Foreword:
The reference textbook for this column is "SPSS22.0 From Beginner to Master". Due to the software version, part of the content has changed. In order to adapt to the changes in the software version, this column is created to facilitate everyone's learning. The software used in this column is:SPSS25.0
Please click this link to download all the data files in this column:SPSS data analysis column attachment!
Table of contents
1.Cox regression model
Cox regression model (also known as proportional hazards model) is a common statistical model used in survival analysis that can be used to explore multiple predictors (such as treatment modality , biochemical indicators, etc.) on survival time, and can control the influence of other factors.
The Cox regression model is a semi-parametric model that can estimate the relationship between a dependent variable (such as death risk) and predictors (such as age, gender, treatment, etc.) without specifying the specific form of the risk distribution in advance. This model is based on the Cox proportional hazards assumption, that is, the mortality risks of different individuals are in a certain proportional relationship at any time, and this proportional factor is the same for different individuals.
In the Cox regression model, the relationship between predictors and hazard ratios is represented by covariates. Covariates refer to dependent variables related to survival time, which can be continuous, categorical, or binary (such as whether to receive treatment or not) variables. The model relates the proportional hazards of the covariates and the dependent variable and estimates the associated regression coefficients by maximizing the likelihood function.
The output results of the Cox regression model include regression coefficients, hazard ratios and confidence intervals of covariates, etc. These results can be used to evaluate whether predictors significantly affect survival time and to predict the probability of a specific event based on the combination of different predictors.
It should be noted that the Cox regression model has some basic assumptions, such as the proportional hazards assumption and the linear correlation assumption. In practical applications, attention should be paid to the applicable scope and conditions of these assumptions to avoid bias in the results.
2.SPSS implementation
(1) Open the "data15-03" data file, select "Analysis" - "Survival Analysis" - "Cox Regression", and the dialog box shown in the figure below will pop up.
(2) As shown in the figure below, select the corresponding variable into the list box, and the selection method is: Wald.
(3) Click the "Define Event" button, the dialog box shown in the figure below will pop up, and enter 0 after the single value.
(4) Click the "Classification" button to pop up the "Define Classification Covariates" dialog box. Select the corresponding variable according to the figure below and move it to the right. Then select the first one for "Histology Type" below and select the other variables. the last one. Click Continue when finished to return to the main dialog box.
(5) Click the "Graph" button to pop up the "Cox Regression: Graph" dialog box, set the corresponding options as shown below, and then click Continue to return to the main dialog box.
(6) Click the "Options" button to pop up the "Cox Regression: Options" dialog box. Check the corresponding options as shown below, and then click Continue to return to the main dialog box.
(7) After completing all settings, click the "OK" button to execute the command.
3. Result analysis
