Cox Survival Analysis
Student’s Name
Institutional Analysis
Instructor
Date
Cox Survival Analysis
Survival analysis is an example of analysis techniques used to predict the duration for an event to happen. For example, the research investigating the progress of certain treatments in a given period of time. It is the best method because it expects either one more event to happen like a biological organism or the machine’s failure. In most cases, research uses the Cox proportional regression analysis to predict how independent variables are influenced by independent variables (Royston et al., 2011). However, cox survival proportional regression analysis associates the survival time and one or several independents variables. Therefore in our study, we investigate the relationship between the risk of dying and the patient group using the cox survival proportional regression analysis.
Hypotheses
H0: the risk of dying is not related to the patient treatment group.
H1: the risk of dying is related to the patient treatment group.
Call:lm(formula = Serial.Time..years. ~ Satus.At.Serial.Time..1.event..0.censored. + Group..1.Chemo.or.2.Placebo.) Residuals: Min 1Q Median 3Q Max -2.0792 -0.8771 -0.2250 0.9021 2.1292 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.812 1.499 3.878 0.00374 **Satus.At.Serial.Time..1.event..0.censored. -1.025 1.038 -0.987 0.34942 Group..1.Chemo.or.2.Placebo. -1.708 0.774 -2.207 0.05471 . —Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.341 on 9 degrees of freedomMultiple R-squared: 0.3938, Adjusted R-squared: 0.259 F-statistic: 2.923 on 2 and 9 DF, p-value: 0.1052 From the result of cox.model, the adjusted R² =0.259 with R² = .339.it means that 33.9% of cox proportional regression explains the variance in treatments. The 25.9% of adjusted R² correct Table 1 of the model summary shows the adjusted R² of our model is 0.333 with the R² = .339. This means that the linear regression explains 33.9% of the variance in the data. The adjusted R² corrects treatments. The Cox proportional hazards model’s advantage is that it is used to estimate different survival factors’ effectiveness simultaneously. It also investigates how certain factors may affect the event like infection and death at a particular time. The Cox proportional regression estimates the linear equation as; Serial. time.years (risky of dying)= 5.812- 1.025 (Status.At.Serial.Time..1.event..0.censored) -1.708 (Group..1. Chemo.or.2.Placebo). An increase in one unit of status will decrease the serial—time year (risk of dying) by 1.205. Also, as we increase one unit of groups in treatment, there will be a decrease of serial time (risk of dying) by 1.708 units. Since the p-value is greater than 0.05, we fail to reject the null hypothesis, which states that the
The risk of dying is not related to the patient treatment group (Adams et al., 2011). There is no statistical significance between the risk of death and patient treatment groups. The t-test finds that only the intercept variable is highly significant (p < 0.01). The independent variables are not significant, and we may conclude that they are no significantly different from zero.
In conclusion, cox survival analysis is effective when examining the relationship between survival time and patients. It also shows the association of patient time and one or more predictors in the model. The cox survival proportional regression analysis model shows that there is no relationship between the risk of dying and patients’ treatment groups. Thus the assumption that there is a relationship between the risky of dying and the patient’s treatment groups is not true.
References
Royston, P., & Lambert, P. C. (2011). Flexible parametric survival analysis using Stata: beyond the Cox model.
Adams, L. A., Harmsen, S., Sauver, J. L. S., Charatcharoenwitthaya, P., Enders, F. B., Therneau, T., & Angulo, P. (2010). Nonalcoholic fatty liver disease increases risk of death among patients with diabetes: a community-based cohort study. The American journal of gastroenterology, 105(7), 1567.