1. Random changepoint segmented regression with smooth transition
- Author
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Julio M. Singer, Giuliana Castello Coatti, Giovani L. Silva, Antonio C. Pedroso-de-Lima, Francisco Mm Rocha, and Mayana Zatz
- Subjects
Statistics and Probability ,Mixed model ,Epidemiology ,01 natural sciences ,Generalized linear mixed model ,010104 statistics & probability ,03 medical and health sciences ,Mice ,0302 clinical medicine ,Software ,Health Information Management ,Fitting algorithm ,Animals ,0101 mathematics ,Segmented regression ,Mathematics ,030219 obstetrics & reproductive medicine ,business.industry ,Random effects model ,Nonlinear system ,Linear Models ,REGRESSÃO LINEAR ,business ,Algorithm ,Algorithms - 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.
- Published
- 2021