ABSTRACT
As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodness-of-fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.
Subject(s)
Humans , Follow-Up Studies , Methods , Proportional Hazards Models , Statistics as Topic , Survival AnalysisABSTRACT
Objective To identify the risk factors associated with cardiovascular and cerebrovascular disease (CCVD) in maintenance hemodialysis (MHD) patients. Methods We analyzed all of the patients undergoing maintenance hemodialysis in the dialysis center of the 3rd Affiliated Hospital of Sun Yat-sen University for at least 3 months from Jan 1st, 2009 to Dec 31st, 2014. Baseline and yearly interval clinical data were recorded and patients were followed up until morbidity or death of CCVD. Cox proportional hazard regression and time-dependent Cox regression were used to estimate the relative risk of outcomes associated with clinical measurements. Results There were 243 patients enrolled in the study, with a mean age of (53.2 ± 16.4) years old, and 138 of them were male (56.8%). The multivariate Cox proportional model revealed that age (HR=1.040, 95%CI:1.015-1.065, P=0.002), Erythropoietin (EPO) dose (HR=0.914, 95%CI: 0.846-0.987, P=0.022) and history of cardiovascular and cerebrovascular disease (HR=4.045, 95%CI: 2.074-7.890, P<0.001) were independent predictors of CCVD in MHD patients. After adjusting for baseline predictors, time-dependent serum phosphorus level (HR=1.722, 95%CI: 1.034-2.866, P=0.037) was significantly associated with CCVD. Conclusion Older age, decreases in EPO dose and history of cardiovascular and cerebrovascular disease were associated with increased risks of CCVD in MHD patients. Increase in serum phosphorus level was associated with increased risks of CCVD in a time-dependent manner.