ASPIRE Workshop 4: Study Procedures and Data Elements, Sources, Uses, and Issues Kari L Olson, BSc(Pharm), Pharm D, FCCP, BCPS

– Example study data needs – Review source(s) for data elements – Identify the elements needed to calculate the sample size/power for 1 o outcome – Sample size calculation example – Review sources of and options for addressing bias 2 Outline

Example Study Review Study Question: Among patients with DM eligible for statin therapy, does an intervention involving a letter, a pre-ordered statin prescription, and pharmacist counseling increase statin initiation compared to no intervention? 1 o Outcome: Compare statin-start rate (i.e., purchase of a statin RX within 3 months after mailing date) between groups 2 o Outcomes : Compare statin persistence rate (i.e., statin purchase 1 year after mailing date +/- 45 days) between groups Compare abnormal CK (>600) or ALT (>200) rate (i.e., at least one abnormal lab result within 6 months after mailing date) between groups Study Design: Quasi-experimental

Example Study Review Inclusion Criteria: KPCO member Age years In DM registry Total Cholesterol > 135 mg/dL No statin purchase in past year Exclusion Criteria: ALT > 60 or SCr > 2.0 in past year CK > 2x ULN in past 2 years Fibrate or cyclosporine Rx purchase w/in past 90 days

Data Sources DM registry Membership database (date of birth, membership status) Laboratory database (TC, ALT, SCr, CK) Pharmacy database (statins, fibrates, cyclosporine)

Study Sample Size How many letters need to be sent to assess whether the intervention works? Need to know: Estimated % of statin starts without intervention Estimated % of statin starts with intervention (difference or ratio = effect size) Desired power (1-β) Significance level (α)

Study Sample Size Estimated % of statin starts without intervention: 10% Estimated % of statin starts with intervention: 20% Desired power (1-β): 80% Significance level (α): 0.05

Study Sample Size Elements two-proportions

W hat happens to the required sample size as the group difference increases? A.The required sample size increases B.The required sample size decreases C.There is no association between the required sample size and the reference proportion D.It is data dependent on whether the sample size increases or decreases

W hat happens to the required sample size as the group difference increases? A.The required sample size increases B.The required sample size decreases C.There is no association between the required sample size and the reference proportion D.It is data dependent on whether the sample size increases or decreases

What is the relationship between effect size and required sample size, with power and alpha remaining constant? A.The larger the effect size, the larger the required sample size B.The larger the effect size, the smaller the required sample size C.There is no association between the effect size and sample size D.It is data dependent whether the sample size increases or decreases 11| © 2011 Kaiser Foundation Health Plan, Inc. For internal use only.February 14, 2016

What is the relationship between effect size and required sample size, with power and alpha remaining constant? A.The larger the effect size, the larger the required sample size B.The larger the effect size, the smaller the required sample size C.There is no association between the effect size and sample size D.It is data dependent whether the sample size increases or decreases 12| © 2011 Kaiser Foundation Health Plan, Inc. For internal use only.February 14, 2016

Addressing Potential Study Bias With a quasi-experimental (i.e., non-randomized) study design, groups may be non-equivalent Sicker/healthier -or- more motivated/less motivated patients are over represented in one group (selection bias) Factors affecting both the exposure and outcome are present (confounding) Exposures/outcomes are not measured accurately (misclassification) How can we tell if groups are non-equivalent and if so, how do we address non-equivalence?

Addressing Potential Selection Bias Determine if patients who received the intervention were the similar to those who did not Calculate comorbidity/disease burden scores from historical patients and compare these values between clinics Compare patient characteristics between days of the week If not similar 1) Use clinics where patients are similar 2) Match patients on characteristic(s) 3) Stratified analysis

Addressing Potential Confounding Assess (based on literature, provider input, own knowledge) what factors could affect the 1 o outcome and compare between groups during analysis phase For example Distance from pharmacy Age Insulin use Use these factors in multivariate regression(s)

Addressing Potential Misclassification Can outcomes, characteristics, potential confounders be measured and are they accurate? Medication purchase from a non-network pharmacy Lab result not present Lab measurement technique changed so values are reported differently over time Need valid and reliable data sources for both groups

17| © 2011 Kaiser Foundation Health Plan, Inc. For internal use only.February 14, 2016