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## Big Question :

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**Big Question:**We now have detailed, longitudinal medical data on tens of millions of patients. Can we use it to improve healthcare?**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**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**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**overall Poisson rate for subject 1:**cohort study contribution to the likelihood: conditional likelihood:**Self-Controlled Case Series Method**Farrington et al. equivalent multinomial likelihood: regularization => Bayesian approach scale to full database?**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**Pr(MI)**"bad drug" dose more drug…less chance of MI. Bad drug is good???**daily aspirin**no daily aspirin Pr(MI) "bad drug" dose bad for aspirin users, bad for non-users! Need a conditional analysis**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))**Confounding and Causality**• Confounding is a causal concept • “The association in the combined D+d populations is confounded for the effect in population D”**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.**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**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**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**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…**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**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.**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**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**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**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