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Activity Number: 405 - Statistical Issues Specific to Therapeutic Areas
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #304820
Title: Estimating Knots in Bilinear Spline Growth Models with Time-Invariant Covariates in the Framework of Individual Measurement Occasions
Author(s): Jin Liu* and Robert A. Perera and Robert M. Kirkpatrick
Companies: and VCU Department of Biostatistics and Virginia Institute for Psychiatric & Behavioral Genetics
Keywords: spline growth models; unknown knots; individually-varying time points; time-invariant covariates; trajectories; simulation studies

The linear spline growth model (LSGM) is a popular tool for examining nonlinear change patterns over time. It approximates more complex patterns by attaching at least two linear pieces. Besides examining within-person changes and between-person differences of trajectories simultaneously, it poses interesting statistical challenges, such as learning the location of a change point (i.e., knot), the knot’s variance, prediction of the knot location using covariates, and analyzing data with individually-varying time points (ITPs). We developed a pair of bilinear spline growth models with time-invariant covariates (BLSGMs-TICs) to estimate a knot and its variability as well as to investigate predictors for individual differences of trajectories in the ITPs framework. Our simulation studies demonstrate that the proposed BLSGMs-TICs are capable of estimating and testing the knot variance while correctly controlling type I error rates. More importantly, they generally estimated the parameters of interest unbiasedly, precisely and exhibited appropriate confidence interval coverage.

Authors who are presenting talks have a * after their name.

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