Focused Information Criteria and Model Averaging for the Cox Hazard Regression Model

Abstract Background Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective. Methods Official surveillance data about the COVID-19 and sociodemographic information in China’s 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis. Results Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran’s I = − 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased. Conclusions There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.

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Nonlinear predictive model selection and model averaging using information criteria

Systems Science & Control Engineering ◽

10.1080/21642583.2018.1496042 ◽

2018 ◽

Vol 6 (1) ◽

pp. 319-328 ◽

Cited By ~ 4

Author(s):

Yuanlin Gu ◽

Hua-Liang Wei ◽

Michael M. Balikhin

Keyword(s):

Model Selection ◽

Predictive Model ◽

Model Averaging ◽

Information Criteria

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Goodness-of-fit test for the parametric proportional hazard regression model with interval-censored data

Biostatistics & Epidemiology ◽

10.1080/24709360.2018.1529347 ◽

2018 ◽

Vol 2 (1) ◽

pp. 115-131

Author(s):

Rieko Sakurai ◽

Satoshi Hattori

Keyword(s):

Regression Model ◽

Censored Data ◽

Goodness Of Fit ◽

Proportional Hazard ◽

Interval Censored Data ◽

Goodness Of Fit Test ◽

Hazard Regression ◽

Interval Censored

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PERBANDINGAN MODEL REGRESI COX PROPORTIONAL HAZARD MENGGUNAKAN METODE BRESLOW DAN EFRON (Studi Kasus: Penderita Stroke di RSUD Tugurejo Kota Semarang)

Jurnal Gaussian ◽

10.14710/j.gauss.v8i1.26624 ◽

2019 ◽

Vol 8 (1) ◽

pp. 93-105

Author(s):

Eri Setiani ◽

Sudarno Sudarno ◽

Rukun Santoso

Keyword(s):

Blood Pressure ◽

Systolic Blood Pressure ◽

Regression Model ◽

Diastolic Blood Pressure ◽

Proportional Hazard ◽

Hazard Regression ◽

Cox Proportional Hazard ◽

History Of ◽

Cox Proportional Hazard Regression ◽

Sugar Levels

Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is phrase used to describe analysis of data in the form of times from a well-defined time origin until occurrence of some particular even or end-point. In analysis survival sometimes ties are found, namely there are two or more individual that have together event. This study aims to apply Cox model on ties event using two methods, Breslow and Efron and determine factors that affect survival of stroke patients in Tugurejo Hospital Semarang. Dependent variable in this study is length of stay, then independent variables are gender, age, type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, blood sugar levels, and BMI. The two methods give different result, Breslow has four significant variables there are type of stroke, history of hypertension, systolic blood pressure, and diastolic blood pressure, while Efron contains five significant variables such as type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure and blood sugar levels. From the smallest AIC criteria obtained the best Cox proportional hazard regression model is Efron method. Keywords: Stroke, Cox Proportional Hazard Regression model, Breslow method, Efron method.

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