.
34. Heffron R, Donnell D, Rees H, et al. Use of hormonal contraceptives and risk of HIV-1 transmission: a prospective cohort study. Lancet Infect Dis 2012; 12(1): 19-26.
35. Hernández D, Muriel A, Abraira C, et al. Renin-angiotensin system blockade and kidney transplantation: a longitudinal cohort study. Nephrol Dial Transplant 2012; 27(1): 417-422.
36. HIV-CAUSAL Collaboration. The effect of efavirenz versus nevirapine-containing regimens on immunologic, virologic and clinical outcomes in a prospective observational study. AIDS 2012; 26(13): 1691-1705.
37. HIV-CAUSAL Collaboration. Impact of antiretroviral therapy on tuberculosis incidence among HIV-positive patients in high-income countries. Clin Infect Dis 2012; 54(9): 1364-1372.
38. Jin H, Tu D, Zhao N, et al. Longer-term outcomes of letrozole versus placebo after 5years of tamoxifen in the NCIC CTG MA.17 trial: analyses adjusting for treatment crossover. J Clin Oncol 2012; 30(7): 718-721.
39. Kalayjian RC, Lau B, Mechekano RN, et al. Risk factors for chronic kidney disease in a large cohort of HIV-1 infected individuals initiating antiretroviral therapy in routine care. AIDS 2012; 26(15): 1907-1915.
40. McCoy SI, Zheng W, Montgomery E, et al. Oral and injectable contraception use and risk of HIV acquisition among women in the Methods for Improving Reproductive health in Africa (MIRA) study. AIDS 2013; 27: 1001-1009.
41. Miller JE, Molnar MZ, Kovesdy CP, et al. Administered paricalcitol dose and survival in hemodialysis patients: a marginal structural model analysis. Pharmacoepidemiol Drug Saf 2012; 21(11): 1232-1239.
42. Morrison CS, Skoler-Karpoff S, Kwok C, et al. Hormonal contraception and the risk of HIV acquisition among women in South Africa. AIDS 2012; 26(4): 497-504.
43. Scherzer R, Estrella M, Li Y, et al. Association of tenofovir exposure with kidney disease risk in HIV infection. AIDS 2012; 26(7): 867-875.
44. Shinozaki T, Matsuyama Y, Iimuro S, et al. Effective prevention of cardiovascular disease and diabetes-related events with atorvastatin in Japanese elderly patients with type 2 diabetes mellitus: adjusting for treatment changes using a marginal structural proportional hazards model and a rank-preserving structural failure time model. Geriatr Gerontol Int 2012; 12(Suppl 1): 88-102.
45. Terrier B, Krastinova E, Marie I, et al. Management of noninfectious mixed cryoglobulinemia vasculitis: data from 242 cases included in the CryoVas survey. Blood 2012; 119(25): 5996-6004.
46. Tiihonen J, Suokas JT, Suvisaari JM, et al. Polypharmacy with antipsychotics, antidepressants, or benzodiazepines and mortality in schizophrenia. Arch Gen Psychiatry 2012; 69(5): 476-483.
47. Young J, Schaefer J, Fux CA, et al. Renal function in patients with HIV starting therapy with tenofovir and either efavirenz, lopinavir or atazanavir. AIDS 2012; 26(5): 567-575.
48. Barber J. Adjusting for time-varying confounding in survival analysis. Sociol Methodol 2004; 34(1): 163-192.
49. Platt RW, Delaney JA, Suissa S. The positivity assumption and marginal structural models: the example of warfarin use and risk of bleeding. Eur J Epidemiol 2012; 27(2): 77-83.
50. Dumas O, Le Moual N, Siroux V, et al. Work related asthma. A causal analysis controlling the healthy worker effect. Occup Environ Med 2013; 70(9): 603-610.
51. Chiba Y. Sensitivity analysis of unmeasured confounding for the causal risk ratio by applying marginal structural models. Commun Stat Theory Methods 2010; 39(1): 65-76.
52. Cain LE, Cole SR. Inverse probability-of-censoring weights for the correction of time-varying noncompliance in the effect of randomized highly active antiretroviral therapy on incident AIDS or death. Stat Med 2009; 28(12): 1725-1738.
53. Setoguchi S, Gerhard T. Comparator selection. In Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide, Velentgas P, Dreyer NA, Nourjah P (eds.). Agency for Healthcare Research and Quality: Rockville, MD, 2013: 59-70.
54. Brookhart MA, Schneeweiss S, Rothman K, et al. Variable selection for propensity score models. Am J Epidemiol 2006; 163(12): 1149-1156.
55. Lipkovich I, Mallinckrodt C, Faries D. The challenges of evaluating dose response in flexible-dose trials using marginal structural models. Pharm Stat 2012; 11(6): 485-493.
56. Xiao Y, Abrahamowicz M, Moodie EE. Accuracy of conventional and marginal structural Cox model estimators: a simulation study. Int J Biostat 2010; 6(2): Article 13.
57. Bryan J, Yu Z, Van Der Laan MJ. Analysis of longitudinal marginal structural models. Biostatistics 2004; 5(3): 361-380.
58. Stürmer T, Rothman KJ, Avorn J, et al. Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution-a simulation study. Am J Epidemiol 2010; 172(7): 843-854.
59. Kurth T, Walker AM, Glynn RJ, et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol 2006; 163(3): 262-270.
60. Greenland S, Maldonado G. The interpretation of multiplicative-model parameters as standardized parameters. Stat Med 1994; 13(10): 989-999.
61. Stürmer T, Rothman KJ, Glynn RJ. Insights into different results from different causal contrasts in the presence of effect-measure modification. Pharmacoepidemiol Drug Saf 2006; 15(10): 698-709.