170
Views
0
CrossRef citations to date
0
Altmetric
ORIGINAL RESEARCH

Inverse Probability Weighting Enhances Absolute Risk Estimation in Three Common Study Designs of Nosocomial Infections

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, & show all
Pages 1053-1064 | Received 25 Jan 2022, Accepted 20 Jul 2022, Published online: 14 Sep 2022

References

  • Ohneberg K, Wolkewitz M, Beyersmann J, et al. Analysis of clinical cohort data using nested case-control and case-cohort sampling designs. A powerful and economical tool. Methods Inf Med. 2015;54(6):505–514. doi:10.3414/ME14-01-0113
  • Doerken S, Mandel M, Zingg W, Wolkewitz M. Use of prevalence data to study sepsis incidence and mortality in intensive care units. Lancet Infect Dis. 2018;18(3):252. doi:10.1016/S1473-3099(18)30081-1
  • Savin I, Ershova K, Kurdyumova N, et al. Healthcare-associated ventriculitis and meningitis in a neuro-ICU: incidence and risk factors selected by machine learning approach. J Crit Care. 2018;45:95–104. doi:10.1016/j.jcrc.2018.01.022
  • Wolkewitz M, Cooper BS, Bonten MJM, Barnett AG, Schumacher M. Interpreting and comparing risks in the presence of competing events. BMJ. 2014;349:g5060. doi:10.1136/bmj.g5060
  • Beyersmann J, Allignol A, Schumacher M. Competing Risks and Multistate Models with R. New York, NY: Springer New York; 2012.
  • Aalen OO, Johansen S. An empirical transition matrix for non-homogeneous Markov chains based on censored observations. Scandinavian. J Stat. 1978;5(3):141–150.
  • van Walraven C, McAlister FA. Competing risk bias was common in Kaplan-Meier risk estimates published in prominent medical journals. J Clin Epidemiol. 2016;69:170–173.e8. doi:10.1016/j.jclinepi.2015.07.006
  • R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2022. https://www.R-project.org/
  • Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation. 2016;133(6):601–609. doi:10.1161/CIRCULATIONAHA.115.017719
  • Støer NC, Samuelsen SO. Inverse probability weighting in nested case-control studies with additional matching–a simulation study. Stat Med. 2013;32(30):5328–5339. doi:10.1002/sim.6019
  • Borgan O, Goldstein L, Langholz B. Methods for the analysis of sampled cohort data in the cox proportional hazards model. Ann Statist. 1995;23(5):1749–1778. doi:10.1214/aos/1176324322
  • Samuelsen SO. A pseudolikelihood approach to analysis of nested case-control studies. Biometrika. 1997;84(2):379–394. doi:10.1093/biomet/84.2.379
  • Støer NC, Samuelsen SO. Comparison of estimators in nested case-control studies with multiple outcomes. Lifetime Data Anal. 2012;18(3):261–283. doi:10.1007/s10985-012-9214-8
  • Hazard D, Schumacher M, Palomar-Martinez M, Alvarez-Lerma F, Olaechea-Astigarraga P, Wolkewitz M. Improving nested case-control studies to conduct a full competing-risks analysis for nosocomial infections. Infect Control Hosp Epidemiol. 2018;39(10):1196–1201. doi:10.1017/ice.2018.186
  • Barlow WE, Ichikawa L, Rosner D, Izumi S. Analysis of case-cohort designs. J Clin Epidemiol. 1999;52(12):1165–1172. doi:10.1016/S0895-4356(99)00102-X
  • Barlow WE. Robust variance estimation for the case-cohort design. Biometrics. 1994;50(4):1064–1072. doi:10.2307/2533444
  • Wolkewitz M, Palomar-Martinez M, Olaechea-Astigarraga P, Alvarez-Lerma F, Schumacher M. A full competing risk analysis of hospital-acquired infections can easily be performed by a case-cohort approach. J Clin Epidemiol. 2016;74:187–193. doi:10.1016/j.jclinepi.2015.11.011
  • von Cube M, Schumacher M, Palomar-Martinez M, Olaechea-Astigarraga P, Alvarez-Lerma F, Wolkewitz M. A case-cohort approach for multi-state models in hospital epidemiology. Stat Med. 2017;36(3):481–495. doi:10.1002/sim.7146
  • Delgado-Rodríguez M, Llorca J. Bias. J Epidemiol Community Health. 2004;58(8):635–641. doi:10.1136/jech.2003.008466
  • Mandel M, Fluss R. Nonparametric estimation of the probability of illness in the illness-death model under cross-sectional sampling. Biometrika. 2009;96(4):861–872. doi:10.1093/biomet/asp046
  • Fluss R, Mandel M, Freedman LS, et al. Correction of sampling bias in a cross-sectional study of post-surgical complications. Stat Med. 2013;32(14):2467–2478. doi:10.1002/sim.5608
  • Frantal S, Pernicka E, Hiesmayr M, Schindler K, Bauer P. Length bias correction in one-day cross-sectional assessments - The nutritionDay study. Clin Nutr. 2016;35(2):522–527. doi:10.1016/j.clnu.2015.03.019
  • Doerken S, Metsini A, Buyet S, Wolfensberger A, Zingg W, Wolkewitz M. Estimating incidence and attributable length of stay of healthcare-associated infections – modelling the Swiss point-prevalence survey. Infect Control Hosp Epidemiol. 2021;1–10. doi:10.1017/ice.2021.295
  • Mandel M. The competing risks illness-death model under cross-sectional sampling. Biostatistics. 2010;11(2):290–303. doi:10.1093/biostatistics/kxp048
  • LII/Legal Information Institute. 45 CFR § 46.117 - Documentation of informed consent. Available from: https://www.law.cornell.edu/cfr/text/45/46.117. Accessed January 18, 2022.
  • Federal agency for technical regulation and metrology. GOST 52379-2005. GCP: Good Clinical Practice. Available from: https://www.medtran.ru/rus/trials/gost/52379-2005.htm. Accessed January 18, 2022.
  • Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–1251. doi:10.1016/0895-4356(94)90129-5
  • Feifel J, von Cube M, Ohneberg K, et al. Sampling designs for rare time-dependent exposures: a comparison of the nested exposure case-control design and exposure density sampling. Epidemiol Infect. 2021;149:e122. doi:10.1017/S095026882100090X