Sample size estimation in diagnostic test studies of biomedical informatics

By Karimollah Hajian-Tilaki. Journal of Biomedical Informatics, 2014

918 citations recorded by the source corpus.

OBJECTIVES This review provided a conceptual framework of sample size calculations in the studies of diagnostic test accuracy in various conditions and test outcomes. METHODS The formulae of sample size calculations for estimation of adequate sensitivity/specificity, likelihood ratio and AUC as an overall index of accuracy and also for testing in single modality and comparing two diagnostic tasks have been presented for desired confidence interval. RESULTS The required sample sizes were calculated and tabulated with different levels of accuracies and marginal errors with 95% confidence level for estimating and for various effect sizes with 80% power for purpose of testing as well. The results show how sample size is varied with accuracy index and effect size of interest. CONCLUSION This would help the clinicians when designing diagnostic test studies that an adequate sample size is chosen based on statistical principles in order to guarantee the reliability of study.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/596c4b9e0b7817120acb917cff4728fab9f95ca8

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Citations in the corpus

• Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies (2020, background): “In addition, considering the variety of the research types and characteristics, it should be noted that a great number of calculations will be required with too many variables (Table 1) (16,24-30).”
• Predicting accuracy on large datasets from smaller pilot data (2018, methodology): “The power-law models used in this paper have been investigated many times in prior literature (Haussler et al., 1996; Mukherjee et al., 2003; Figueroa et al., 2012; Beleites et al., 2013; Hajian-Tilaki, 2014; Cho et al., 2015).”