| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Full Review Modeling longitudinal data and its impact on survival in observational nephrology studies: tools and considerations Elani Streja 1,2 , Leanne Goldstein 3 , Melissa Soohoo 1 , Yoshitsugu Obi 1 , Kamyar Kalantar-Zadeh 1,2 and Connie M. Rhee 1 1 Harold Simmons Center for Chronic Disease Research and Epidemiology, Division of Nephrology and Hypertension, University of California Irvine School of Medicine, Irvine, CA, USA, 2 Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA and 3 Division of Biostatistics, City of Hope National Medical Center, Duarte, CA, USA Correspondence and offprint requests to: Elani Streja; E-mail: estreja@uci.edu ABSTRACT Nephrologists and kidney disease researchers are often inter- ested in monitoring how patients’ clinical and laboratory measures change over time, what factors may impact these changes, and how these changes may lead to differences in morbidity, mortality, and other outcomes. When longitudinal data with repeated measures over time in the same patients are available, there are a number of analytical approaches that could be employed to describe the trends and changes in these measures, and to explore the associations of these changes with outcomes. Researchers may choose a streamlined and simplified analytic approach to examine trajectories with sub- sequent outcomes such as estimating deltas (subtraction of the last observation from the first observation) or estimating per patient slopes with linear regression. Conversely, they could more fully address the data complexity by using a longitudinal mixed model to estimate change as a predictor or employ a joint model, which can simultaneously model the longitudinal effect and its impact on an outcome such as survival. In this re- view, we aim to assist nephrologists and clinical researchers by reviewing these approaches in modeling the association of lon- gitudinal change in a marker with outcomes, while appropri- ately considering the data complexity. Namely, we will discuss the use of simplified approaches for creating predictor vari- ables representing change in measurements including deltas and patient slopes, as well more sophisticated longitudinal models including joint models, which can be used in addition to simplified models based on the indications and objectives of the study as warranted. Keywords: change analysis, joint models, longitudinal, mixed- effects models, repeated measures INTRODUCTION In a large number of survival analysis studies, laboratory and clinical measurements are measured at a single-point-in-time at the beginning of the study (baseline), and patients are then fol- lowed for a period of time subsequent to this baseline time point to examine associations of these markers with morbidity and mortality outcomes. However, when researchers have data with repeated measurements, they may prefer to measure variability that occurs over a specified time period and investigate how changes in these variables impact outcomes after the exposure period. They may use methods such as time-dependent (or time-varying covariate) analyses, where the single measurement may be updated over time and replaced with subsequent meas- urements to examine short-term associations, or time-averaged analyses, where a patient’s baseline measurement is represented by a single estimate derived from the average of measurements over a specified period. Additionally, with repeated measure- ments, researchers can also examine changes or fluctuations in these laboratory measurements and how it impacts clinical out- comes. Researchers may use these changes and variations as a predictor of an outcome or examine and describe trends and the factors that impact these trends. A previous review by Leffondre et al. [1] superbly summarizes and compares three methods of estimating trajectories of renal function over time and discusses their advantages and limitations, especially when renal function trajectories are not completely observable due to | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | V C The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. ii77 Nephrol Dial Transplant (2017) 32: ii77–ii83 doi: 10.1093/ndt/gfx015 Advance Access publication 3 March 2017 Downloaded from https://academic.oup.com/ndt/article/32/suppl_2/ii77/3061525 by guest on 10 January 2023