Measurement and Modeling Issues with Adherence to Pharmacotherapy


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Measurement and Modeling Issues with Adherence to Pharmacotherapy

  1. 1. Measurement and Modeling Issues with Adherence to Pharmacotherapy M. Christopher Roebuck, M.B.A. Director, Health Economics CVS Caremark Teresa B. Gibson, Ph.D. Director, Health Outcomes Thomson Reuters, Healthcare & Science AMCP Educational Conference Workshop (W1) St. Louis, Missouri Thursday, October 14, 2010 08:15-09:30
  2. 2. Outline <ul><li>Measurement Issues: </li></ul><ul><li>Introduction to Adherence </li></ul><ul><li>Calculation of Adherence Measures </li></ul><ul><li>Defining “Adherent”: The 80% Threshold </li></ul><ul><li>Handling Primary Non-Compliance </li></ul><ul><li>Modeling Issues: </li></ul><ul><li>Introduction to Adherence as Key Independent Variable </li></ul><ul><li>Endogeneity/Selection Bias in Observational Studies </li></ul><ul><li>Methods to Address Endogeneity/Selection Bias </li></ul><ul><ul><li>Regression Adjustment </li></ul></ul><ul><ul><li>Propensity Score Matching </li></ul></ul><ul><ul><li>Instrumental Variables </li></ul></ul><ul><ul><li>Fixed Effects </li></ul></ul>
  3. 3. Some Notes <ul><li>While we will largely focus on adherence (compliance), some issues raised will apply to persistence and other utilization patterns measures </li></ul><ul><li>We rely on pharmacy claims data only for measurement, but… </li></ul><ul><li>The presence of Rx fills does not necessarily indicate medication was consumed (in accordance with physician’s orders) </li></ul><ul><li>The absence of an initial Rx fill does not guarantee medication wasn’t prescribed </li></ul><ul><li>The absence of refills doesn’t mean patient wasn’t compliant (physician’s orders, free samples, cash purchase) </li></ul>
  4. 4. Measurement Issues
  5. 5. Introduction <ul><li>“ Drugs don't work in patients who don't take them.” </li></ul><ul><ul><li>C. Everett Koop, M.D. </li></ul></ul><ul><li>Need to measure if and how medications are taken </li></ul><ul><li>Conduct analyses with these measures to answer research questions </li></ul><ul><li>Adherence as a dependent variable: </li></ul><ul><ul><li>What are the drivers of adherence? </li></ul></ul><ul><ul><li>How does pharmacy benefit design impact adherence? </li></ul></ul><ul><ul><li>Determine copay elasticity for Value-Based Insurance Design </li></ul></ul><ul><li>Adherence as an independent variable: </li></ul><ul><ul><li>What is the impact of adherence on adverse health events? </li></ul></ul><ul><ul><li>Does adherence avert hospitalization and provide total healthcare cost savings? </li></ul></ul><ul><ul><li>Are adherent employees more productive? </li></ul></ul>
  6. 6. Terminology and Definitions <ul><li>International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Medication Compliance and Persistence Work Group 1,2 </li></ul><ul><li>Medication compliance (adherence) defined as “the extent to which a patient acts in accordance with the prescribed interval and dose of a dosing regimen.” </li></ul><ul><ul><li>Measured over a period of time </li></ul></ul><ul><ul><li>Reported as a percentage (or proportion) </li></ul></ul><ul><li>Differs from other utilization pattern measures like persistence and gaps </li></ul>
  7. 7. Adherence Calculation <ul><li>Medication Possession Ratio (MPR) </li></ul><ul><li>Can be calculated at drug, class, or condition levels </li></ul><ul><li>Usually conditional on having at least 1 or 2 fills (more on this later) </li></ul><ul><li>Time period (denominator) can be </li></ul><ul><ul><li>variable (e.g., first fill to last fill + days’ supply) </li></ul></ul><ul><ul><li>fixed (e.g., annual) </li></ul></ul><ul><li>Indexed (person-specific windows) or calendar-based </li></ul><ul><li>Since all days of medication supply are counted, values can exceed 1.00 </li></ul>
  8. 8. Adherence Calculation <ul><li>Proportion of Days Covered (PDC) </li></ul><ul><li>Each day with medication on hand counted once, thus, maximum PDC is 1 </li></ul><ul><li>Can be calculated at drug, class, or condition levels </li></ul><ul><li>Usually conditional on having at least 1 or 2 fills (more on this later) </li></ul><ul><li>Time period (denominator) can be </li></ul><ul><ul><li>variable (e.g., first fill to last fill + days’ supply) </li></ul></ul><ul><ul><li>fixed (e.g., annual) </li></ul></ul><ul><li>Indexed (person-specific windows) or calendar-based </li></ul>
  9. 9. Defining “Adherent” <ul><li>MPR/PDC measure adherence, so at what level is a patient “adherent”? </li></ul><ul><li>Threshold of 0.80 is very common, but arbitrary </li></ul><ul><li>Common approaches: </li></ul><ul><ul><li>Retain continuous MPR/PDC, but this assumes linearity of response </li></ul></ul><ul><ul><ul><li>Is a move from 0.20 to 0.30 clinically equivalent to a move from 0.70 to 0.80? </li></ul></ul></ul><ul><ul><li>As independent variable, use linear and squared terms to allow for curvature </li></ul></ul><ul><ul><li>Or categorized </li></ul></ul><ul><li>Preferably, start with theory of adherence based on pharmacological properties </li></ul><ul><li>Could let data speak for themselves—perform nonparametric exploratory analysis </li></ul><ul><li>One technique: STATA’s user-written command locpr 3 </li></ul><ul><ul><li>Semi-parametrically estimates proportion as function of 1 variable, graphs result </li></ul></ul><ul><ul><li>Estimates local linear regression @ 99 percentiles, smoothes results, and plots </li></ul></ul>
  10. 10. Case Study Data 4 <ul><li>Integrated pharmacy & medical claims data on 135,008 patients from 9 employers </li></ul><ul><li>Annual panel dataset of adults continuously eligible from 7/1/05 through 6/30/08 </li></ul><ul><li>With one or more of the following conditions (sample size): </li></ul><ul><ul><li>Hypertension: 112,757 </li></ul></ul><ul><ul><li>Diabetes: 42,080 </li></ul></ul><ul><ul><li>Dyslipidemia: 53,041 </li></ul></ul><ul><li>For each of three 1-year observations, </li></ul><ul><li>Calculated PDC by therapeutic class (TC) </li></ul><ul><li>Rolled up to condition-level as PDC mean, weighted by TC days’ supply </li></ul>
  11. 11. PDC & Hospitalization: Functional Form
  12. 12. PDC & Hospitalization: Functional Form
  13. 13. PDC & Hospitalization: Functional Form
  14. 14. Zero Adherence <ul><li>Standard MPR/PDC measures require 1 or 2 fills (no initial 0s) </li></ul><ul><li>Then, can either assume… </li></ul><ul><ul><li>once on therapy must remain on therapy (subsequent 0 adherence allowed) or </li></ul></ul><ul><ul><li>clocked stopped once discontinued (no subsequent 0 adherence) </li></ul></ul><ul><li>Either approach requires an assumption about the physician’s treatment intent </li></ul><ul><li>Similarly, requiring 1 st fill assumes all patients prescribed medication initiated treatment </li></ul><ul><li>Researcher should consider implications of these assumptions </li></ul><ul><li>What is the population of interest? </li></ul>
  15. 15. Primary Non-Adherence <ul><li>What about patients who receive a prescription but do not fill? </li></ul><ul><li>Primary non-adherence measurable with e-prescribing data and pharmacy claims </li></ul><ul><li>Karter et al. (2009) 5 report primary non-adherence rates for new prescriptions in a diabetic population (in San Francisco Bay area): </li></ul><ul><ul><li>Antihypertensives 3.2%; Cholesterol-lowering 8.5%; Antihyperglycemics 4.0% </li></ul></ul><ul><li>Moreover, they find 16%-22% fill only once in these conditions </li></ul><ul><li>Liberman et al. (2010) 6 find 34% primary non-adherent on dyslipidemics </li></ul><ul><li>Fischer et al. (2010a) 7 published primary non-adherence rates: hypertension (28.4%), hyperlipidemia (28.2%), and diabetes (31.4%) </li></ul><ul><li>And, in an examination of nearly 1 million e-prescriptions, Fischer et al. (2010) 8 estimate primary non-adherence rates of 20-24% for cardiovascular, endocrine, and metabolic agents </li></ul>
  16. 16. Primary Non-Adherence <ul><li>Important for inference, primary non-adherent patients may be a heterogeneous group (e.g., healthier?) </li></ul><ul><li>No measurement alternative if only pharmacy claims data are available </li></ul><ul><li>With medical data on hand, another approach is to assume diagnosed patients should be on therapy (as of diagnosis date) </li></ul><ul><li>Appropriateness of this approach should take into account the condition </li></ul><ul><ul><li>Perhaps ok for congestive heart failure, but maybe not for depression </li></ul></ul><ul><li>Depending on the research question and model, it may be worthwhile to try both approaches; may provide lower and upper bounds effect estimates </li></ul>
  17. 17. Comparing PDC Approaches: Hypertension
  18. 18. Comparing PDC Approaches: Diabetes
  19. 19. Comparing PDC Approaches: Dyslipidemia
  20. 20. PDC & Hospitalization <ul><li>Estimated ordinary least squares models of hospitalization on PDC>=0.80 using two approaches </li></ul><ul><li>Controls include: age, gender, region, Charlson Comorbidity Index, yearly time dummies </li></ul><ul><li>Of course, sample sizes larger when only diagnosis is required </li></ul><ul><li>Coefficients represent impact of optimal adherence on probability of hospitalization (p<0.01 for all) </li></ul><ul><li>Larger reductions when primary non-compliant cohort included for hypertension, but smaller effects in diabetes & dyslipidemia </li></ul>N = 142,944 Coef = -0.017 N = 103,064 Coef = -0.038 Dyslipidemia N = 115,637 Coef = -0.054 N = 83,642 Coef = -0.075 Diabetes N = 314,440 Coef = -0.068 N = 247,375 Coef = -0.059 Hypertension Dx Only Required Rx & Dx Required Condition
  21. 21. Modeling Issues
  22. 22. Medication Adherence and Outcomes <ul><li>Admissions </li></ul><ul><li>ED Visits </li></ul><ul><li>Inpatient Spending </li></ul><ul><li>Medical Spending </li></ul><ul><li>Complications </li></ul><ul><li>Indirect Costs </li></ul>The Value of Medication Adherence Outcome adherent – Outcome non-adherent Medication Adherence Health Outcomes
  23. 23. Value of Medication Adherence <ul><li>Patients </li></ul><ul><ul><li>Health </li></ul></ul><ul><ul><li>Work </li></ul></ul><ul><ul><li>Disease progression </li></ul></ul><ul><li>Payers </li></ul><ul><ul><li>Health benefits </li></ul></ul><ul><ul><li>Productivity </li></ul></ul><ul><ul><li>Spending </li></ul></ul><ul><li>Practice </li></ul><ul><ul><li>Disease progression </li></ul></ul><ul><ul><li>Compliance with therapeutic regimens </li></ul></ul><ul><ul><li>Complication rates </li></ul></ul>
  24. 24. Adherence and Outcomes Randomization relies on law of large numbers to create like comparison groups to compare means, and estimate the effects of adherence Adherent Non-Adherent Outcome (e.g., # hospitalizations)
  25. 25. Adherence and Outcomes With non-randomized data, there may be differences between those who are adherent and non-adherent Adherent Non-Adherent Outcome (e.g., # hospitalizations)
  26. 26. Analytical Approaches <ul><li>Randomized Controlled Trial </li></ul><ul><li>Natural Experiment </li></ul><ul><ul><li>Group assignment according to an external, exogenous event </li></ul></ul><ul><ul><ul><li>Policy change, natural event </li></ul></ul></ul><ul><ul><li>Difference-in-Differences </li></ul></ul><ul><ul><li>Regression Discontinuity </li></ul></ul><ul><li>Instrumental Variables </li></ul><ul><li>Adjustment for Observable Differences </li></ul><ul><ul><li>Regression Adjustment </li></ul></ul><ul><ul><li>Propensity Score Matching </li></ul></ul><ul><ul><li>Fixed Effects </li></ul></ul>
  27. 27. Analytical Approaches <ul><li>In the absence of randomization the effect estimate of adherence on an outcome of interest cannot be interpreted as strictly causal, but correlational </li></ul>
  28. 28. Regression Adjustment <ul><li>Regression-adjusted differences </li></ul><ul><ul><li>A common approach </li></ul></ul><ul><ul><li>Adjusts for differences in observable characteristics </li></ul></ul><ul><ul><li>Y = f(Adherent, X) </li></ul></ul><ul><ul><li>Y = XB + u </li></ul></ul>
  29. 29. Regression Adjustment Example <ul><li>Data Source </li></ul><ul><ul><li>Thomson Reuters MarketScan Database </li></ul></ul><ul><ul><li>N=55,555 patients with Type 2 Diabetes on oral antidiabetic medications </li></ul></ul><ul><ul><li>Adherent is Percent of Days Covered (1=PDC  0.80; 0=PDC < 0.80) </li></ul></ul><ul><ul><li>Outcome is acute myocardial infarction rate (1/0 variable) </li></ul></ul><ul><li>Unadjusted Comparison of Means </li></ul><ul><li>Regression Adjusted Comparison of Means </li></ul><ul><ul><li>Pr(Complications|Adherent,X) = f(Adherent, sociodemographic, health plan, provider, health status) </li></ul></ul><ul><ul><li>Logistic regression </li></ul></ul>
  30. 30. Regression Adjusted Example: Results Rates are similar (p=0.5821) Adjusted OR: 0.861 (p=0.073) Rate of Acute Myocardial Infarction (N=55,555)
  31. 31. Considerations <ul><li>Adherent and nonadherent patients have different characteristics 9,10,11 </li></ul><ul><li>Naïve differences may not accurately represent the actual adherence effect </li></ul><ul><li>Is risk adjustment adequate to remove important biases? </li></ul><ul><li>Selection Bias Issue: Adherence may be related to relevant, but omitted variables </li></ul><ul><ul><li>After adjusting for observed differences between groups, important differences may remain </li></ul></ul><ul><li>Examples of Bias </li></ul><ul><ul><li>Differences in unobserved health status: </li></ul></ul><ul><ul><ul><li>If adherent patients are healthier and have correspondingly lower levels of utilization, then the effects of adherence may be biased upward </li></ul></ul></ul><ul><ul><ul><li>If adherent patients are sicker and have correspondingly higher levels of utilization, then the effects of adherence may be biased downward </li></ul></ul></ul>
  32. 32. Addressing Selection Bias <ul><li>Propensity Score Matching </li></ul><ul><li>Instrumental Variables </li></ul><ul><li>Fixed Effects </li></ul>
  33. 33. Propensity Score Matching <ul><li>Create a matched comparison group of patients who have characteristics that are similar to those in the treated group 12 </li></ul><ul><ul><li>Compare outcomes in treated group to matched comparison group </li></ul></ul><ul><li>Counterfactual </li></ul><ul><ul><ul><li>Estimate the effect on the treated as if they had been untreated (not observed) </li></ul></ul></ul><ul><li>Matching is based on a propensity score </li></ul><ul><ul><li>Matching on age, gender, location,… versus </li></ul></ul><ul><ul><li>Matching on a propensity score </li></ul></ul>
  34. 34. Propensity Score Matching <ul><li>To create the propensity score: </li></ul><ul><li>One observation per individual </li></ul><ul><li>Estimate the propensity score on the Xs (i.e., exposure equation) </li></ul><ul><ul><li>Post-estimation predicted probability of treated (e.g., adherence) vs. untreated (1/0 variable) </li></ul></ul><ul><ul><li>Logit or probit model </li></ul></ul><ul><li>Match based on the predicted propensity score </li></ul><ul><ul><li>Types of matching (examples) </li></ul></ul><ul><ul><ul><li>Nearest neighbor </li></ul></ul></ul><ul><ul><ul><li>Caliper </li></ul></ul></ul><ul><ul><ul><li>Mahalanobis metric </li></ul></ul></ul><ul><ul><ul><li>Kernel </li></ul></ul></ul><ul><li>Compare outcomes </li></ul><ul><ul><li>Unadjusted </li></ul></ul><ul><ul><li>Regression Adjusted </li></ul></ul>
  35. 35. Propensity Score Matching Example <ul><li>Comparison of complication rates between patients adherent to antidiabetic medications (PDC >= 0.80) and patients who are nonadherent to antidiabetic medications </li></ul><ul><li>Exposure equation (logistic regression): </li></ul><ul><li>Pr(Adherent|X) = g(sociodemographics, health plan, provider, health status) </li></ul><ul><li>---> estimate propensity score </li></ul><ul><li>Matching based on propensity score </li></ul><ul><ul><li>N=30,190 </li></ul></ul><ul><ul><li>Sample size is different (was N=55,555) </li></ul></ul><ul><li>Outcome equation (logistic regression): </li></ul><ul><ul><li>Comparison of matched samples </li></ul></ul><ul><ul><ul><li>Adjusted </li></ul></ul></ul><ul><ul><ul><li>Unadjusted </li></ul></ul></ul>
  36. 36. Propensity Score Matching Example: Results Rates are different (p<0.01) Rate of Acute Myocardial Infarction (N=30,190) Adjusted OR: 0.791 (p=0.045)
  37. 37. Propensity Score Matching Caliper Matching Match within common support
  38. 38. Considerations <ul><li>Observables </li></ul><ul><li>A good match </li></ul><ul><ul><li>Comparison of individual characteristics </li></ul></ul><ul><ul><ul><li>For example, age distribution pre- and post- matching </li></ul></ul></ul><ul><ul><li>Reduction in bias </li></ul></ul><ul><ul><li>Which patients were matched in treatment and comparison group? </li></ul></ul><ul><li>Propensity scores as weights </li></ul><ul><li>Dose-response relationships </li></ul><ul><li>Regression adjustment after matching? </li></ul><ul><ul><li>If matching is perfect </li></ul></ul><ul><ul><li>If matching is imperfect </li></ul></ul><ul><li>Hidden bias may remain 13 </li></ul><ul><li>Treatment group and available comparison group are very dissimilar </li></ul><ul><ul><li>Matches may be limited to the overlap in the distribution </li></ul></ul><ul><ul><li>Generalizability and conclusions </li></ul></ul>
  39. 39. Instrumental Variables <ul><li>Y = f(Adherent, X) </li></ul><ul><ul><ul><li>Assume a linear relationship, f=1 </li></ul></ul></ul><ul><li>Instrumental variables (Z) 14,15 </li></ul><ul><ul><ul><li>Are correlated with treatment (adherence), and </li></ul></ul></ul><ul><ul><ul><li>Are uncorrelated with outcome (Y), conditional on treatment (adherence) </li></ul></ul></ul><ul><li>In this context, instrumental variables rely on finding variables (Z) that affect adherence but have no direct effect on outcome (Y) </li></ul><ul><ul><li>Randomization to adherence is an excellent instrument </li></ul></ul><ul><ul><li>In observational studies, need to find good instruments </li></ul></ul><ul><ul><li>Good instruments help isolate the effects of adherence on outcomes </li></ul></ul>
  40. 40. Adherence and Outcomes X Z X: covariates Z: instrumental variables Y=outcome Adherent Outcome (Y) (e.g., # hospitalizations)
  41. 41. Instrumental Variables <ul><li>One of the most common methods is Two-Stage Least Squares (2SLS) </li></ul><ul><li>Y1 =  0 +  1Y2 +  2Z1 +u1 (1) </li></ul><ul><ul><ul><li>Y1 is the outcome </li></ul></ul></ul><ul><ul><ul><li>Y2 is endogenous </li></ul></ul></ul><ul><ul><ul><li>Z1 is a covariate </li></ul></ul></ul><ul><li>Y2 =  0 +  1Z2 +  2Z3 + v1 (2) </li></ul><ul><ul><ul><li>Z2 and Z3 are instrumental variables </li></ul></ul></ul><ul><li>Equivalent to regressing </li></ul><ul><ul><ul><li>Y1 on Y2hat and Z1 (3) </li></ul></ul></ul>
  42. 42. Instrumental Variables Example <ul><li>Comparison of complication rates between patients adherent to antidiabetic medications (PDC >= 0.80) and patients nonadherent to antidiabetic medications </li></ul><ul><li>Data Source: Thomson Reuters MarketScan Database (administrative claims and enrollment data) </li></ul><ul><li>Two-Stage Residual Inclusion Model 11,16 </li></ul><ul><li>1. First Stage (Treatment) </li></ul><ul><li>Pr(Adherent|X,Z) = g(sociodemographics, health plan, provider, health status, benefit design) </li></ul><ul><ul><li>Benefit design: e.g., prescription drug cost-sharing amount </li></ul></ul><ul><li>2. Second Stage (Outcome) </li></ul><ul><li>Pr(Complications|X) = f(Adherent, sociodemographics, health plan, provider, health status, residual from first stage) </li></ul>
  43. 43. Instrumental Variables Example: Results <ul><li>First Stage </li></ul><ul><ul><li>Joint significance of instruments in first stage equation p<0.01 </li></ul></ul><ul><li>Second Stage (Logistic Regression) </li></ul>0.014 0.285 p=0.005 Acute Myocardial Infarction Outcome Test of Exogeneity (p-value) Adjusted Odds Ratio
  44. 44. Instrumental Variables Example: Results Rate of Acute Myocardial Infarction (N=55,555)
  45. 45. Considerations <ul><li>Instruments </li></ul><ul><ul><li>Strong/Weak 17 </li></ul></ul><ul><ul><li>Good/Bad </li></ul></ul><ul><ul><ul><li>“ That Instrument is Lousy!” 18 </li></ul></ul></ul><ul><ul><ul><li>Tests of Instruments </li></ul></ul></ul><ul><ul><ul><ul><li>Significance in the first stage equation </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Theoretical relationship </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Tests of overidentification </li></ul></ul></ul></ul><ul><li>In the reduced form Y=f(X,Z), interpretation of estimated coefficient on Z </li></ul><ul><li>Caution: Use of instrumental variables may affect efficiency </li></ul><ul><ul><li>Standard error estimates </li></ul></ul><ul><li>Examples of instruments </li></ul><ul><ul><li>Distance to a provider of care 19 </li></ul></ul><ul><ul><li>Patient blood type 20 </li></ul></ul>
  46. 46. Fixed Effects <ul><li>Panel data (aka cross-sectional time series; longitudinal; repeated measures) allows for the use of fixed effects modeling </li></ul><ul><li>With two time periods, one can estimate a first-differenced model: </li></ul><ul><li>Notice that first-differencing all variables (dependent and independent) eliminates unobservables ( ) constant across time periods </li></ul><ul><li>Thus, unobservable confounders that would otherwise bias treatment (adherence) effect estimates are also removed </li></ul><ul><li>With more than two time periods, the first-difference model is transformed; instead within-person means are subtracted from each observation (mathematically comparable to first-differencing) </li></ul>
  47. 47. Fixed Effects Example: PDC & Hospitalization <ul><li>Estimated linear fixed effects models of hospitalization on PDC>=0.80, compared to OLS </li></ul><ul><li>Controls include: Charlson Comorbidity Index, yearly time dummies </li></ul><ul><li>Coefficients represent impact of optimal adherence on probability of hospitalization (p<0.01 for all) </li></ul><ul><li>Fixed effects results are smaller in absolute value across three conditions; naïve models would overstate reductions in hospitalization from adherence </li></ul><ul><li>“ Healthy user bias” possible </li></ul>Coef = -0.012 Coef = -0.017 Dyslipidemia Coef = -0.027 Coef = -0.054 Diabetes Coef = -0.041 Coef = -0.068 Hypertension Fixed Effects OLS Condition
  48. 48. Considerations <ul><li>Fixed effects models cannot include covariates constant over time (e.g., gender), although one can interact them with other dynamic variables </li></ul><ul><li>Adequate within-subject variation necessary for identification of effects </li></ul><ul><li>Fixed effects are less efficient than random effects because between-subject variation is “discarded” </li></ul><ul><li>Only time-invariant unobservables are removed as potential confounders; endogeneity from time-varying characteristics could still persist </li></ul><ul><li>Can be combined with IV to assist with remaining endogeneity </li></ul>
  49. 49. Questions? <ul><li>M. Christopher Roebuck, MBA </li></ul><ul><li>Director, Health Economics </li></ul><ul><li>CVS Caremark </li></ul><ul><li>(410) 215-8380 </li></ul><ul><li>[email_address] </li></ul>Teresa B. Gibson, Ph.D. Director, Health Outcomes Thomson Reuters (734) 913-3481 [email_address]
  50. 50. References <ul><li>Cramer, JA; Roy, A; Burrell, A; Fairchild, CJ; Fuldeore, MJ; Ollendorf, DA; Wong, PK. 2008. “Medication Compliance and Persistence: Terminology and Definitions.” Value in Health 11(1):44-47. </li></ul><ul><li>Peterson, AM; Nau, DP; Cramer, JA; Benner, J; Gwadry-Sridhar, F; Nichol, M. 2007. “A Checklist for Medication Compliance and Persistence Studies Using Retrospective Databases.” Value in Health 10(1):3-12. </li></ul><ul><li>Nichols, A. 2008. – LOCPR - user-written STATA command available for download at: </li></ul><ul><li>Roebuck, MC; Liberman, JN; Gemmill-Toyama, M; Brennan, TA. In Review. “Impact of Medication Adherence in Chronic Vascular Disease on Health Services Utilization and Cost.” Health Affairs . </li></ul><ul><li>Karter, AJ; Parker, MM; Moffet, HH; Ahmed, AT; Schmittdiel, JA; Selby, JV. 2009. “New Prescription Medication Gaps: A Comprehensive Measure of Adherence to New Prescriptions.” Health Services Research 44:1640-1661. </li></ul><ul><li>Liberman, JN; Hutchins, DS; Popiel, RG; Patel, MH; Jan, SA; Berger, SE. 2010. “Determinants of Primary Non-Adherence in Asthma Controller and Dyslipidemia Pharmacotherapy.” American Journal of Pharmacy Benefits 2(2): 1-10. </li></ul><ul><li>Fischer, MA; Stedman, MR; Lii, J; Vogeli, C; Shrank, WH; Brookhart, MA; Weissman, JS. 2010a. “Primary Medication Non-Adherence: Analysis of 195,930 Electronic Prescriptions.” Journal of General Internal Medicine 25(4):284-290. </li></ul><ul><li>Fischer, MA; Choudhry, NK; Brill, G; Avorn, J; Schneeweiss, S; Hutchins, D; Liberman, JN; Brennan, TA; Shrank, WH. 2010b. “The Tip of the Iceberg: Rates and Predictors of Medication Non-Adherence.” Brigham and Women’s Hospital-Harvard Medical School, working paper. </li></ul><ul><li>Osterberg, L; Blaschke, T. 2005. “Adherence to Medication.” New England Journal of Medicine 353(5):487-97. </li></ul><ul><li>Gibson, TB; Mark, TL; Axelsen, K; Baser, O; Rublee, DA; McGuigan, KA. 2006. “Impact of Statin Copayments on Adherence and Medical Care Utilization and Expenditures.” American Journal of Managed Care 12 Spec no.:SP11-9. </li></ul>
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