Random changepoint segmented regression with smooth transition

Author(s)

Singer, Julio M. ^[1] ; Rocha, Francisco M. M. ^[2] ; Pedroso-de-Lima, Antonio Carlos ^[1] ; Silva, Giovani L. ^{[3, 4]} ; Coatti, Giuliana C. ^[5] ; Zatz, Mayana ^[5]

Affiliation

^[1] Univ Sao Paulo, Dept Estat, Rua Matao 1010, BR-05508090 Sao Paulo, SP - Brazil

^[2] Univ Fed Sao Paulo, Dept Multidisciplinar, Escola Paulista Polit Econ & Negocios, Sao Paulo, SP - Brazil

^[3] Univ Lisbon, CEAUL, Lisbon - Portugal

^[4] Univ Lisbon, Dept Matemat IST, Lisbon - Portugal

^[5] Univ Sao Paulo, Inst Biociencias, Sao Paulo - Brazil

• Document type: Journal article
• Source: STATISTICAL METHODS IN MEDICAL RESEARCH; v. 30, n. 3, p. 643-654, MAR 2021.
• Web of Science Citations: 0

Abstract

We consider random changepoint segmented regression models to analyse data from a study conducted to verify whether treatment with stem cells may delay the onset of a symptom of amyotrophic lateral sclerosis in genetically modified mice. The proposed models capture the biological aspects of the data, accommodating a smooth transition between the periods with and without symptoms. An additional changepoint is considered to avoid negative predicted responses. Given the nonlinear nature of the model, we propose an algorithm to estimate the fixed parameters and to predict the random effects by fitting linear mixed models iteratively via standard software. We compare the variances obtained in the final step with bootstrapped and robust ones. (AU)

• FAPESP's process: 13/21728-2 - The use of modern autopsy techniques to investigate human diseases (MODAU)
• Grantee: Paulo Hilário Nascimento Saldiva
• Support Opportunities: Research Projects - Thematic Grants