@article{b914573d676047d9ae3f1cba706006aa,
title = "Analyses of preventive care measures with incomplete historical data in electronic medical records: An example from colorectal cancer screening",
abstract = "The calculation of quality of care measures based on electronic medical records (EMRs) may be inaccurate because of incomplete capture of past services. We evaluate the influence of different statistical approaches for calculating the proportion of patients who are up-to-date for a preventive service, using the example of colorectal cancer (CRC) screening. We propose an extension of traditional mixture models to account for the uncer-tainty in compliance which is further complicated by the choice of various screening modalities with different recommended screening intervals. We conducted simulation studies to compare various statistical approaches and demonstrated that the proposed method can alleviate bias when individuals with complete prior medical history information were not representative of the targeted population. The method is motivated by and applied to data from the National Cancer Institute–funded consortium Population-Based Research Optimizing Screening through Personalized Regiments (PROSPR). Findings from the application are important for the evaluation of appropriate use of preventive care and provide a novel tool for dealing with similar analytical challenges with EMR data in broad settings.",
keywords = "Cancer screening, EMR data, Event-time analysis, Mixture model",
author = "Yingye Zheng and Corley, {Douglas A.} and Chyke Doubeni and Ethan Halm and Shortreed, {Susan M.} and Barlow, {William E.} and Ann Zauber and Tosteson, {Tor Devin} and Jessica Chubak",
note = "Funding Information: This work was funded by NCI at the NIH (grant no. U01CA163304 and P30 CA015704, to W. E. Barlow and Y. Zheng; U54CA163261 to J. Chubak, S. Shortreed; U54CA163262, to D. A. Corley, C. A. Doubeni, and A. G. Za-uber; P30CA008748 to A. G. Zauber; U54CA163308, to E. A. Halm; and U54CA163307, to T. D. Tosteson). The authors thank the participating PROSPR Research Centers for the data they have provided for this study. A list of the PROSPR investigators and contributing research staff is provided at: https://healthcaredelivery.cancer.gov/prospr/acknowledgements. html. Funding Information: 7. Acknowledgments. This work was funded by NCI at the NIH (grant no. U01CA163304 and P30 CA015704, to W. E. Barlow and Y. Zheng; U54CA163261 to J. Chubak, S. Shortreed; U54CA163262, to D. A. Corley, C. A. Doubeni, and A. G. Za-uber; P30CA008748 to A. G. Zauber; U54CA163308, to E. A. Halm; and U54CA163307, to T. D. Tosteson). The authors thank the participating PROSPR Research Centers for the data they have provided for this study. A list of the PROSPR investigators and contributing research staff is provided at: https://healthcaredelivery.cancer.gov/prospr/acknowledgements. html. Publisher Copyright: {\textcopyright} Institute of Mathematical Statistics, 2020.",
year = "2020",
doi = "10.1214/20-AOAS1342",
language = "English (US)",
volume = "14",
pages = "1030--1044",
journal = "Annals of Applied Statistics",
issn = "1932-6157",
publisher = "Institute of Mathematical Statistics",
number = "2",
}