Joint-modeling of prostate-specific antigen levels and survival data of prostate cancer patients
Objective To explore the effect of dynamical changes of prostate-specific antigen(PSA)on the survival progno-sis of advanced prostate cancer patients,and provide a theoretical basis for individualized treatment of prostate cancer pa-tients.Methods This study was a retrospective cohort analysis.A total of 176 patients diagnosed with prostate cancer through pathological examination at the Affiliated Cancer Hospital of Xinjiang Medical University from January 01,2011 to December 31,2017 were continuously collected as the study subjects.According to the treatment regimen,the patients were divided into bicalutamide combined with goserelin group(n=126)and flutamide combined with goserelin group(n=50).Linear mixed effects model and Cox proportional risk model were separately used to fit the dynamical changes of ser-um PSA and survival data of advanced prostate cancer patients.Under the shared random effects,two joint models based on maximum likelihood and Bayesian estimation methods were further constructed,respectively.In addition,the goodness of fit for two joint models were separately assessed by the Akaike information criterion(AIC),Bayesian information cri-terion(BIC),and Log-Likelihood function(LLF)values.The area under the receiver operating characteristic curve(AUC)and prediction error(PE)were applied to compare the predictive performance of two joint models,respectively.The independent sam-ple t test,Mann-Whitney U rank sum test,x2 test,or Fisher exact probability method were applied in comparing the baseline da-ta differences between groups with different treatment regimens.Results A total of 176 patients with prostate cancer were in-cluded,aged 45-90 years,with an average age of(71.76±7.86)years,and the patients were followed-up for 1.30-36.77 months.It was shown from the joint model based on maximum likelihood estimation method that when the serum PSA increases tenfold over time,the risk of death would increase by 0.94 times(HR=1.94,95％CI:1.74-2.16,P＜0.001)by comparing with the patients whose serum PSA level did not increase,and it was shown from the joint model based on Bayesian estimation method that when the serum PSA increases tenfold over time,the risk of death would increase by 1.18 times(HR=2.18,95％CI:1.77-2.73,P＜0.001)by comparing with the patients whose serum PSA level did not increase.In addition,the joint model based on maximum likelihood estimation showed the better goodness of fit(AIC=3 265.01,BIC=3 303.06,LLF=-1 620.51),while the joint model based on Bayesian estimation with larger values of AUC(0.70-0.88)and smaller values of PE(0.04-0.10),had the stronger prediction performance.Conclusions The increase of serum PSA is a dangerous factor on the survival of advanced prostate cancer patients.It will be essential to closely monitor the dynamical changes of PSA for advanced prostate cancer patients in clinical,which can provide more accurate formulation of individualized treatment for prostate cancer patients.
prostate cancerprostate-specific antigen(PSA)longitudinal datasurvival prognosisjoint model
NETL
NSTL
万方数据
