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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.

Keywords

Amyotrophic lateral sclerosis fitting algorithm mixed models random effects

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References

Doble

Kennel

Animal models of amyotrophic lateral sclerosis.

Amyotrophic Lateral Sclerosis 2000; 1: 301–312.

McCombe

Henderson

RD.

Effects of gender in amyotrophic lateral sclerosis.

Gender Med 2010; 7: 557–570.

Coatti

Frangni

Valadares

, et al. Pericytes extend survival of ALS SOD1 mice and induce expression of antioxidant enzymes in the murine model and in IPSCS derived motor neurons from an ALS patient. Stem Cell Rev Rep 2017; 13: 686–698.

Muggeo

VMR

Atkins

Gallop

, et al. Segmented mixed models with random changepoints: a maximum likelihood approach with application to treatment for depression study. Stat Model 2014; 14: 293–313.

Fasola

Muggeo

VMR

Küchenhoff

A heuristic and iterative algorithm for change-point detection in abrupt change models. Computat Stat 2018; 33: 997–1015.

Jacqmin-Gadda

Commenges

Dartigues

JF.

Random changepoint model for joint modelling of cognitive decline and dementia.

Biometrics 2006; 62: 254–260.

Sen

Singer

Pedroso-de Lima

AC.

From finite sample to asymptotic methods in statistics. Cambridge: Cambridge University Press, 2009.

Singer

Rocha

FMM

Nobre

JS.

Graphical tools for detecting departures from linear mixed models assumptions and some remedial measures. Int Stat Rev 2017; 85: 290–324.

Liang

Zeger

SL.

Longitudinal data analysis using generalized linear models. Biometrika 1986; 73: 13–22.

10.

Segalas

Amieva

Jacqmin-Gadda

A hypothesis testing procedure for random changepoint mixed models.

Stat Med 2019; 38: 3791–3803.

11.

Bacon

Watts

DG.

A heuristic and iterative algorithm for change-point detection in abrupt change models. Biometrika 1971; 58: 525–534.

12.

Morrell

Pearson

Carter

, et al. Estimating unknown transition times using a piecewise nonlinear mixed-effects model in men with prostate cancer. J Am Stat Assoc 1995; 90: 45–53.

13.

Wood

SN.

Minimizing model fitting objectives that contain spurious local minima by bootstrap restarting.

Biometrics 2001; 57: 240–244.

14.

Muggeo

VMR.

Estimating regression models with unknown break-points.

Stat Med 2003; 22: 3055–3071.

15.

Diggle

Heagerty

Liang

, et al. Analysis of longitudinal data. 2nd ed. Oxford: Oxford University Press, 2002.

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