Journal of Preventive Medicine and Public Health

  • 제57권5호
  • /
  • Pages.499-507
  • /
  • 2024
  • /
  • 1975-8375(pISSN)
  • /
  • 2233-4521(eISSN)
대한예방의학회 (The Korean Society for Preventive Medicine)
권호 표지
DOI QR코드

DOI QR Code

A Comparison of Green, Delta, and Monte Carlo Methods to Select an Optimal Approach for Calculating the 95% Confidence Interval of the Population-attributable Fraction: Guidance for Epidemiological Research

  • Sangjun Lee (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Sungji Moon (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Kyungsik Kim (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Soseul Sung (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Youjin Hong (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Woojin Lim (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Sue K. Park (Department of Preventive Medicine, Seoul National University College of Medicine)
  • 투고 : 2024.05.29
  • 심사 : 2024.08.28
  • 발행 : 2024.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the "GDM-PAF CI Explorer," was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland's method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions: This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

키워드

과제정보

This study was conducted using a core database of a cohort study provided by the Korean Genome and Epidemiology Study (KoGES), Korea National Institute of Health, Korea Disease Control and Prevention Agency, and a cohort study based on the Korea National Health and Nutrition Examination Survey (KNHANES), Korea Disease Control and Prevention Agency (KDCA), and customized cohort databases provided by the National Health Insurance Service (NHIS-2019-1-495, NHIS-2020-1-164). The prevalence rates of risk factors were used from data provided by the Korea National Institute of Health (KNIH), KDCA, and the Occupational Safety and Health Research Institute (OSHRI), Korea Occupational Safety and Health Agency (KOSHA), and the Korean Statistical Information Service (KOSIS). The incidence and mortality rates of cancers were used from data provided by the Cancer Registration Statistics, Korea National Cancer Center (KNCC), and the KOSIS.

참고문헌

  1. Newson RB. Attributable and unattributable risks and fractions and other scenario comparisons. Stata J 2013;13(4):672-698. https://doi.org/10.1177/1536867X1301300402
  2. Mansournia MA, Altman DG. Population attributable fraction. BMJ 2018;360:k757. https://doi.org/10.1136/bmj.k757
  3. Hanley JA. A heuristic approach to the formulas for population attributable fraction. J Epidemiol Community Health 2001;55(7):508-514. https://doi.org/10.1136/jech.55.7.508
  4. Benichou J. A review of adjusted estimators of attributable risk. Stat Methods Med Res 2001;10(3):195-216. https://doi.org/10.1177/096228020101000303
  5. Rockhill B, Newman B, Weinberg C. Use and misuse of population attributable fractions. Am J Public Health 1998;88(1):15-19. https://doi.org/10.2105/AJPH.88.1.15
  6. Bruzzi P, Green SB, Byar DP, Brinton LA, Schairer C. Estimating the population attributable risk for multiple risk factors using case-control data. Am J Epidemiol 1985;122(5):904-914. https://doi.org/10.1093/oxfordjournals.aje.a114174
  7. Greenland S, Drescher K. Maximum likelihood estimation of the attributable fraction from logistic models. Biometrics 1993;49(3):865-872. https://doi.org/10.2307/2532206
  8. Flegal KM, Graubard BI, Williamson DF. Methods of calculating deaths attributable to obesity. Am J Epidemiol 2004;160(4):331-338. https://doi.org/10.1093/aje/kwh222
  9. Oehlert GW. A note on the delta method. Am Stat 1992;46(1):27-29. https://doi.org/10.1080/00031305.1992.10475842
  10. Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. Am J Epidemiol 1987;125(5):761-768. https://doi.org/10.1093/oxfordjournals.aje.a114593
  11. Khosravi A, Nazemipour M, Shinozaki T, Mansournia MA. Population attributable fraction in textbooks: time to revise. Glob Epidemiol 2021;3:100062. https://doi.org/10.1016/j.gloepi.2021.100062
  12. Laaksonen MA, Harkanen T, Knekt P, Virtala E, Oja H. Estimation of population attributable fraction (PAF) for disease occurrence in a cohort study design. Stat Med 2010;29(7-8):860-874. https://doi.org/10.1002/sim.3792
  13. Greenland S. The effect of misclassification in the presence of covariates. Am J Epidemiol 1980;112(4):564-569. https://doi.org/10.1093/oxfordjournals.aje.a113025
  14. Miettinen OS. Proportion of disease caused or prevented by a given exposure, trait or intervention. Am J Epidemiol 1974;99(5):325-332. https://doi.org/10.1093/oxfordjournals.aje.a121617
  15. Hong Y, Lee S, Moon S, Sung S, Lim W, Kim K, et al. Projection of cancer incidence and mortality from 2020 to 2035 in the Korean population aged 20 years and older. J Prev Med Public Health 2022;55(6):529-538. https://doi.org/10.3961/jpmph.22.128
  16. Jones PA, Friedman MA. Propagating bias and precision errors using the perturbation method. ISA Trans 1990;29(4):71-77. https://doi.org/10.1016/0019-0578(90)90044-L
  17. Pannell DJ. Sensitivity analysis of normative economic models: theoretical framework and practical strategies. Agric Econ 1993;16(2):139-152. https://doi.org/10.1111/j.1574-0862.1997.tb00449.x
  18. Lee S, Ko KP, Lee JE, Kim I, Jee SH, Shin A, et al. The Korea cohort consortium: the future of pooling cohort studies. J Prev Med Public Health 2022;55(5):464-474. https://doi.org/10.3961/jpmph.22.299
  19. Greenland S, Robins JM. Estimation of a common effect parameter from sparse follow-up data. Biometrics 1985;41(1):55-68. https://doi.org/10.2307/2530643
  20. Stryhn H, Christensen J. Confidence intervals by the profile likelihood method, with applications in veterinary epidemiology. In: Proceedings of 10th Symposium of the International Society for Veterinary Epidemiology and Economics (ISVEE). November 2003; Vinal del Mar, Chile.
  21. Banack HR, Hayes-Larson E, Mayeda ER. Monte Carlo simulation approaches for quantitative bias analysis: a tutorial. Epidemiol Rev 2022;43(1):106-117. https://doi.org/10.1093/epirev/mxab 012
  22. Johnson RW. An introduction to the bootstrap. Teach Stat 2001;23(2):49-54. https://doi.org/10.1111/1467-9639.00050
  23. Greenland S. Dose-response and trend analysis in epidemiology: alternatives to categorical analysis. Epidemiology 1995;6(4):356-365. https://doi.org/10.1097/00001648-199507000-00005
  24. Breslow NE, Day NE, Halvorsen KT, Prentice RL, Sabai C. Estimation of multiple relative risk functions in matched case-control studies. Am J Epidemiol 1978;108(4):299-307. https://doi.org/10.1093/oxfordjournals.aje.a112623