Instrumental variables for comparative effectiveness research: a review of applications

1. Instrumental variables for comparative effectiveness research: a review of applications M. Alan Brookhart, Ph.D. Division of Pharmacoepidemiology, Brigham & Women’s Hospital, Harvard Medical School

2. Overview of Lecture • Brief introduction to instrumental variable analysis • Examples of instrumental variables, some characteristics • Role of IV in observational studies of medical interventions

3. The Challenge of Observational Studies of Intended Effects • Confounding by indication is strong • Patients who need treatment are more likely to receive treatment • Indications unmeasured or poorly measured • > Unmeasured confounding bias

4. Instrumental Variables • Can permit estimation of causal effects even when important confounders are unmeasured • Instrument should be correlated with treatment • Instrument should be related to outcome only through association with treatment (often termed the exclusion restriction) • Empirically unverifiable, but can be explored in observed data.

5. Randomization Instrument Z Blinding Confounding and Instrumental Variables Example: Randomized Controlled Trial with Non-Compliance Confounders Treatment Arm Assignment C Y X Received Treatment Outcome

6. Intention-to-treat and (Wald) IV Estimator ITT Estimator = E[Y|Z=1] - E[Y|Z=0] E[Y|Z=1] - E[Y|Z=0] IV Estimator = ------------------------- E[X|Z=1] - E[X|Z=0] Effect of the Instrument on the Outcome = ------------------------------------------------------------ Effect of the Instrument on the Exposure α

7. Interpretation of an IV • When treatment effects are heterogeneous, IV estimator may be biased for average treatment effect (ATE) • IV estimates a weighted average of causal treatment effects • Subgroups of patients whose treatment status is more likely to be influenced by the IV are weighted up • Empirical data, subject-matter knowledge may be used to anticipate direction of bias in IV relative to ATE

8. IVs For Comparative Effectiveness

9. Preference-based Instrumental Variables • Substantial variation in medical practice across regions, hospitals, physicians • Differences in medical practice may represent a natural experiment • Suggests IVs defined at level of provider

10. Observational Study of Non-steroidal Anti-Inflammatory Drugs and GI bleeding risk in an elderly population(Brookhart et al, Epidemiology 2006) • Compare short-term risk of GI outcomes between • Non-selective NSAIDs • COX-2 selective NSAIDs • Coxibs are slightly less likely to cause GI problems • Coxibs are likely to be selectively prescribed to patients at increased GI risk • Classic problem of confounding by indication

11. Characteristics of Cohort

12. Patient’s GI Risk Moderate High Low “Marginal Patient” NS NSAID COXIB COXIB COX-2 Preferring Physician NS NSAID NS NSAID COXIB NS NSAID Preferring Physician

13. Estimating Preference • Volume of NSAID prescribing varies considerably among physicians • Our approach: use the type of the last NSAID prescription written by each physician as a measure of current preference • If for last patient, physician wrote a coxib prescription, for the current patient he is classified as a “coxib preferring physician” other he is classified as an “non-selective NSAID preferring physician.”

14. Treatment Treatment = ? Index Patient’s IV is Previous Patient’s Treatment Index Patient Previous Patient Treated with NSAIDs Time

15. Instrument should be related to treatment

16. Instrument should be unrelated to observed patient risk factors

17. IV estimate of the effect of coxib exposure on GI outcome • IV Estimate • E[Y|Z=1]-E[Y|Z=0] -0.21% • ------------------------- = -------- = -0.92% • E[X|Z=1]-E[X|Z=0] 22.8% • Crude • E[Y|X=1]-E[Y|X=0] = +0.03% • After multivariable adjustment • = -0.04%

18. Other examples of preference-based instrument • Clinic, hospital as IV • Johnston SC, J Clin Epi • Schneeweiss, Seeger, Walker NEJM 2008: Aprotinin during CABG • Geographic region as instrument • Wen, J & Kramer J Clin Epi 1997 • Brooks et al, HSR, Breast cancer treatment • Stuckel T, et. al JAMA – Cardiac catheterization • Generally available, but vulnerable to case-mix bias, concomitant treatments associated with the IV

19. Distance to Specialty Provider as IV McClellan, M., B. McNeil and J. Newhouse, JAMA, 1994. "Does More Intensive Treatment of Acute Myocardial Infarction Reduce Mortality?” • Medicare claims data identify admissions for AMI, 1987-91 • Treatment: Cardiac catheterization (marker for aggressive care) • Outcome: Survival to 1 day, 30 days, 90 days, etc. • Instrument: Indicator of whether the hospital nearest to a patient’s residence does catheterizations.

20. Are assumptions valid ? • Is IV associated with treatment? 26.2% get cath if nearest hospital does caths 19.5% get cath if nearest hospital does not do caths • Is IV associated with outcome other than through it effect on treatment? They demonstrated IV is largely unassociated with observed patient characteristics.

21. McClellan, et al. results • Conventional methods - Crude estimate -30% (17% 1-year mortality if catheterized vs. 47%) - OLS estimate is -24%, adjusting for observable risk factors • IV estimator suggest catheterization associated with 10 percentage point reduction in mortality E[Y|Z=1]-E[Y|Z=0] -0.7% ------------------------- = -------- = -10.4% E[X|Z=1]-E[X|Z=0] 6.7% α

22. Other Examples of Distance IVs • Brooks et al -- Effect of dialysis center profit status on survival • McConnell KJ et al -- Treatment of head injuries at level I vs level II trauma centers • Must be used studying a treatment that is dispensed at particular locations • Not applicable to many prescription medications • Treatment must depend on distance

23. Calendar Time as an IV Beta blocker after HF hospitalization and 1-year mortality IV status: After % BB use after HF hospital. IV status: Before Johnston et al. Stat Med 2008 Bias: Secular trends in other things related to the outcome Best used when there is a dramatic shift in practice in a short time period: e.g, changes in guidelines, or safety warnings.

24. IVs can also be created • ‘Randomized encouragement’ designs (Ten Have et al) • Designed delays (McClure M., Dormuth C; work in British Columbia)

25. One-off Instruments • Day of the week of hospital admission as an instrument for waiting time for surgery (Ho et al.) • Surgeons operate only on weekdays and therefore patients admitted on the weekend may have to wait longer for surgical treatment. • Bias: patients admitted on the weekend were different from those admitted on the weekday. • Bias: IV could be independently related to the outcome if other aspects of hospital care that could affect the outcome were different over the weekend.

26. Characteristics of Good Application of IVs • IV should be have theoretical motivation • IV should be strongly associated with treatment • IV should be largely unrelated to patient characteristics • Some consideration should be given to generalizing the estimate • Used in the setting of a large sample

27. Role of Instrumental Variable • IV assumptions are different from those underlying conventional approaches • Makes IV excellent for secondary analysis • Wang et al, NEJM 2005 • Problem arises if methods given different results • IV method deserve primary status if IV is strong&valid, sample size is large, and unmeasured confounding expected to be great

28. Coming soon to the AHRQ website… Practical guide to IV Methods for Comparative Effectiveness Research, by Brookhart, Rassen, and Schneeweiss