1. ASPIRE Study Procedures and Data Elements, Sources, Uses, and Issues
Thomas Delate, PhD, MS Clinical Pharmacy Research Scientist

2. Learning Objectives ASPIRE
Class 3: Study Procedures and Data Elements, Sources, Uses, and Issues
Describe basics of a study procedure
Illustrate data elements and sources
Characterize methods for identifying study patients/subjects, exposures, and outcomes
Appraise data limitations and means to overcome limitations
Identify factors needed for and to calculate a sample size
A study’s procedures section describes how a study is to be conducted. Mostly about data collection and analysis. This includes both exposures and outcomes. I will point out the basics of this process.
There are myriad types of data and ways to collect data. From using existing administrative sources to directly collecting information from study patients/participants.
Each data collection methodology has limitations that you will need to appreciate and identify ways to address.
An IRB is going to want to know an estimate of how many patients/participants will be included and justification for this amount.

3. Study Procedures: Basic Contents
Objectives & the hypotheses underlying them
Study design
Inclusion & exclusion criteria
Sample size estimation
Data collection & sources
Data analysis
These are the minimum contents of a protocol’s procedures section
Some investigators prefer to include information on how the collected data will be manipulated. For example, creating quartiles of LDL values to be used as a categorical rather than continuous variable.

4. Study Procedures: A “How to” Conduct Your Study
Cookbook - Be specific and clear about what you will be doing
At a minimum, will need to describe:
How patients will be identified - by whom and with which data source(s)
If necessary - how, where, and by whom patients recruited
What, how, and when data will be collected
How data will be analyzed
My preference is for detailed a step-by-step recipe on how the study is to be conducted.
Other investigators prefer to be less specific to maintain an ability to be agile with their methodology.
Either way, anticipate “worse-case” scenarios and what you plan to do if one occurs

5. You Will Need Data For Sample Size/Power Estimation
Patient/Participant Identification
Apply inclusion/exclusion criteria
Recruitment (where applicable)
Group Assignment (independent variable)
Other Exposure Measurement
Patient/Participant descriptions
Assess/Control for potential biases and confounders
Outcome Measurement (dependent variable)
Perform statistical analysis

6. Types of Data
Primary Data – Collected Specifically for Research Purposes
Surveys
Patient/Caregiver
Healthcare Provider/Payor
Key Informant Reports
Diary
Focus Group
Interview
These data can be qualitative (e.g., describing patient perspectives) or quantitative (e.g., counts of inhaler usage).

7. Types of Data (cont.)
Secondary Data Collection - Initially Collected for Non-Research Purposes
Medical Charts
Electronic Medical Record Databases
Billing (Claims) Databases
Registries
Government Databases
Medical Record Abstraction
Internal Administrative Record Electronic Database
Electronic Medical Record (EMR)
Pharmacy Dispensing
Inpatient/Outpatient Claim
External Billing Database
Purchase from a vendor (e.g., IMS)
Registry
Disease Management
Reportable Disease (e.g., Cancer)
Government Database
Birth/Death Certificate
Population-Based Information (e.g., Medical Expenditure Panel Survey)

8. Primary vs. Secondary Data
Primary Data
Can assess both qualitative and quantitative issues
Researcher controls how and what data are collected
Time-consuming and expensive to collect
Potential for incomplete/biased information
Secondary Data
Broader data than a researcher can collect on her/his own
Contains longitudinal information
Required information may not be available
Requires training/ability and specialized technology to access I

9. Data Needs for Prospective Cohort Study
Study Objective: To determine if endogenous 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D levels are associated with all-cause and cardiovascular mortality
Patient Identification
Inclusion
1) Age >=18 years
2) Undergoing coronary angiography
3) Vitamin D level drawn at time of procedure
4) 180-day membership prior to procedure
5) Prescription drug benefit
Group Assignment
Outcomes:
All-Cause Mortality
Cardiovascular Mortality
Study
Outcomes
Lower Vit D
Quartiles
Follow-up
What is good about prospective cohort study is that you can ensure that all patients have required labs drawn, in this instance, Vit D levels before follow-up
Cohort studies evaluate ‘associations’ but can do little to assess ‘causality’, Causality can really only be assessed with experimental studies.
Also, cohort studies are useful if you are determining risks where randomization may not be ethical.
Exposures:
Demographics
Potential Confounders
Upper Vit D
Quartiles
Pre-Period

10. Data Needs for Prospective Cohort Study
Study Objective: To determine if endogenous 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D levels are associated with all-cause and cardiovascular mortality
Patient Identification
Inclusion
1) Age >=18 years
2) Undergoing coronary angiography
3) Vitamin D level drawn at time of procedure
4) 180-day membership prior to procedure
5) Prescription drug benefit
Group Assignment
Outcomes:
All-Cause Mortality
Cardiovascular Mortality
Study
Outcomes
Lower Vit D
Quartiles
Follow-up
What is good about prospective cohort study is that you can ensure that all patients have required labs drawn, in this instance, Vit D levels before follow-up
Cohort studies evaluate ‘associations’ but can do little to assess ‘causality’, Causality can really only be assessed with experimental studies.
Also, cohort studies are useful if you are determining risks where randomization may not be ethical.
Exposures:
Demographics
Potential Confounders
Upper Vit D
Quartiles
Pre-Period

11. Identification of Potential Study Patients
Primary data
Provider recommendation
In-clinic recruitment
Secondary data
Random or Targeted chart review
Administrative data query (e-screening)
Coronary angiography status from procedures (e.g., claims) or registry database
Use ICD-9/ICD-10, Current Procedural Terminology (CPT), or Diagnosis-Related Group (DRG) codes
Age, drug benefit, and membership status from membership database
Vitamin D measurement information from laboratory database
Obtain patient identifiers (e.g., medical record number (MRN))

12. Secondary Data Collection Sources
Electronic Medical Record
Pharmacy
Claims
Registries
Describe some data that are available in each example:
Dispensing data: return to stock, determine adherence
EMR: encounters, lab values
Claims: hospitalizations, ER visits, SNF stays
Registries: Patients with cancer, diabetes

13. Data Needs for Prospective Cohort Study
Study Objective: To determine if endogenous 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D levels are associated with all-cause and cardiovascular mortality
Patient Identification
Inclusion
1) Age >=18 years
2) Undergoing coronary angiography
3) Vitamin D level drawn at time of procedure
4) 180-day membership prior to procedure
5) Prescription drug benefit
Group Assignment
Outcomes:
All-Cause Mortality
Cardiovascular Mortality
Study
Outcomes
Lower Vit D
Quartiles
Follow-up
What is good about prospective cohort study is that you can ensure that all patients have required labs drawn, in this instance, Vit D levels before follow-up
Cohort studies evaluate ‘associations’ but can do little to assess ‘causality’, Causality can really only be assessed with experimental studies.
Also, cohort studies are useful if you are determining risks where randomization may not be ethical.
Exposures:
Demographics
Potential Confounders
Upper Vit D
Quartiles
Pre-Period

14. Data for Group Assignment
Primary data
Phlebotomy
Provider notification of laboratory values
Secondary data
Use patient identifiers and chart review for laboratory values
Vitamin D information from electronic query of laboratory database
Use patient identifiers and Logical Observation Identifiers Names and Codes (LOINC), CPT, or internal laboratory codes
Determine quartiles of Vitamin D levels
1st to 25th percentile vs. 75th to 100th percentile

15. Data Needs for Prospective Cohort Study
Study Objective: To determine if endogenous 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D levels are associated with all-cause and cardiovascular mortality
Patient Identification
Inclusion
1) Age >=18 years
2) Undergoing coronary angiography
3) Vitamin D level drawn at time of procedure
4) 180-day membership prior to procedure
5) Prescription drug benefit
Group Assignment
Outcomes:
All-Cause Mortality
Cardiovascular Mortality
Study
Outcomes
Lower Vit D
Quartiles
Follow-up
What is good about prospective cohort study is that you can ensure that all patients have required labs drawn, in this instance, Vit D levels before follow-up
Cohort studies evaluate ‘associations’ but can do little to assess ‘causality’, Causality can really only be assessed with experimental studies.
Also, cohort studies are useful if you are determining risks where randomization may not be ethical.
Exposures:
Demographics
Potential Confounders
Upper Vit D
Quartiles
Pre-Period

16. Exposure Data What are critical exposures to capture? Primary data
Are exposures occurring before and/or after group assignment?
Primary data
Provider/Patient/Caregiver supplied
Secondary data
Use patient identifiers and chart review for diagnoses, procedures, medications use, and sociodemographic information
Use patient identifiers and diagnoses, procedures, medications use, and sociodemographic information for electronic queries of administrative databases
Use ICD-9/ICD-10, CPT, DRG, Healthcare Common Procedure Coding System (HCPCS) codes
Geocoded Census data

17. Data Needs for Prospective Cohort Study
Study Objective: To determine if endogenous 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D levels are associated with all-cause and cardiovascular mortality
Patient Identification
Inclusion
1) Age >=18 years
2) Undergoing coronary angiography
3) Vitamin D level drawn at time of procedure
4) 180-day membership prior to procedure
5) Prescription drug benefit
Group Assignment
Outcomes:
All-Cause Mortality
Cardiovascular Mortality
Study
Outcomes
Lower Vit D
Quartiles
Follow-up
What is good about prospective cohort study is that you can ensure that all patients have required labs drawn, in this instance, Vit D levels before follow-up
Cohort studies evaluate ‘associations’ but can do little to assess ‘causality’, Causality can really only be assessed with experimental studies.
Also, cohort studies are useful if you are determining risks where randomization may not be ethical.
Exposures:
Demographics
Potential Confounders
Upper Vit D
Quartiles
Pre-Period

18. Outcome Data Primary data Secondary data Provider/Caregiver supplied
Use patient identifiers and chart review for mortality and cause of death (COD) information
Generally unavailable for patients who terminated membership prior to dying
Use patient identifiers to obtain mortality information electronic queries of membership database
Use patient identifiers (e.g., SSN, name & address) to obtain mortality and COD information from external source(s) (e.g., SS Administration, state health department, death-record.com)

19. Data Limitations – Confounding and Bias
A systematic error in the design, conduct, or analysis of a study
Can be differential (affects only one group) or systematic (affects all groups)
Apprehensions about these are common with non-randomized studies
Methodologies are available to temper concerns of their impact on a study’s findings

20. Data Limitations – Selection Bias
Can occur with non-randomized assignment to groups
Suspected in prospective and retrospective cohort studies when an intervention (e.g., novel pharmacy service) is being investigated
Can confound the relationship between group assignment and study outcome
For example, sicker/healthier patients may be more likely to receive a medication
When randomization is not practical, patient matching can be used to address
Propensity score based on likelihood of receiving medication modeled with logistic regression using factors related to intervention
Matching of eligible intervention and control patients on propensity score and/or other relevant factors (e.g., age, sex, diagnoses, lab values, seasonality)

21. Data Limitations – Measurement Bias
Can occur when exposure/outcome variable is measured/reported inaccurately
For example, suspected with using non-validated questions in a survey, diagnosis codes with low positive predictive ability, non-validated data sources (e.g., laboratory values, infusions, deaths)
Can result in the misclassification of study patients on an exposure/outcome
For example, mixing up the codes for 25-hydroxyvitamin D & 1,25-dihydroxyvitamin D when extracting laboratory values electronically
Attention to detail in the study procedures can prevent this bias
Review codes to used carefully, review programming code for errors, chart a review a sample of patients to confirm exposure/outcome, use only validated codes & datasets

22. Data Limitations – Confounding
Can occur when an extraneous variable correlates with both the dependent variable and the independent variable
Can result in a mistaken estimate of an exposure’s effect on the risk of disease
When randomization is not practical, can address confounding by
Specification
Exclude patients who exhibit/possess confounding factor (e.g., smokers)
Matching
Match patients on the confounding factor (e.g., match smokers to smokers)
Multivariate regression analysis
Adjustment of the effect of the confounding factors
Stratification
Perform separate analyses on patients who do and do not exhibit/possess confounding factor

23. Sample Size and Power
Sample Size - The number of patients needed to detect a statistically significant difference, if one exists, between groups
Power - The probability to detect a statistically significant difference, if one exists, between groups with the sample size available
These are used to assess if there are sufficient numbers of patients obtainable to conduct feasibly a study so as to provide conclusive answers to the study questions
Conversely will help avoid over enrollment, which can be costly and unethical

24. Sample Size Estimation
Determine the type of response variable you will have
Generally, response variables fall into one of three categories:
Dichotomous (e.g., yes/no, survive/die)
Event rates (%) compared between the intervention and control groups
Continuous (e.g., blood pressure, $, cholesterol level)
Estimated mean (or medians) in the intervention group compared to the control group
Time to failure (e.g., time to death)
Hazard ratio is determined between the intervention and control groups
Can also assess non-inferiority or other measures of equivalence

25. Sample Size: Key Concepts
Hypothesis Testing
Based on the hypothesis you developed with study objectives
H0: No difference in event rates between groups (null hypothesis)
Null hypothesis assumed to be true until proven otherwise
Your goal in conducting the study is to accept or reject the null
No difference between groups, you will accept the H0
Difference between groups, you will reject the H0 and accept the alternative (Ha)

26. Sample Size Estimation
Factors included in sample size:
Alpha
Usually α = 0.05
Beta
β=0.20 (1- β = power)
Expected Difference
Proportional or mean
Standard deviation (continuous outcomes only)
A priori vs. post-hoc

27. On-line Sample Size Calculators
For Studies:
For Surveys:
Try this out for your study