Supplementing Claims Data with Outpatient Lab Test Results to Improve Confounding Control

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1 Supplementing Claims Data with Outpatient Lab Test Results to Improve Confounding Control Sebastian Schneeweiss, MD, ScD Professor of Medicine and Epidemiology Division of Pharmacoepidemiology and Pharmacoeconomics, Dept of Medicine, Brigham & Women s Hospital/ Harvard Medical School 1

2 Potential conflicts of interest v PI, Brigham & Women s Hospital DEcIDE Center for Comparative Effectiveness Research (AHRQ) v PI, DEcIDE Methods Center (AHRQ) v Co-Chair, Methods Core of the Mini Sentinel System (FDA) v Member, national PCORI Methods Committee v No paid consulting or speaker fees from pharmaceutical manufacturers v Consulting in past year: WHISCON LLC, Booz&Co, Aetion Inc. v Investigator-initiated research grants to the Brigham from Pfizer, Novartis, Boehringer-Ingelheim v Multiple grants from NIH 2

3 Topics v Why combing claims data with clinical data? v Expectations and conceptual challenges v The reality in US healthcare data Missing data and patterns of missingness Estimation with missing data: validity vs. generalizability v Conclusions and future developments 3

4 Goal: Improved confounding ctrl. Patient factors become confounders (C) if they are associated with treatment choice and are also independent predictors of the outcome: Randomization C Severity Prognosis Comorbidity Trt Outcome 4

5 Secondary Healthcare Databases Claims data describe the sociology of health care and its recording practice in light of economic interests Schneeweiss J Clin Epi

6 Electronic health care information in each Center Constant flow of data with little delay and at low cost Millions of patients with defined person time denominator Data reflect routine care Generalizable to large population segments HIPAA compliance protects patient privacy Claims Data Member ID Plan Gender Age Dates of Eligibility Member ID Prescribing physician Drug dispensed (NDC) Quantity and date dispensed Drug strength Days supply Dollar amounts Member ID Physician or Facility identifier Procedures (CPT-4, revenue codes, ICD-9) Diagnosis (ICD-9- CM, DRG) Admission and discharge dates Date and place of service Dollar amounts Supplemental Data Member ID Lab Test Name Result Member ID Income Net Worth Education Race & Ethnicity Life Stage Life Style Indicators Member ID Subspecialty notes Endoscopy reports Histology reports Radiology reports Free text notes Administrative Data Pharmacy Claims Data Physician and Facility Claims Data Lab Test Results Data Consumer Elements Electronic Medical Records Computerized Linked Longitudinal Dataset 6

7 A Preferred Data Structure for fast, improved CER Example: Older adults using Medicare data Medicare Part A: Hospitaliza3ons Medicare Part B: Medical services Medicare Part D: Pharmacy dispensings Laboratory test results data Medicare Current Beneficiary Survey plus+ Ongoing disease registries, e.g. cancer registry, RA registry New study- specific registries

8 Lets take a moment to think about missing data in secondary data bases v Claims data: A record for a condition is identified in the database -> coded as condition is present No record identified -> we assume the condition is absent Every patient has a 0 or a 1 for that condition, i.e. there is no missing data! However, a missing data problem is translated into a misclassification problem Misclassified confounder variables lead to inadequate adjustment for that variable (if this is differential then additional bias may occur) 8

9 No missing data in claims data but misclassification 9

10 Missing data in secondary data v EHR data: For binary data the same approach as for claims data can be utilized by setting missing values to 0 at least you will not loose subjects b/c of missing data For metric variables imputing a 0 value is often inadequate Other strategies need to be applied This is further complicated when requiring adjustment for multiple covariates in a non-parsimonious PS model this is frequently desired for maximum confounding control. 10

11 Secondary data: Hb A1c, LDL, HDL, BMI Hb A1c LDL HDL Patients with all variables of interest available BMI 11

12 Missing data in primary data v The investigator is in control of measurements v Depending on resources All measurements that are deemed necessary can be made and The measurements can be made in all subjects At defined points in time 12

13 Primary data: Hb A1c, LDL, HDL, BMI HDL LDL BMI Hb A1c 13

14 Some examples 14

15 Cohort expl. 1: Unintended effects of statins Data source: QResearch EMR system, England & Wales H-C C and CC, BMJ

16 Cohort expl. 2: Coxibs vs. nsnsaids and GI toxicity Data source: CPRD EMR system, UK TS et al., PDS

17 Hemoglobin availability depending on colorectal cancer Case/Control status Patients with newly diagnosed colorectal cancer were 2 times more likely to have a hemoglobin lab test result available in the year leading up to the diagnosis. Strongly selective nature of ordering hemoglobin lab tests. Data source: THIN EMR system, UK HW et al., BrJCancer

18 How should we deal with such missing data? v Restriction to complete cases 18

19 19

20 How should we deal with such missing data? v Restriction to complete data v Imputation Single Multiple Fixed 20

21 Toh S. et al: Coxib vs. nsnsaid and GI tox v Very limited imbalance in BMI, alcohol use, smoking status in this example study v Compete case analysis is a selected sample v Without meaningful confounding by the factors with missing values results from different imputation method are similar 21

22 How should we deal with such missing data? v Restriction to complete data v Imputation Single Multiple Fixed v Inverse probability of missing weighting 22

23 Quality of Imputation depends on v Our ability to predict missing values v The proportion of missing values per variable v So, how well are we doing in predicting? v A case study in a typical U.S. healthcare data environment Large longitudinal claims data bases A, M, O, H (30-80M) Linked to data from 2 nationally operating outpatient lab test provider chains About 30% of patients have at least 1 lab test result 23

24 24

25 Set-up for imputation study: Vytorin vs. high-intensity statin alone, AT analysis 25

26 Vytorin vs. high-intensity statin therapy 26

27 Lab tests ordered and duration of the covariate assessment period 27

28 Lab tests ordered 28

29 Lab tests ordered HB A1c 29

30 Lab tests available 30

31 CV predictors 31

32 Region 32

33 Associates of outpatient lab test ordering in 703,000 starters of statin therapy (MV Log Reg) 33

34 Text here 34

35 Text here 35

36 Results for MI hospitalization 1 (564,208) v Note very short real world treatment episodes v Follow-up started 1 month after treatment initiation v Analysis stratified by PS decile after 2.5% trimming on each end. 36

37 Results for MI hospitalization 2 (564,208) 37

38 EHR data: Embrace the Chaos v 1,750 EHR products in US * v American National standard E 1384 >100 pages of definitions and rules However, no real standard for EHR systems Have you ever worked with >1 HER DB? People will tell you more anecdotes than they give you useful data. v Missing data Usually highly informative v Potential solution: identify word stems and empirically determine their value for confounding adjustment * Mandel, Kohane NEJM

39 Performance of EHR word stem adjustment 1 Word: leukocytosi oxycontin haptic extracrani scleral splenomengali valium 2 Words: site cervix categori within specimen categori peripher edema maxillari siuns differenti diagnosi 3 Words: specimen site cervix site cervix endocervix categori within normal impress ct abdomen or 3 view white female a exam ct abdomen Rassen et al.

40 Conclusions v We need a much better understanding of the patterns of missing information in EHR v We need better methods that can deal with missing information that Affects multiple variables of interest Is substantial in quantity Is strongly informed by health state v The value of EHR for improved confounding adjustment is currently overrated and needs more work for prime time. 40

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