BackgroundObservational burden of illness studies are used in pharmacoepidemiology to address a variety of objectives, including contextualizing the current treatment setting, identifying important treatment gaps, and providing estimates to parameterize economic models. Methodologies such as retrospective chart review may be utilized in settings for which existing datasets are not available or do not include sufficient clinical detail. While specifying the number of charts to be extracted and/or determining whether the number that can feasibly extracted will be clinically meaningful is an important study design consideration, there is a lack of rigorous methods available for sample size calculation in this setting. The objective of this study was to develop recommended sample size calculations for use in such studies.MethodsCalculations for identifying the optimal feasible sample size calculations were derived, for studies characterizing treatment patterns and medical costs, based on the ability to comprehensively observe treatments and maximize precision of resulting 95% confidence intervals. For cost outcomes, if the standard deviation is not known, the coefficient of variation cv can be used as an alternative. A case study of a chart review of advanced melanoma (MELODY) was used to characterize plausible values for cv in a real-world example.ResultsAcross sample sizes, any treatment given with greater than 1% frequency has a high likelihood of being observed. For a sample of size 200, and a treatment given to 5% of the population, the precision of a 95% confidence interval (CI) is expected to be ±0.03. For cost outcomes, for the median cv value observed in the MELODY study (0.72), a sample size of approximately 200 would be required to generate a 95% CI precise to within ±10% of the mean.ConclusionThis study presents a formal guidance on sample size calculations for retrospective burden of illness studies. The approach presented here is methodologically rigorous and designed for practical application in real-world retrospective chart review studies.
Methods of sample size calculation in descriptive retrospective burden of illness studies
K. Johnston,P. Lakzadeh,B. Donato,S. Szabo
Published 2019 in BMC Medical Research Methodology
ABSTRACT
PUBLICATION RECORD
- Publication year
2019
- Venue
BMC Medical Research Methodology
- Publication date
2019-01-09
- Fields of study
Medicine, Computer Science, Economics
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- 95% confidence intervals
Interval estimates used here to express precision around observed treatment frequencies and mean costs.
Aliases: CI
- coefficient of variation
A relative variability measure used as an alternative to standard deviation for cost outcomes when the standard deviation is unknown.
Aliases: cv
- descriptive retrospective burden of illness studies
Descriptive observational studies that use retrospectively collected data to quantify treatment use, costs, and other disease-burden measures.
Aliases: retrospective burden of illness studies, burden of illness studies
- retrospective chart review
A chart abstraction method used when existing datasets lack sufficient clinical detail.
Aliases: chart review
- sample size calculations
Calculations used to determine how many charts to extract in these descriptive studies.
Aliases: sample size calculation
- treatment frequency
The proportion of the population receiving a given treatment in the reviewed data.
- treatment patterns
The distribution of treatments received in the reviewed charts.
REFERENCES
Showing 1-24 of 24 references · Page 1 of 1