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  1. Big Question: We now have detailed, longitudinal medical data on tens of millions of patients. Can we use it to improve healthcare?

  2. Observational Studies • A empirical study in which: • Examples: • smoking and heart disease • vitamin C and cancer survival • DES and vaginal cancer “The objective is to elucidate cause-and-effect relationships in which it is not feasible to use controlled experimentation” • aspirin and mortality • cocaine and birthweight • diet and mortality

  3. BMJ 2010; 341:c4444

  4. Longitudinal Claims Data CELECOXIB MI ROFECOXIB ] ] ] ] ] ] patient 1 M44 ROFECOXIB ROFECOXIB ROFECOXIB MI ] ] ] ] ] ] ] ] M78 patient 2 ROFECOXIB ROFECOXIB MI MI ] ] ] ] ] ] F24 patient 3 ] ] ] ] OLANZAPINE QUETIAPINE

  5. Self Controlled Case Series CV RISK = 1 • assume diagnoses arise according to a non-homogeneous Poisson process CV RISK = 0 VIOXX MI ] ] ] ] ] 493 365 472 547 730 baseline incidence for subject i relative incidence associated with CV risk group 1 relative incidence associated with Vioxx risk level 1 Poisson rate for subject 1, period 1

  6. overall Poisson rate for subject 1: cohort study contribution to the likelihood: conditional likelihood:

  7. Self-Controlled Case Series Method Farrington et al. equivalent multinomial likelihood: regularization => Bayesian approach scale to full database?

  8. Vioxx & MI: SCCS RRsi3 claims database • Bayesian analysis N(0,10) prior + MCMC • Overall: 1.38 (n=11,581) • Male: 1.41 Female: 1.36 • Age >= 80: 1.48 • Male + Age >= 80: 1.68

  9. overall (n=11,581)

  10. males 80 and over (n=440)

  11. June 30, 2000 RR=1.53 Pr(RR>1)=0.92

  12. Dec 31, 2000 RR=1.51 Pr(RR>1)=1.0

  13. Pr(MI) "bad drug" dose more drug…less chance of MI. Bad drug is good???

  14. daily aspirin no daily aspirin Pr(MI) "bad drug" dose bad for aspirin users, bad for non-users! Need a conditional analysis

  15. Causal Inference View • Rubin causal model • Potential outcomes Factual outcome I am a smoker and I get lung cancer Counterfactual outcome If I had not been a smoker, I would not have gotten lung cancer • Define: • Zi: treatment applied to unit i (0=control, 1=treat) • Yi (0) : response for unit i if Zi= 0 • Yi (1) : response for unit i if Zi= 1 • Unit level causal effect: Yi (1) - Yi (0) • Fundamental problem: only see one of these! • Average causal effect: AVEi(Yi (1) - Yi (0))

  16. Confounding and Causality • Confounding is a causal concept • “The association in the combined D+d populations is confounded for the effect in population D”

  17. Why does this happen? • For confounding to occur there must be some characteristics/covariates/conditions that distinguish D from d. • However, the existence of such factors does not in and of itself imply confounding. • For example, D could be males and d females but it could still be the case that b=c.

  18. Stratification can introduce confounding

  19. BMJ 2010; 341:c4444

  20. JAMA 2010; 304(6): 657-663

  21. Randomized trials are better!

  22. Why does randomization work?

  23. The two groups are comparable at baseline • Could do a better job manually matching patients on 18 characteristics listed, but no guarantees for other characteristics • Randomization did a good job without being told what the 18 characteristics were • Chance assignment could create some imbalances but the statistical methods account for this properly

  24. In 10,000 person two-arm trial, probability that a specific binary characteristic splits more unevenly than 48:52 is 10-4 In 10,000 person two-arm trial, probability that a specific binary characteristic splits more unevenly than 46:54 is 10-16

  25. The Hypothesis of No Treatment Effect • In a randomized experiment, can test this hypothesis essentially without making any assumptions at all • “no effect” formally means for each patient the outcome would have been the same regardless of treatment assignment • Test statistic, e.g., proportion (D|TT)-proportion(D|PCI) P=1/6 observed

  26. Back to observational studies…

  27. Overt Bias in Observational Studies “An observational study is biased if treatment and control groups differ prior to treatment in ways that matter for the outcome under study” Overt bias: a bias that can be seen in the data Hidden bias: involves factors not in the data Can adjust for overt bias…

  28. Matched Analysis Using a model with 29 covariates to predict VHA use, we wereable to obtain an accuracy of 88 percent (receiver-operating-characteristiccurve, 0.88) and to match 2265 (91.1 percent) of the VHA patientsto Medicare patients. Before matching, 16 of the 29 covariateshad a standardized difference larger than 10 percent, whereasafter matching, all standardized differences were less than5 percent

  29. Conclusions VHA patients had more coexisting conditions thanMedicare patients. Nevertheless, we found no significant differencein mortality between VHA and Medicare patients, a result thatsuggests a similar quality of care for acute myocardial infarction.

  30. JAMA 2010; 304(6): 657-663

  31. JAMA study design choices • Data source: General Practice Research Database • Study design: Cohort • Inclusion criteria: Age > 40 • Exclusion criteria: Cancer diagnosis in 3 years before index date • Exposed cohort: Patients with >=1 prescription between 1996-2006 • “Unexposed” cohort: 1-to-1 match with exposed cohort • Matched on year of birth, sex, practice • “HR” estimated with Cox proportional hazards model • Time-at-risk: >6mo from index date • Covariates: • Smoking, alcohol, BMI before exposure index date • Hormone therapy, NSAIDs, H2blockers, PPIs • Sensitivity analyses: • Excluding people that were in both exposed and unexposed cohorts • Exclude patients with missing confounders (not reported) • Subgroup analyses: • Low vs. medium vs. high use, based on defined daily dose • Alendronate vs. nitrogen-containing bisphosphonates vs. non-nitrogen-contrainingbisphosphonates

  32. Do these choices matter?

  33. Range of estimates across high-dimensional propensity score inception cohort (HDPS) parameter settings True - False - False + True + Parameter settings explored in OMOP: Washout period (1): 180d Surveillance window (3): 30 days from exposure start; exposure + 30d ; all time from exposure start Covariate eligibility window (3): 30 days prior to exposure, 180, all-time pre-exposure # of confounders (2): 100, 500 covariates used to estimate propensity score Propensity strata (2): 5, 20 strata Analysis strategy (3): Mantel-Haenszel stratification (MH), propensity score adjusted (PS), propensity strata adjusted (PS2) Comparator cohort (2): drugs with same indication, not in same class; most prevalent drug with same indication, not in same class • Each row represents a drug-outcome pair. • The horizontal span reflects the range of point estimates observed across the parameter settings. • Ex. Benzodiazepine-Aplastic anemia: HDPS parameters vary in estimates from RR= 0.76 and 2.70 Relative risk

  34. Range of estimates across univariate self-controlled case series (USCCS) parameter settings True - False - False + True + USCCS Parameter settings explored in OMOP: Condition type (2): first occurrence or all occurrences of outcome Defining exposure time-at-risk: Days from exposure start (2): should we include the drug start index date in the period at risk? Surveillance window (4): 30 d from exposure start Duration of exposure (drug era start through drug era end) Duration of exposure + 30 d Duration of exposure + 60 d Precision of Normal prior (4): 0.5, 0.8, 1, 2 • For Bisphosphonates-GI Ulcer hospitalization, USCCS using incident events, excluding the first day of exposure, and using large prior of 2: • When surveillance window = length of exposure, no association is observed • Adding 30d of time-at-risk to the end of exposure increased to a significant RR=1.14 Relative risk

  35. Fix everything except the database…

  36. Cohort

  37. SCCS

  38. Does this stuff work at all?

  39. Common Data Model OMOP 2010/2011 Research Experiment OMOP Methods Library • Open-source • Standards-based Inceptioncohort Case control Logisticregression • 10 data sources • Claims and EHRs • 200M+ lives • OSIM • 14 methods • Epidemiology designs • Statistical approaches adapted for longitudinal data Positives: 9 Negatives: 44