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Approaches to Optimize Medication Data Analysis in Clinical Cohort Studies.
Matthew S Duprey1, John W Devlin1, Becky A Briesacher1
1Department of Pharmacy and Health Systems Sciences, Bouve College of Health Sciences, Northeastern University, Boston, Massachusetts, USA.
Journal of the American Geriatrics Society
|October 1, 2020
Summary
This study offers guidance on medication data analysis in clinical cohorts of older adults. It highlights varying methods for medication classification and analysis, emphasizing the need for validated approaches in pharmacoepidemiologic research.
Area of Science:
- Gerontology
- Pharmacoepidemiology
- Clinical Research Methodology
Background:
- Clinical cohorts of older adults are valuable for understanding medication risks and benefits.
- Methods for managing medication data in these cohorts are often unclear and lack validation.
- Existing pharmacoepidemiologic methods may not be optimally suited for smaller clinical datasets.
Purpose of the Study:
- To provide guidance on methodological tools for analyzing medication data in clinical cohort studies of older adults.
- To assess the appropriateness and validation of methods for characterizing pharmacoepidemiologic data.
- To inform researchers on best practices for medication risk and outcome assessment in aging populations.
Main Methods:
- Utilized the Successful Aging After Elective Surgery (SAGES) prospective cohort.
- Identified, reviewed, and analyzed methods for medication characterization within the cohort.
- Distinguished methods based on their appropriateness and validation for pharmacoepidemiologic data analysis.
Main Results:
- Medication coding methods vary in preference and validation; American Hospital Formulary System is not universally preferred.
- Equivalent dosing scales (e.g., morphine equivalents) are preferred over multiclass scales.
- Aggregation of medications within the same class is established, but optimal aggregation levels are unclear.
- Use of validated scales for structurally dissimilar medications (e.g., anticholinergics) requires caution due to lack of consensus.
- Directed acyclic graphs are accepted for conceptualizing confounders; modeling should use evidence-based variable selection.
Conclusions:
- Methods for classifying and analyzing medication data in clinical cohort studies differ in rigor and validation.
- The SAGES cohort illustrates the variability in medication data analysis methods.
- Further research is needed to establish consensus on optimal methods for medication data analysis in clinical cohorts.
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