Mixed-effects Models for Health Care Longitudinal Data with an Informative Visiting Process: A Monte Carlo Simulation Study

Overview

Journal Stat Neerl

Date 2020 Jan 3

PMID 31894164

Citations 10

Authors

Alessandro Gasparini

Keith R Abrams

Jessica K Barrett

Rupert W Major

Michael J Sweeting

Nigel J Brunskill

Michael J Crowther

Affiliations

Soon will be listed here.

Abstract

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.

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References

Neuhaus J, McCulloch C, Boylan R . Analysis of longitudinal data from outcome-dependent visit processes: Failure of proposed methods in realistic settings and potential improvements. Stat Med. 2018; 37(29):4457-4471. DOI: 10.1002/sim.7932. View

Tanuseputro P, Wodchis W, Fowler R, Walker P, Bai Y, Bronskill S . The health care cost of dying: a population-based retrospective cohort study of the last year of life in Ontario, Canada. PLoS One. 2015; 10(3):e0121759. PMC: 4374686. DOI: 10.1371/journal.pone.0121759. View

Hernan M, Hernandez-Diaz S, Robins J . A structural approach to selection bias. Epidemiology. 2004; 15(5):615-25. DOI: 10.1097/01.ede.0000135174.63482.43. View

Pullenayegum E, Lim L . Longitudinal data subject to irregular observation: A review of methods with a focus on visit processes, assumptions, and study design. Stat Methods Med Res. 2014; 25(6):2992-3014. DOI: 10.1177/0962280214536537. View

McCulloch C, Neuhaus J, Olin R . Biased and unbiased estimation in longitudinal studies with informative visit processes. Biometrics. 2016; 72(4):1315-1324. PMC: 5026863. DOI: 10.1111/biom.12501. View

Buzkova P, Brown E, John-Stewart G . Longitudinal data analysis for generalized linear models under participant-driven informative follow-up: an application in maternal health epidemiology. Am J Epidemiol. 2009; 171(2):189-97. PMC: 2878101. DOI: 10.1093/aje/kwp353. View

Royston P, Parmar M . Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002; 21(15):2175-97. DOI: 10.1002/sim.1203. View

Liu L, Huang X, OQuigley J . Analysis of longitudinal data in the presence of informative observational times and a dependent terminal event, with application to medical cost data. Biometrics. 2007; 64(3):950-958. DOI: 10.1111/j.1541-0420.2007.00954.x. View

Van Ness P, Allore H, Fried T, Lin H . Inverse intensity weighting in generalized linear models as an option for analyzing longitudinal data with triggered observations. Am J Epidemiol. 2009; 171(1):105-12. PMC: 2800302. DOI: 10.1093/aje/kwp333. View

10.

Hernan M, McAdams M, McGrath N, Lanoy E, Costagliola D . Observation plans in longitudinal studies with time-varying treatments. Stat Methods Med Res. 2008; 18(1):27-52. PMC: 3878210. DOI: 10.1177/0962280208092345. View

11.

Gruger J, Kay R, Schumacher M . The validity of inferences based on incomplete observations in disease state models. Biometrics. 1991; 47(2):595-605. View

12.

Goldstein B, Bhavsar N, Phelan M, Pencina M . Controlling for Informed Presence Bias Due to the Number of Health Encounters in an Electronic Health Record. Am J Epidemiol. 2016; 184(11):847-855. PMC: 5152663. DOI: 10.1093/aje/kww112. View

13.

Levey A, Bosch J, Lewis J, Greene T, Rogers N, Roth D . A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999; 130(6):461-70. DOI: 10.7326/0003-4819-130-6-199903160-00002. View

14.

Hemmelgarn B, Clement F, Manns B, Klarenbach S, James M, Ravani P . Overview of the Alberta Kidney Disease Network. BMC Nephrol. 2009; 10:30. PMC: 2770500. DOI: 10.1186/1471-2369-10-30. View

15.

Benchimol E, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I . The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015; 12(10):e1001885. PMC: 4595218. DOI: 10.1371/journal.pmed.1001885. View

16.

Cole S, Hernan M . Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008; 168(6):656-64. PMC: 2732954. DOI: 10.1093/aje/kwn164. View

17.

Runesson B, Gasparini A, Qureshi A, Norin O, Evans M, Barany P . The Stockholm CREAtinine Measurements (SCREAM) project: protocol overview and regional representativeness. Clin Kidney J. 2016; 9(1):119-27. PMC: 4720196. DOI: 10.1093/ckj/sfv117. View

18.

Major R, Brown C, Shepherd D, Rogers S, Pickering W, Warwick G . The Primary-Secondary Care Partnership to Improve Outcomes in Chronic Kidney Disease (PSP-CKD) Study: A Cluster Randomized Trial in Primary Care. J Am Soc Nephrol. 2019; 30(7):1261-1270. PMC: 6622412. DOI: 10.1681/ASN.2018101042. View

19.

Gasparini A, Abrams K, Barrett J, Major R, Sweeting M, Brunskill N . Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study. Stat Neerl. 2020; 74(1):5-23. PMC: 6919310. DOI: 10.1111/stan.12188. View

20.

Wu M, Bailey K . Estimation and comparison of changes in the presence of informative right censoring: conditional linear model. Biometrics. 1989; 45(3):939-55. View