Linkage of routinely collected oncology clinical data with health insurance claims data-an example with aromatase inhibitors, tamoxifen, and all-cause mortality

Pharmacoepidemiology and Drug Safety

Volumn 21, Issue SUPPL.2, 2012, Pages 29-36

  Dore, David D ^a,b   Liang, Caihua ^b   Ziyadeh, Najat ^b   Norman, Heather ^b   Bayliss, Martha ^c   Seeger, John D ^b,d  

^a BROWN UNIVERSITY   (United States)

^b OPTUM   (United States)

^c QualityMetric Incorporated   (United States)

^d BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

Claims data; Confounding; Oncology; Pharmacoepidemiology

Indexed keywords

AROMATASE INHIBITOR; TAMOXIFEN;

ARTICLE; BREAST CANCER; CANCER CHEMOTHERAPY; FEMALE; HAZARD RATIO; HEALTH INSURANCE; HUMAN; INFORMATION PROCESSING; MAJOR CLINICAL STUDY; MORTALITY; ONCOLOGY; PRIORITY JOURNAL; PROPENSITY SCORE; PROPORTIONAL HAZARDS MODEL;

ANTINEOPLASTIC AGENTS; AROMATASE INHIBITORS; BREAST NEOPLASMS; COHORT STUDIES; CONFOUNDING FACTORS (EPIDEMIOLOGY); DATABASES, FACTUAL; FEMALE; HUMANS; INSURANCE CLAIM REVIEW; MEDICAL RECORD LINKAGE; PHARMACOEPIDEMIOLOGY; PROPORTIONAL HAZARDS MODELS; TAMOXIFEN; UNITED STATES;

EID: 84860594389     PISSN: 10538569     EISSN: 10991557     Source Type: Journal    
DOI: 10.1002/pds.3244     Document Type: Article

References (26)

1. Walker AM. Confounding by indication. Epidemiology 1996; 7: 335-336.
2. Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology 2009; 20: 512-522.
3. Seeger JD, Walker AM, Williams PL, Saperia GM, Sacks FM. A propensity score-matched cohort study of the effect of statins, mainly fluvastatin, on the occurrence of acute myocardial infarction. Am J Cardiol 2003; 92: 1447-1451.
4. Cuzick J, Sestak I, Baum M, et al. Effect of anastrozole and tamoxifen as adjuvant treatment for early-stage breast cancer: 10-year analysis of the ATAC trial. Lancet Oncol 2010; 11: 1135-1141.
5. Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol 2003; 158: 915-920.
6. Fetter RB, Shin Y, Freeman JL, Averill RF, Thompson JD. Case mix definition by diagnosis-related groups. Med Care 1980; 18: 1-53.
7. Dang DK, Pont JM, Portmoy MA. Episode treatment groups: an illness classification and episode building system--Part II. Med Interface 1996; 9: 122-128.
8. Dang DK, Pont JM, Portnoy MA. Episode treatment groups: an illness classification and episode building system--Part I. Med Interface 1996; 9: 118-122.
9. Forthman MT, Dove HG, Wooster LD. Episode Treatment Groups (ETGs): a patient classification system for measuring outcomes performance by episode of illness. Top Health Inf Manage 2000; 21: 51-61.
10. Global Internet Management, U.S. Department of Commerce NTIS. Social Security Administration's Death Master File Database, 2011. [7 July 2011].
11. Eng PM, Seeger JD, Loughlin J, Clifford CR, Mentor S, Walker AM. Supplementary data collection with case-cohort analysis to address potential confounding in a cohort study of thromboembolism in oral contraceptive initiators matched on claims-based propensity scores. Pharmacoepidemiol Drug Saf 2008; 17: 297-305.
12. Walker AM. Observation and inference: an introduction to the methods of epidemiology. Epidemiology Resources Inc.: Chestnut Hill, MA, 1991.
13. Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf 2006; 15: 291-303.
14. US Food and Drug Administration. Orange Book: Approved Drug Products with Therapeutic Equivalence Evaluations, 2010. [8 July 2011].
15. Dore DD, Bloomgren GL, Wenten M, et al. A cohort study of acute pancreatitis in relation to exenatide use. Diabetes Obes Metab 2011; 13: 559-566. DOI: 10.1111/j.1463-1326.2011.01376.x.
16. Schneeweiss S, Glynn RJ, Tsai EH, Avorn J, Solomon DH. Adjusting for unmeasured confounders in pharmacoepidemiologic claims data using external information: the example of COX2 inhibitors and myocardial infarction. Epidemiology 2005; 16: 17-24.
17. Sturmer T, Glynn RJ, Rothman KJ, Avorn J, Schneeweiss S. Adjustments for unmeasured confounders in pharmacoepidemiologic database studies using external information. Med Care 2007; 45: S158-S165.
18. Sturmer T, Schneeweiss S, Rothman KJ, Avorn J, Glynn RJ. Propensity Score Calibration and its Alternatives. Am J Epidemiol 2007; 165: 1122-1123.
19. Sturmer T, Schneeweiss S, Rothman KJ, Avorn J, Glynn RJ. Performance of propensity score calibration--a simulation study. Am J Epidemiol 2007; 165: 1110-1118.
20. Walker AM. Anamorphic analysis: sampling and estimation for covariate effects when both exposure and disease are known. Biometrics 1982; 38: 1025-1032.
21. Lash TL. Heuristic thinking and inference from observational epidemiology. Epidemiology 2007; 18: 67-72.
22. Sturmer T, Schneeweiss S, Avorn J, Glynn RJ. Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. Am J Epidemiol 2005; 162: 279-289.
23. Fowler FJ. Survey research methods (3rd edn). Sage Publications: Thousand Oaks, CA, 2002.
24. McKenzie DA, Semradek J, McFarland BH, Mullooly JP, McCamant LE. The validity of medicaid pharmacy claims for estimating drug use among elderly nursing home residents: The Oregon experience. J Clin Epidemiol 2000; 53: 1248-1257.
25. Rothman KJ, Greenland S, Lash TL. Modern epidemiology (3rd edn). Wolters Kluwer Health/Lippincott Williams & Wilkins: Philadelphia, 2008.
26. Dore DD, McAfee AT, Woo RK, Seeger JD. Enhanced Oncology Applications of Health Insurance Claims Data through Clinical Data Linkage (Abstract 081). Pharmacoepidemiol Drug Saf 2009; 18: S36. DOI: 10.1002/pds.2206.