Comparison of high-dimensional confounder summary scores in comparative studies of newly marketed medications

Journal of Clinical Epidemiology

Volumn 76, Issue , 2016, Pages 200-208

  Kumamaru, Hiraku ^a,b   Gagne, Joshua J ^a   Glynn, Robert J ^a   Setoguchi, Soko ^c   Schneeweiss, Sebastian ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^c DUKE CLINICAL RESEARCH INSTITUTE   (United States)

Author keywords

Administrative data; Comparative safety study; Confounding adjustment; Disease risk score; Historical cohort; Propensity score

Indexed keywords

CYCLOOXYGENASE 2 INHIBITOR; DABIGATRAN; NONSTEROID ANTIINFLAMMATORY AGENT; WARFARIN;

ADULT; ARTICLE; BLEEDING; COHORT ANALYSIS; COMPARATIVE STUDY; DEATH; DRUG MARKETING; DRUG SAFETY; FEMALE; GASTROINTESTINAL HEMORRHAGE; HIGH DIMENSIONAL DISEASE RISK SCORE; HIGH DIMENSIONAL PROPENSITY SCORE; HUMAN; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; PRIORITY JOURNAL; PROPENSITY SCORE; RANDOMIZED CONTROLLED TRIAL (TOPIC); RISK; AGED; EPIDEMIOLOGY; MORTALITY; RISK ASSESSMENT; STATISTICAL ANALYSIS;

ADULT; AGED; ANTI-INFLAMMATORY AGENTS, NON-STEROIDAL; CONFOUNDING FACTORS (EPIDEMIOLOGY); CYCLOOXYGENASE 2 INHIBITORS; DATA INTERPRETATION, STATISTICAL; FEMALE; GASTROINTESTINAL HEMORRHAGE; HUMANS; MALE; MIDDLE AGED; PROPENSITY SCORE; RISK ASSESSMENT; WARFARIN;

EID: 84962109372     PISSN: 08954356     EISSN: 18785921     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2016.02.011     Document Type: Article

References (28)

1. [1] Schneeweiss, S., Gagne, J.J., Glynn, R.J., Ruhl, M., Rassen, J.A., Assessing the comparative effectiveness of newly marketed medications: methodological challenges and implications for drug development. Clin Pharmacol Ther 90 (2011), 777–790.
2. [2] Eichler, H.-G., Oye, K., Baird, L.G., Abadie, E., Brown, J., Drum, C.L., et al. Adaptive licensing: taking the next step in the evolution of drug approval. Clin Pharmacol Ther 91 (2012), 426–437.
3. [3] Schneeweiss, S., Avorn, J., A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 58 (2005), 323–337.
4. [4] Sauer, B.C., Brookhart, M.A., Roy, J., Vanderweele, T., A review of covariate selection for non-experimental comparative effectiveness research. Pharmacoepidemiol Drug Saf 22 (2013), 1139–1145.
5. [5] Brookhart, M.A., Stürmer, T., Glynn, R.J., Rassen, J., Schneeweiss, S., Confounding control in healthcare database research: challenges and potential approaches. Med Care 48 (2010), S114–S120.
6. [6] Rassen, J.A., Schneeweiss, S., Using high-dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system. Pharmacoepidemiol Drug Saf 21 (2012), 41–49.
7. [7] Schneeweiss, S., Rassen, J.A., Glynn, R.J., Avorn, J., Mogun, H., Brookhart, M.A., High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology 20 (2009), 512–522.
8. [8] Rassen, J.A., Glynn, R.J., Brookhart, M.A., Schneeweiss, S., Covariate selection in high-dimensional propensity score analyses of treatment effects in small samples. Am J Epidemiol 173 (2011), 1404–1413.
9. [9] Hansen, B.B., The prognostic analogue of the propensity score. Biometrika 95 (2008), 481–488.
10. [10] Glynn, R.J., Gagne, J.J., Schneeweiss, S., Role of disease risk scores in comparative effectiveness research with emerging therapies. Pharmacoepidemiol Drug Saf 21 (2012), 138–147.
11. [11] Wyss, R., Ellis, A.R., Brookhart, M.A., Funk, M.J., Girman, C.J., Simpson, R.J. Jr., et al. Matching on the disease risk score in comparative effectiveness research of new treatments. Pharmacoepidemiol Drug Saf 24 (2015), 951–961.
12. [12] Kumamaru H, Schneeweiss S, Glynn RJ, Setoguchi S, Gagne JJ. Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data. Emerging Themes in Epidemiology. [Epub ahead of print].
13. [13] January, C.T., Wann, L.S., Alpert, J.S., Calkins, H., Cleveland, J.C., Cigarroa, J.E., et al. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the heart rhythm society. J Am Coll Cardiol 64 (2014), e1–e76.
14. [14] Connolly, S., Ezekowitz, M., Reilly, P.A., Themeles, E., Varrone, J., Wang, S., et al. Dabigatran versus warfarin in patients with atrial fibrillation. N Engl J Med 361 (2009), 1139–1151.
15. [15] Hill, M., Rosenwaike, I., The social security administration's death master file: the completeness of death reporting at older ages. Soc Secur Bull 64 (2001–2002), 45–51.
16. [16] Solomon, D.H., Schneeweiss, S., Glynn, R.J., Kiyota, Y., Levin, R., Mogun, H., et al. Relationship between selective cyclooxygenase-2 inhibitors and acute myocardial infarction in older adults. Circulation 109 (2004), 2068–2073.
17. [17] Wahl, P.M., Rodgers, K., Schneeweiss, S., Gage, B.F., Butler, J., Wilmer, C., et al. Validation of claims-based diagnostic and procedure codes for cardiovascular and gastrointestinal serious adverse events in a commercially-insured population. Pharmacoepidemiol Drug Saf 19 (2010), 596–603.
18. [18] Watson, D.J., Harper, S.E., Zhao, P.L., Quan, H., Bolognese, J.A., Simon, T.J., Gastrointestinal tolerability of the selective cyclooxygenase-2 (COX-2) inhibitor rofecoxib compared with nonselective COX-1 and COX-2 inhibitors in osteoarthritis. Arch Intern Med 160 (2000), 2998–3003.
19. [19] Moore, R.A., Derry, S., Makinson, G.T., Mcquay, H.J., Tolerability and adverse events in clinical trials of celecoxib in osteoarthritis and rheumatoid arthritis: systematic review and meta-analysis of information from company clinical trial reports. Arthritis Res Ther 7 (2005), R644–R665.
20. [20] Bombardier, C., Laine, L., Reicin, A., Shapiro, D., Burgos-Vargas, R., Davis, B., et al. Comparison of upper gastrointestinal toxicity of rofecoxib and naproxen in patients with rheumatoid arthritis. N Engl J Med 343 (2000), 1520–1528.
21. [21] Silverstein, F.E., Faich, G., Goldstein, J.L., Simon, L.S., Pincus, T., Whelton, A., et al. 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 (2000), 1247–1255.
22. [22] Bross, I.D.J., Spurious effects from an extraneous variable. J Chronic Dis 19 (1966), 637–647.
23. [23] Brookhart, M.A., Schneeweiss, S., Rothman, K.J., Glynn, R.J., Avorn, J., Stürmer, T., Variable selection for propensity score models. Am J Epidemiol 163 (2006), 1149–1156.
24. [24] Robins, J.M., Mark, S.D., Newey, W.K., Mark, D., Estimating exposure effects by modelling expectation of exposure conditional on confounders. Biometrics 48 (2014), 479–495.
25. [25] Stürmer, T., Rothman, K.J., Avorn, J., Glynn, R.J., 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 (2010), 843–854.
26. [26] Pisters, R., Lane, D.A., Nieuwlaat, R., de Vos, C.B., Crijns, H.J.G.M., Lip, G.Y.H., A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest 138 (2010), 1093–1100.
27. [27] Peduz, P., Concato, J., Kemper, E., Holford, T.R., Feinstein, A.R., A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49 (1996), 1373–1379.
28. [28] Drake, C., Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics 49 (1993), 1231–1236.