1. ASPIRE Workshop 4: Study Procedures and Data Elements, Sources, Uses, and Issues
Thomas Delate, PhD, MS

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

3. 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?
1o Outcome: Compare statin-start rate (i.e., purchase of a statin RX within 3 months after mailing date) between groups
2o 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

4. Example Study Review Inclusion Criteria: KPCO member Age 40-80 years
Diabetes diagnosis
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

5. Data Sources
Report of patients from the electronic health record (EHR) who have diabetes
Membership in EHR (date of birth, membership status)
Laboratory results from EHR (TC, ALT, SCr, CK)
Pharmacy dispensings from EHR (statins, fibrates, cyclosporine)

6. 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)

7. 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

8. Study Sample Size Elements

9. What happens to the required sample size as the group difference increases?
The required sample size increases
The required sample size decreases
There is no association between the required sample size and the reference proportion
It is data dependent on whether the sample size increases or decreases
Answer: B

10. What happens to the required sample size as the group difference increases?
The required sample size increases
The required sample size decreases
There is no association between the required sample size and the reference proportion
It is data dependent on whether the sample size increases or decreases
Answer: B

11. What is the relationship between effect size and required sample size, with power and alpha remaining constant?
The larger the effect size, the larger the required sample size
The larger the effect size, the smaller the required sample size
There is no association between the effect size and sample size
It is data dependent whether the sample size increases or decreases
Answer: B
August 26, 2018
| © 2011 Kaiser Foundation Health Plan, Inc. For internal use only.

12. What is the relationship between effect size and required sample size, with power and alpha remaining constant?
The larger the effect size, the larger the required sample size
The larger the effect size, the smaller the required sample size
There is no association between the effect size and sample size
It is data dependent whether the sample size increases or decreases
Answer: B
August 26, 2018
| © 2011 Kaiser Foundation Health Plan, Inc. For internal use only.

13. 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?

14. 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
Example: Patients residing in lower down-town may be sicker than those in suburban Highlands Ranch

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

16. 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. http://www.ASPIREKPCO.WEEBLY.COM August 26, 2018
| © 2011 Kaiser Foundation Health Plan, Inc. For internal use only.