Dataset Information

Methods of sample size calculation in descriptive retrospective burden of illness studies.

ABSTRACT:

Background

Observational 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.

Methods

Calculations 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.

Results

Across 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.

Conclusion

This 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.

SUBMITTER: Johnston KM

PROVIDER: S-EPMC6325730 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Methods of sample size calculation in descriptive retrospective burden of illness studies.

Johnston Karissa M KM Lakzadeh Pardis P Donato Bonnie M K BMK Szabo Shelagh M SM

BMC medical research methodology 20190109 1

<h4>Background</h4>Observational 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 extract ...[more]

PMID: 30626343

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Project description:BACKGROUND:Value of information is now recognized as a reference method in the decision process underpinning cost-effectiveness evaluation. The expected value of perfect information (EVPI) is the expected value from completely reducing the uncertainty surrounding the cost-effectiveness of an innovative intervention. Among sample size calculation methods used in cost-effectiveness studies, only one is coherent with this decision framework. It uses a Bayesian approach and requires data of a pre-existing cost-effectiveness study to derive a valid prior EVPI. When evaluating the cost-effectiveness of innovations, no observed prior EVPI is usually available to calculate the sample size. We here propose a sample size calculation method for cost-effectiveness studies, that follows the value of information theory, and, being frequentist, can be based on assumptions if no observed prior EVPI is available. METHODS:The general principle of our method is to define the sampling distribution of the incremental net monetary benefit (?B), or the distribution of ?B that would be observed in a planned cost-effectiveness study of size n. Based on this sampling distribution, the EVPI that would remain at the end of the trial (EVPIn) is estimated. The optimal sample size of the planned cost-effectiveness study is the n for which the cost of including an additional participant becomes equal or higher than the value of the information gathered through this inclusion. RESULTS:Our method is illustrated through four examples. The first one is used to present the method in depth and describe how the sample size may vary according to the parameters' value. The three other examples are used to illustrate in different situations how the sample size may vary according to the ceiling cost-effectiveness ratio, and how it compares with a test statistic-based method. We developed an R package (EBASS) to run these calculations. CONCLUSIONS:Our sample size calculation method follows the value of information theory that is now recommended for analyzing and interpreting cost-effectiveness data, and sets the size of a study that balances its cost and the value of its information.

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Dataset Information

Methods of sample size calculation in descriptive retrospective burden of illness studies.

Background

Methods

Results

Conclusion

Publications

Methods of sample size calculation in descriptive retrospective burden of illness studies.

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