References
- Andersen, P. K. , and Gill, R. D. (1982), “Cox’s Regression Model for Counting Processes: A Large Sample Study,” The Annals of Statistics , 10, 1100–1120. DOI: https://doi.org/10.1214/aos/1176345976.
- Cao, H. , Churpek, M. M. , Zeng, D. , and Fine, J. P. (2015), “Analysis of the Proportional Hazards Model with Sparse Longitudinal Covariates,” Journal of the American Statistical Association , 110, 1187–1196. DOI: https://doi.org/10.1080/01621459.2014.957289.
- Casey, J. A. , Schwartz, B. S. , Stewart, W. F. , and Adler, N. E. (2016), “Using Electronic Health Records for Population Health Research: A Review of Methods and Applications,” Annual Review of Public Health , 37, 61–81. DOI: https://doi.org/10.1146/annurev-publhealth-032315-021353.
- Cook, R. J. , and Lawless, J. (2007), The Statistical Analysis of Recurrent Events , New York: Springer.
- Coorevits, P. , Sundgren, M. , Klein, G. O. , Bahr, A. , Claerhout, B. , Daniel, C. , Dugas, M. , Dupont, D. , Schmidt, A. , Singleton, P. , and De Moor, G. (2013), “Electronic Health Records: New Opportunities for Clinical Research,” Journal of Internal Medicine , 274, 547–560. DOI: https://doi.org/10.1111/joim.12119.
- Dai, H. , and Pan, J. (2018), “Joint Modelling of Survival and Longitudinal Data With Informative Observation Times,” Scandinavian Journal of Statistics , 45, 571–589. DOI: https://doi.org/10.1111/sjos.12314.
- Faucett, C. L. , Schenker, N. , and Elashoff, R. M. (1998), “Analysis of Censored Survival Data with Intermittently Observed Time-Dependent Binary Covariates,” Journal of the American Statistical Association , 93, 427–437. DOI: https://doi.org/10.1080/01621459.1998.10473692.
- Fiks, A. G. , Grundmeier, R. W. , Margolis, B. , Bell, L. M. , Steffes, J. , Massey, J. , and Wasserman, R. C. (2012), “Comparative Effectiveness Research Using the Electronic Medical Record: An Emerging Area of Investigation in Pediatric Primary Care,” The Journal of Pediatrics , 160, 719–724. DOI: https://doi.org/10.1016/j.jpeds.2012.01.039.
- Goldstein, B. A. , Bhavsar, N. A. , Phelan, M. , and Pencina, M. J. (2016), “Controlling for Informed Presence Bias Due to the Number of Health Encounters in an Electronic Health Record,” American Journal of Epidemiology , 184, 847–855. DOI: https://doi.org/10.1093/aje/kww112.
- Han, M. , Song, X. , Sun, L. , and Liu, L. (2014), “Joint Modeling of Longitudinal Data With Informative Observation Times and Dropouts,” Statistica Sinica , 24, 1487–1504. DOI: https://doi.org/10.5705/ss.2013.063.
- Hindley, N. , Ramchandani, P. G. , and Jones, D. P. (2006), “Risk Factors for Recurrence of Maltreatment: A Systematic Review,” Archives of Disease in Childhood , 91, 744–752. DOI: https://doi.org/10.1136/adc.2005.085639.
- Humar, A. , Lebranchu, Y. , Vincenti, F. , Blumberg, E. , Punch, J. , Limaye, A. , Abramowicz, D. , Jardine, A. , Voulgari, A. , Ives, J. , and Hauser, I. A. (2010), “The Efficacy and Safety of 200 Days Valganciclovir Cytomegalovirus Prophylaxis in High-Risk Kidney Transplant Recipients,” American Journal of Transplantation , 10, 1228–1237. DOI: https://doi.org/10.1111/j.1600-6143.2010.03074.x.
- Inácio de Carvalho, V. , de Carvalho, M. , Alonzo, T. A. , and González-Manteiga, W. (2016), “Functional Covariate-Adjusted Partial Area Under the Specificity-ROC Curve With an Application to Metabolic Syndrome Diagnosis,” The Annals of Applied Statistics , 10, 1472–1495. DOI: https://doi.org/10.1214/16-AOAS943.
- Li, S. , Sun, Y. , Huang, C.-Y. , Follmann, D. A. , and Krause, R. (2016), “Recurrent Event Data Analysis With Intermittently Observed Time-Varying Covariates,” Statistics in Medicine , 35, 3049–3065. DOI: https://doi.org/10.1002/sim.6901.
- Li, Y. , He, X. , Wang, H. , and Sun, J. (2016), “Joint Analysis of Longitudinal Data and Informative Observation Times With Time-Dependent Random Effects,” in New Developments in Statistical Modeling, Inference and Application , eds. Z. Jin , M. Liu , and X. Luo , Cham: Springer, pp. 37–51.
- Lin, D. , Wei, L. , Yang, I. , and Ying, Z. (2000), “Semiparametric Regression for the Mean and Rate Functions of Recurrent Events,” Journal of the Royal Statistical Society, Series B, 62, 711–730. DOI: https://doi.org/10.1111/1467-9868.00259.
- Liu, L. , Huang, X. , and O’Quigley, J. (2008), “Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, With Application to Medical Cost Data,” Biometrics , 64, 950–958. DOI: https://doi.org/10.1111/j.1541-0420.2007.00954.x.
- Luo, L. , Small, D. , Stewart, W. F. , and Roy, J. A. (2013), “Methods for Estimating Kidney Disease Stage Transition Probabilities Using Electronic Medical Records,” eGEMs , 1, 1040. DOI: https://doi.org/10.13063/2327-9214.1040.
- Maity, A. , Ma, Y. , and Carroll, R. J. (2007), “Efficient Estimation of Population-Level Summaries in General Semiparametric Regression Models,” Journal of the American Statistical Association , 102, 123–139. DOI: https://doi.org/10.1198/016214506000001103.
- Morgan, C. , Martin, A. , Shapiro, R. , Randhawa, P. , and Kayler, L. (2007), “Outcomes After Transplantation of Deceased-Donor Kidneys With Rising Serum Creatinine,” American Journal of Transplantation , 7, 1288–1292. DOI: https://doi.org/10.1111/j.1600-6143.2007.01761.x.
- Nan, B. , and Wellner, J. A. (2013), “A General Semiparametric Z-Estimation Approach for Case-Cohort Studies,” Statistica Sinica , 23, 1155–1180.
- Phelan, M. , Bhavsar, N. , and Goldstein, B. A. (2017), “Illustrating Informed Presence Bias in Electronic Health Records Data: How Patient Interactions with a Health System Can Impact Inference,” eGEMs , 5, 22. DOI: https://doi.org/10.5334/egems.243.
- Prentice, R. (1982), “Covariate Measurement Errors and Parameter Estimation in a Failure Time Regression Model,” Biometrika , 69, 331–342. DOI: https://doi.org/10.1093/biomet/69.2.331.
- Pullenayegum, E. M. , and Lim, L. S. (2016), “Longitudinal Data Subject to Irregular Observation: A Review of Methods With a Focus on Visit Processes, Assumptions, and Study Design,” Statistical Methods in Medical Research , 25, 2992–3014. DOI: https://doi.org/10.1177/0962280214536537.
- Razonable, R. R. , and Humar, A. (2013), “Cytomegalovirus in Solid Organ Transplantation,” American Journal of Transplantation , 13, 93–106. DOI: https://doi.org/10.1111/ajt.12103.
- Rizopoulos, D. (2012), Joint Models for Longitudinal and Time-to-Event Data: With Applications in R , Boca Raton, FL: Chapman& Hall/CRC.
- Sun, L. , Song, X. , Zhou, J. , and Liu, L. (2012), “Joint Analysis of Longitudinal Data with Informative Observation Times and a Dependent Terminal Event,” Journal of the American Statistical Association , 107, 688–700. DOI: https://doi.org/10.1080/01621459.2012.682528.
- Tsiatis, A. A. (2007), Semiparametric Theory and Missing Data , New York: Springer.
- Tsiatis, A. A. , and Davidian, M. (2004), “Joint Modeling of Longitudinal and Time-to-Event Data: An Overview,” Statistica Sinica , 14, 809–834.
- van der Vaart, A. W. , and Wellner, J. A. (1996), Weak Convergence and Empirical Processes: With Applications to Statistics , New York: Springer.
- van Velthoven, M. H. , Mastellos, N. , Majeed, A. , O’Donoghue, J. , and Car, J. (2016), “Feasibility of Extracting Data from Electronic Medical Records for Research: An International Comparative Study,” BMC Medical Informatics and Decision Making , 16, 90. DOI: https://doi.org/10.1186/s12911-016-0332-1.
- Wu, P.-Y. , Cheng, C.-W. , Kaddi, C. D. , Venugopalan, J. , Hoffman, R. , and Wang, M. D. (2016), “-Omic and Electronic Health Record Big Data Analytics for Precision Medicine,” IEEE Transactions on Biomedical Engineering , 64, 263–273.
- Yao, F. (2007), “Functional Principal Component Analysis for Longitudinal and Survival Data,” Statistica Sinica , 17, 965–983.