Linear mixed models (LMMs) are a powerful and established tool for studying genotype-phenotype relationships. A limiting assumption of LMMs is that the residuals are Gaussian distributed, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and losses in power, and hence it is common practice to pre-process the phenotypic values to make them G...

This paper aims at proposing suitable statistical tools to address heterogeneity in repeated measures, within a Multiple Sclerosis (MS) longitudinal study. Indeed, due to unobservable sources of heterogeneity, modelling the effect of covariates on MS severity evolves as a very difficult feature. Bayesian P-Splines are suggested for modelling non linear smooth effects of covariates within genera...

To evaluate changes in skin carotenoid score (SCS) from baseline, during and following a two-week sweet potato snack added to participant's usual diet. Forty participants were recruited by convenience for 7-week longitudinal cohort study with 1-week period establish baseline SCS, an intervention consisting of fed 3 times/week 2 weeks the lab while consuming diet outside lab, followed 4-week mon...

Abstract BACKGROUND We previously reported on our in vitro and vivo studies using the metallocompound gallium maltolate (GaM) to target glioblastoma (GBM). demonstrated a profound cytotoxic effect vitrov vivo. Additionally, preliminary data suggested substantial inhibitory tumor growth, as well significant extension of disease-specific survival (OS). Here, we further validate effects GaM METHOD...

BACKGOUND The term 'joint modelling' is used in the statistical literature to refer to methods for simultaneously analysing longitudinal measurement outcomes, also called repeated measurement data, and time-to-event outcomes, also called survival data. A typical example from nephrology is a study in which the data from each participant consist of repeated estimated glomerular filtration rate (e...

Longitudinal studies often gather joint information on time to some event (survival analysis, time to dropout) and serial outcome measures (repeated measures, growth curves). Depending on the purpose of the study, one may wish to estimate and compare serial trends over time while accounting for possibly non-ignorable dropout or one may wish to investigate any associations that may exist between...