High-dimensional versus conventional propensity scores in a comparative effectiveness study of coxibs and reduced upper gastrointestinal complications

European Journal of Clinical Pharmacology

Volumn 69, Issue 3, 2013, Pages 549-557

  Garbe, E ^a,b   Kloss, S ^a   Suling, M ^a   Pigeot, I ^a   Schneeweiss, S ^c  

^a LEIBNIZ INSTITUTE FOR PREVENTION RESEARCH AND EPIDEMIOLOGY BIPS   (Germany)

^b UNIVERSITY OF BREMEN   (Germany)

^c BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

Confounding by indication; High dimensional propensity score; NSAIDs; Propensity score; Upper gastrointestinal complications

Indexed keywords

ACECLOFENAC; ACEMETACIN; AZAPROPAZONE; CELECOXIB; CYCLOOXYGENASE 2 INHIBITOR; DEXIBUPROFEN; DEXKETOPROFEN; DICLOFENAC; ETORICOXIB; FENAMIC ACID DERIVATIVE; FLURBIPROFEN; GLUCOSAMINE; IBUPROFEN; INDOMETACIN; KEBUZONE; KETOPROFEN; LONAZOLAC; LORNOXICAM; MEFENAMIC ACID; MELOXICAM; MOFEBUTAZONE; NABUMETONE; NAPROXEN; NONSTEROID ANTIINFLAMMATORY AGENT; OXACEPROL; OXAPROZIN; PHENYLBUTAZONE; PIROXICAM; UNINDEXED DRUG; VALDECOXIB;

ADULT; ALGORITHM; ARTICLE; COMORBIDITY; COMPARATIVE EFFECTIVENESS; CONFOUNDING VARIABLE; DRUG EFFICACY; DRUG EXPOSURE; DRUG INDUCED DISEASE; FEMALE; GASTROINTESTINAL DISEASE; HUMAN; INCIDENCE; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; PROPENSITY SCORE; RISK; UPPER GASTROINTESTINAL COMPLICATION;

ADULT; AGED; ALGORITHMS; ANTI-INFLAMMATORY AGENTS, NON-STEROIDAL; BENCHMARKING; COMPARATIVE EFFECTIVENESS RESEARCH; CONFIDENCE INTERVALS; CONFOUNDING FACTORS (EPIDEMIOLOGY); CYCLOOXYGENASE 2 INHIBITORS; FEMALE; GASTROINTESTINAL DISEASES; GERMANY; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; MULTIVARIATE ANALYSIS; ODDS RATIO; PHARMACOEPIDEMIOLOGY; PROPENSITY SCORE; RISK ASSESSMENT; RISK FACTORS;

EID: 84877127012     PISSN: 00316970     EISSN: 14321041     Source Type: Journal    
DOI: 10.1007/s00228-012-1334-2     Document Type: Article

References (35)

1. Schneeweiss S, Avorn J (2005) A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 58(4):323-337
2. Suissa S, Garbe E (2007) Primer: administrative health databases in observational studies of drug effects - advantages and disadvantages. Nat Clin Pract Rheumatol 3(12):725-732
3. Schneeweiss S, Gagne JJ, Glynn RJ, Ruhl M, Rassen JA (2011) Assessing the comparative effectiveness of newly marketed medications: methodological challenges and implications for drug development. Clin Pharmacol Ther 90(6):777-790
4. Schneeweiss S (2007) Developments in post-marketing comparative effectiveness research. Clin Pharmacol Ther 82(2):143-156
5. Cox E, Martin BC, Van Staa T, Garbe E, Siebert U, Johnson ML (2009) Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report - Part I. Value Health 12(8):1053-1061
6. Wooldridge JM (2001) Econometric analysis of cross section and panel data. MIT Press, Cambridge
7. Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA (2009) High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology 20(4):512-522
8. Schneeweiss S, Rassen J (2011) Re: Confounding adjustment via a semi-automated high-dimensional propensity score algorithm: an application to electronic medical records. Pharmacoepidemiol Drug Saf 20(10):1110-1111
9. Joffe MM (2009) Exhaustion, automation, theory, and confounding. Epidemiology 20(4):523-524
10. Pigeot I, Ahrens W (2008) Establishment of a pharmacoepidemiological database in Germany: methodological potential, scientific value and practical limitations. Pharmacoepidemiol Drug Saf 17(3):215-223
11. Schink T, Garbe E (2010) Representativity of dispensations of non-steroidal anti-inflammatory drugs (NSAIDs) in the German Pharmacoepidemiological Research Database. Pharmacoepidemiol Drug Saf 19:S294
12. Schink T, Garbe E (2010) Assessment of the representativity of in-patient hospital diagnoses in the German Pharmacoepidemiological Research Database. Pharmacoepidemiol Drug Saf 19:S178-S179
13. Cattaruzzi C, Troncon MG, Agostinis L, Garcia Rodriguez LA (1999) Positive predictive value of ICD-9th codes for upper gastrointestinal bleeding and perforation in the Sistema Informativo Sanitario Regionale database. J Clin Epidemiol 52(6):499-502
14. Raiford DS, Perez GS, Garcia Rodriguez LA (1996) Positive predictive value of ICD-9 codes in the identification of cases of complicated peptic ulcer disease in the Saskatchewan hospital automated database. Epidemiology 7(1):101-104
15. Glynn RJ, Schneeweiss S, Sturmer T (2006) Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin Pharmacol Toxicol 98(3):253-259
16. Rostom A, Muir K, Dube C, Jolicoeur E, Boucher M, Joyce J, Tugwell P, Wells GW (2007) Gastrointestinal safety of cyclooxygenase-2 inhibitors: a Cochrane Collaboration systematic review. Clin Gastroenterol Hepatol 5(7):818-28, 828
17. Moore RA, Derry S, Phillips CJ, McQuay HJ (2006) Nonsteroidal anti-inflammatory drugs (NSAIDs), cyxlooxygenase-2 selective inhibitors (coxibs) and gastrointestinal harm: review of clinical trials and clinical practice. BMC Musculoskelet Disord 7:79
18. Schneeweiss S, Glynn RJ, Avorn J, Solomon DH (2005) A Medicare database review found that physician preferences increasingly outweighed patient characteristics as determinants of first-time prescriptions for COX-2 inhibitors. J Clin Epidemiol 58(1):98-102
19. Solomon DH, Schneeweiss S, Glynn RJ, Levin R, Avorn J (2003) Determinants of selective cyclooxygenase-2 inhibitor prescribing: are patient or physician characteristics more important? Am J Med 115(9):715-720
20. Depont F, Fourrier A, Merliere Y et al (2007) Channelling of COX-2 inhibitors to patients at higher gastrointestinal risk but not at lower cardiovascular risk: the Cox2 inhibitors and tNSAIDs description of users (CADEUS) study. Pharmacoepidemiol Drug Saf 16(8):891-900
21. Laharie D, Droz-Perroteau C, Benichou J et al (2010) Hospitalizations for gastrointestinal and cardiovascular events in the CADEUS cohort of traditional or Coxib NSAID users. Br J Clin Pharmacol 69(3):295-302
22. Morant SV, Pettitt D, MacDonald TM, Burke TA, Goldstein JL (2004) Application of a propensity score to adjust for channelling bias with NSAIDs. Pharmacoepidemiol Drug Saf 13(6):345-353
23. MacDonald TM, Morant SV, Goldstein JL, Burke TA, Pettitt D (2003) Channelling bias and the incidence of gastrointestinal haemorrhage in users of meloxicam, coxibs, and older, non-specific non-steroidal anti-inflammatory drugs. Gut 52(9):1265-1270
24. Bombardier C, Laine L, Reicin A, Shapiro D, Burgos-Vargas R, Davis B, Day R, Ferraz MB, Hawkey CJ, Hochberg MC, Kvien TK, Schnitzer TJ (2000) Comparison of upper gastrointestinal toxicity of rofecoxib and naproxen in patients with rheumatoid arthritis. VIGOR Study Group. N Engl J Med 343(21):1520-8, 2
25. Silverstein FE, Faich G, Goldstein JL, Simon LS, Pincus T, Whelton A, Makuch R, Eisen G, Agrawal NM, Stenson WF, Burr AM, Zhao WW, Kent JD, Lefkowith JB, Verburg KM, Geis GS (2000) Gastrointestinal toxicity with celecoxib vs nonsteroidal anti-inflammatory drugs for osteoarthritis and rheumatoid arthritis: the CLASS study: a randomized controlled trial. Celecoxib Long-term Arthritis Safety Study. JAMA 284(10):1247-1255
26. Toh S, Garcia Rodriguez LA, Hernan MA (2011) Confounding adjustment via a semi-automated high-dimensional propensity score algorithm: an application to electronic medical records. Pharmacoepidemiol Drug Saf 20(8):849-857
27. Schneeweiss S (2010) A basic study design for expedited safety signal evaluation based on electronic healthcare data. Pharmacoepidemiol Drug Saf 19(8):858-868
28. Hernan MA, Hernandez-Diaz S (2012) Beyond the intention-to-treat in comparative effectiveness research. Clin Trials 9(1):48-55
29. Schneeweiss S, Seeger JD, Maclure M, Wang PS, Avorn J, Glynn RJ (2001) Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol 154(9):854-864
30. Rassen JA, Glynn RJ, Brookhart MA, Schneeweiss S (2011) Covariate selection in high-dimensional propensity score analyses of treatment effects in small samples. Am J Epidemiol 173(12):1404-1413
31. Sturmer T, Rothman KJ, Avorn J, Glynn RJ (2010) 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 172(7):843-854
32. Cadarette SM, Gagne JJ, Solomon DH, Katz JN, Sturmer T (2010) Confounder summary scores when comparing the effects of multiple drug exposures. Pharmacoepidemiol Drug Saf 19(1):2-9
33. Rassen JA, Solomon DH, Glynn RJ, Schneeweiss S (2011) Simultaneously assessing intended and unintended treatment effects of multiple treatment options: a pragmatic "matrix design". Pharmacoepidemiol Drug Saf 20(7):675-683
34. Wei L, Brookhart MA, Schneeweiss S, Setoguchi S (2012) Implications of collider-stratification bias in epidemiologic studies: simulation study. Am J Epidemiol (in press)
35. Myers JA, Rassen JA, Gagne JJ, Huybrechts KF, Schneeweiss S, Rothman KJ, Joffe MM, Glynn RJ (2011) Effects of adjusting for instrumental variables on bias and precision of effect estimates. Am J Epidemiol 174(11):1213-1222