Several types of residuals in cox regression model : an empirical study

There are several methods for calculating residual in survival analysis, especially in Cox regression model by which each method has specific use, such as goodness-of-fit, to identify possible outliers and influential observations, or in general to check necessary assumptions. In this article, we study four methods of residuals, namely Schoenfeld, Martingale, deviance, and score residuals and we applied those methods on cardiovascular data

Cross-cohort prognosis of levodopa-induced dyskinesia in Parkinson’s disease

peer reviewedR-AGR-3931 - INTER/ERAPerMed 20/14599012/DIGIPD (01/04/2021 - 31/03/2024) - GLAAB Enrico3. Good health and well-bein

Age at onset as stratifier in idiopathic Parkinson’s disease – effect of ageing and polygenic risk score on clinical phenotypes

Several phenotypic differences observed in Parkinson’s disease (PD) patients have been linked to age at onset (AAO). We endeavoured to find out whether these differences are due to the ageing process itself by using a combined dataset of idiopathic PD (n = 430) and healthy controls (HC; n = 556) excluding carriers of known PD-linked genetic mutations in both groups. We found several significant effects of AAO on motor and non-motor symptoms in PD, but when comparing the effects of age on these symptoms with HC (using age at assessment, AAA), only positive associations of AAA with burden of motor symptoms and cognitive impairment were significantly different between PD vs HC. Furthermore, we explored a potential effect of polygenic risk score (PRS) on clinical phenotype and identified a significant inverse correlation of AAO and PRS in PD. No significant association between PRS and severity of clinical symptoms was found. We conclude that the observed non-motor phenotypic differences in PD based on AAO are largely driven by the ageing process itself and not by a specific profile of neurodegeneration linked to AAO in the idiopathic PD patients

Measures of influence and weighted partial likelihood estimation for cox proportional hazards regression

In this study, we consider the development of influential diagnostics to assess case influence for the Cox proportional hazards model and stratified Cox proportional hazards regression model. We examine various residuals previously proposed for these models and develop a diagnostics method using the case-deletion technique. However, existing diagnostics methods are affected by masking effect. This effect may cause diagnostics methods to fail to correctly detect influential cases. Therefore, we propose an influential diagnostics method that has lower masking effect as compared to other methods. The proposed influential diagnostics method is approximately Chi-square distribution with p degress of freedom. The simulation study is implemented to evaluate the performance of the proposed influential diagnostics method via comparison with existing diagnostics method. Then, the diagnostics methods are applied into the real data such as kidney catheter data, Worcester Heart Attack study and also Stanford Heart Transplant study. The performance of the proposed influential detection method is better than that of the existing influential detection method. The partial likelihood estimation for the Cox regression model is biased when there are measurement errors in the covariate. Therefore, a weighted partial likelihood estimation for Cox regression model is proposed when there is violation of underlying assumptions due to measurement error in the covariates. In the simulation study, the proposed weighted partial likelihood estimations for parameter coefficients have smaller bias, root mean square errors, and ratio of bias over standard error than the existing parameter estimators, both with and without contamination of the covariates. The demonstrated performance of the proposed influential methods and weighted partial likelihood estimators are superior to existing influential detection methods and parameter estimators

Education as Risk Factor of Mild Cognitive Impairment: The Link to the Gut Microbiome

peer reviewedBackground: With differences apparent in the gut microbiome in mild cognitive impairment (MCI) and dementia, and risk factors of dementia linked to alterations of the gut microbiome, the question remains if gut microbiome characteristics may mediate associations of education with MCI. Objectives: We sought to examine potential mediation of the association of education and MCI by gut microbiome diversity or composition. Design: Cross-sectional study. Setting: Luxembourg, the Greater Region (surrounding areas in Belgium, France, Germany). Participants: Control participants of the Luxembourg Parkinson’s Study. Measurements: Gut microbiome composition, ascertained with 16S rRNA gene amplicon sequencing. Differential abundance, assessed across education groups (0–10, 11–16, 16+ years of education). Alpha diversity (Chao1, Shannon and inverse Simpson indices). Mediation analysis with effect decomposition was conducted with education as exposure, MCI as outcome and gut microbiome metrics as mediators. Results: After exclusion of participants below 50, or with missing data, n=258 participants (n=58 MCI) were included (M [SD] Age=64.6 [8.3] years). Higher education (16+ years) was associated with MCI (Odds ratio natural direct effect=0.35 [95% CI 0.15–0.81]. Streptococcus and Lachnospiraceae-UCG-001 genera were more abundant in higher education. Conclusions: Education is associated with gut microbiome composition and MCI risk without clear evidence for mediation. However, our results suggest signatures of the gut microbiome that have been identified previously in AD and MCI to be reflected in lower education and suggest education as important covariate in microbiome studies.MCI-BIOME_20193. Good health and well-bein