Bottleneck analysis: Simple prediction of the precision of a planned case–control or cohort study based on healthcare registers

Jesper Hallas*, Morten Rix Hansen, Anton Pottegård, Henrik Støvring

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Purpose: In pharmacoepidemiological studies, the precision of effect estimates usually depends on the lowest number in the underlying two by two table. We denote this the “bottleneck count” (BNC). We describe how to translate the BNC into an achievable precision and provide empirical examples. Methods: First, we derive a theoretical prediction of the precision in a study where only the BNC determines precision. As an illustration, we calculated the expected precision of a null-effect study on retinoids and peptic ulcer bleeding, expressed as the upper/lower confidence limit ratio (ULCLR). Finally, we reviewed 126 effect estimates from the literature, analyzing the relationship between the predicted and achieved precision. Results: The log–log transformed ULCLR was shown to be a simple linear function of log(BNC). The expected annual number of retinoid-users experiencing a peptic ulcer bleeding was 9.8, yielding an estimated ULCLR for a 1-year study of 3.84. The literature review showed an inverse linear relationship between the logarithmic BNC and the log–log transformed ULCLR, which was largely independent of study design, effect measure and category of BNC. Achieved precision deviated little from predictions but was usually lower than predicted, particularly with low BNC. Conclusion: The precision of a study can be predicted simply and with good accuracy from the BNC, which is useful for determining whether a study is worth pursuing or not.

Original languageEnglish
JournalPharmacoepidemiology and Drug Safety
Volume30
Issue number5
Pages (from-to)619-625
ISSN1053-8569
DOIs
Publication statusPublished - May 2021

Keywords

  • case–control studies
  • cohort studies
  • pharmacoepidemiological databases
  • statistical precision

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