Descriptive Summaries
A total of 295 stroke patients were treated in the hospital during the study period from 1st September 2014 to 1st April 2017. Of total population, this study included 202 stroke patients for whom data for variables of interest are complete. Of all 202 stroke patients 147(72.8%) were censored or not experienced the event and 55(27.2%) are died. Average time duration for all patients was 6.05 with standard deviation of 4.698 and the median and mean survival time of age was 6 and 7.168 days respectively. The mean and median age of stroke patient was 62.56 and 65 days respectively .The mean and median survival time from stroke was found to be 15.596 and 19 days respectively. The mean survival time of male and female were 9.1 days and 5.1 days respectively. The minimum and the maximum survival time observed in the data were 1 days and 24 days respectively. The median survival time of female and male was 3 days and 6 days respectively. Patients with hypertension stayed 4 days of which 72.13% were death. However, patients with no hypertension stayed 9 days on average of which 27.87% were death. Patients with baseline complication stayed 4 days on average of which 67.3 % were death (Table 1).
The Kaplan- Meier Estimate
The estimate for overall Kaplan –Meier survivor function depicted that relatively a large number of the deaths occurred at the earlier days of anti-stroke treatment and the same graph showed the decrement over a follow up period (Figure 1).
Kaplan-Meier Estimates is represented by the survival curves for without hypertension diseases are above those the patients’ complications with hypertension. This implied that the patients without hypertension more survival than with hypertension (Figure 2).
Log Rank Tests of Each Covariate
The log-rank test indicates that statistically there is a significant difference of survival experience among groups of gender, age, blood pressure (hypertension), and baseline complication. On the other hand, there are statistically no significant difference in survival/death experiencing among groups of the categorical covariates cardiac disease, diabetes mellitus, stroke type and drug type. Accordingly, the mean survival time of male patients’ to death had been 9.1 days greater than females 5.1 days with [95% CI [8.04889, 10.3094] (Table 2).
Univariable Analysis of Cox PH regression model
from the outputs in univariable analysis, we can observe that the covariate age of stroke patient (HR=1.011731,P-value= 0.0119),gender of patient (HR=0.4123,P-value=1.58e-06), hypertension (HR=2.5510,P-value=2.4e-06), stroketype(HR= 0.6152, 1.7325, P-value= 0.0140, 0.0961),drug type and baseline complication (HR =3.0710,P-value = 4.84e-08) are significant and hypertension, baseline complication, gender and stroke type are highly significant in the univariable analysis. However, diabetes mellitus and cardiac is not a significant factor for the death time at 25% level of significance (Table 3).
Multivariable analysis of Cox PH Regression Model.
Covariates which become insignificant in the multivariable analysis were removed from the model by using stepwise elimination technique. Accordingly, cardiac disease, and Diabetes mellitus were excluded. In order to decide whether or not a variable is significant, the p-value associated with each Parameter has been estimated and variables that have p-value less than 0.05 cut point or 5% significance level are considered as important variables and hence, are included in the final model (Table 4).
Checking for the Linearity of Continuous Covariates in the Model
For the covariates age the plots show systematic patterns or trends and the resulting smoothed plots are not a straight line. Therefore the plots of martingale residual confirm that age of a patient have no linear relationship with the survival time (Figure 3).
Checking of Proportional hazard assumption
The test of correlation (rho) is insignificant that indicates proportional hazards assumption is fulfilled. Variables age, gender of patients, Hypertension (blood pressure), base line complication ,drug type and Stroke type are fulfilled the assumption because all the p values are greater than 0.05. In Schoenfeld if the p value is greater than 0.05 it indicates that the Cox proportional hazards assumptions are fulfilled (Table 5).
Diagnosis of the Model: The likelihood ratio and significance of the final Cox PH model
From the likelihood ratio test, it can be seen that the PH model is significant since p-value is less than 5% (Table 6).
PARAMETRIC PH REGRESSION ANALYSIS
The Weibull model appears to be an appropriate PH regression model according to AIC compared with other regression models in multivariate analysis (Table 7).
From the Weibull regression model, after fixing other coefficients, the hazard rate for, male stroke patient 1.4 times than the hazard rate of females patients. The hazard rate of patients with baseline complication was 0.8 times that of patients who had no baseline complications. Finally, the hazard rate of patients who had hypertension was 0.7 times that of patients who had no hypertension. The interpretation of the result from the fitted final Weibull PH model is based on the hazard ratios (Table 8).