How to Run a Survival Analysis Test in SPSS: A Comprehensive Guide|2025

Learn How to Run a Survival Analysis Test in SPSS with step-by-step instructions. Understand the process, analyze time-to-event data, and interpret results effectively.

Survival analysis is a statistical method used to analyze the time it takes for an event of interest to occur. It is commonly used in various fields such as medicine, engineering, and social sciences to study the duration until an event like death, failure, or a particular condition occurs. SPSS (Statistical Package for the Social Sciences) is one of the most widely used statistical software packages for performing survival analysis. In this paper, we will explore how to run a survival analysis test in SPSS, including detailed steps, key terms, and concepts like Kaplan-Meier estimation and Cox regression. Additionally, we will examine how to calculate survival rates and interpret the results.

Table of Contents

Understanding Survival Analysis

Survival analysis focuses on analyzing the time-to-event data. The event could be death, disease occurrence, product failure, or any other significant event. In survival analysis, the duration or “survival time” is the key variable. The goal is to estimate the survival function, which provides the probability that an individual or item survives beyond a certain time.

Key concepts in survival analysis include:

Censoring: In survival data, some subjects may not experience the event during the study period. This is known as censoring, and it can occur due to loss to follow-up or the event not occurring by the study’s end.
Survival Function: The survival function represents the probability that the event of interest has not occurred by a certain time.
Hazard Function: The hazard function is the rate at which the event of interest occurs at a specific time point.

How to Run Survival Analysis in SPSS

Running survival analysis in SPSS involves several steps, including preparing the data, selecting the correct test, and interpreting the results. Below are the steps you need to follow to run a survival analysis test in SPSS:

Step 1: Preparing the Data

Before running survival analysis in SPSS, it is essential to prepare your data correctly. The data should consist of at least two variables:

Time-to-event variable: This represents the duration of time from the beginning of the study until the event occurs (or the subject is censored).
Event indicator: This is a binary variable (1 for the event occurring, 0 for censoring).

In addition to these, you might include other covariates (e.g., age, gender, treatment type) that you want to control for in your analysis.

Step 2: Opening the Data in SPSS

Launch SPSS and open your dataset by navigating to File > Open > Data.
Ensure your data is properly formatted, with the time-to-event variable and event indicator in columns.

Step 3: Running Kaplan-Meier Survival Analysis

The Kaplan-Meier estimator is a non-parametric statistic used to estimate the survival function from lifetime data. It provides an estimate of the probability of surviving at each time point.

In SPSS, go to Analyze > Survival > Kaplan-Meier.
In the dialog box, move your time-to-event variable into the Time box and the event indicator variable into the Status box.
You can also define groups (e.g., treatment groups or gender) by moving a grouping variable into the Factor box.
Click OK to run the analysis.

SPSS will generate the Kaplan-Meier survival curve, which shows the probability of survival over time. The output will also include statistical tests like the Log Rank Test to compare survival curves across groups.

Step 4: Running Cox Proportional Hazards Regression

Cox regression is a popular method used to examine the effect of several variables on survival time. It assumes that the hazard ratios between groups are proportional over time.

To run Cox regression in SPSS, go to Analyze > Survival > Cox Regression.
In the dialog box, place your time-to-event variable in the Time box and the event indicator in the Status box.
Add the independent variables (e.g., age, treatment, gender) into the Covariates box.
Click OK to run the analysis.

The output will display hazard ratios (HR) for each covariate. A hazard ratio greater than 1 indicates that the variable increases the risk of the event occurring, while a hazard ratio less than 1 suggests a protective effect.

How to Calculate the 5-Year Survival Rate in SPSS

To calculate the 5-year survival rate in SPSS, you will first need to run a Kaplan-Meier analysis and then extract the survival probability at the 5-year mark. Here’s how to do it:

Run the Kaplan-Meier Analysis as explained in Step 3.
Look at the Survival Curve: SPSS will generate a Kaplan-Meier curve, where you can estimate the survival probability at any given time.
Locate the 5-Year Mark: Identify the 5-year point on the x-axis of the Kaplan-Meier survival curve.
Extract the Survival Probability: The survival probability at the 5-year mark is the value of the curve at that point. You can also use the SPSS output to directly obtain this probability.

Alternatively, SPSS provides summary statistics for survival analysis, including median survival times and the percentage surviving at certain time points. You can extract these figures from the output for the 5-year survival rate.

Interpreting Kaplan-Meier Survival Analysis Results

The Kaplan-Meier output includes a survival table and a plot of the survival curve. Here’s how to interpret it:

Survival Table: This table shows the number of individuals at risk at each time point, the number of events, the survival probability, and the cumulative survival probability. The cumulative survival probability at any given time is the probability that an individual will survive up to that point.
Survival Curve: The survival curve shows the proportion of subjects surviving over time. The curve typically starts at 1 (100% survival) and decreases as events occur. If the curve levels off, it indicates that no further events have occurred.

The Log Rank Test is used to compare survival curves between groups. A significant p-value (typically less than 0.05) indicates that there is a significant difference in survival between the groups.

Cox Regression in SPSS

Cox regression is a widely used method for analyzing the effect of several covariates on survival time. The Cox Proportional Hazards model is particularly useful when you want to assess the effect of multiple variables on survival, while adjusting for potential confounders.

Step 1: Run Cox Regression

Go to Analyze > Survival > Cox Regression in SPSS.
Define your time variable in the Time box and your event status variable in the Status box.
Add the independent variables (e.g., age, gender, treatment) into the Covariates box.
Click OK to run the analysis.

Step 2: Interpret the Results

Hazard Ratio (HR): The hazard ratio represents the risk of the event occurring in one group compared to another. For instance, if the hazard ratio for age is 1.5, this means that with each unit increase in age, the hazard of the event increases by 50%.
Confidence Interval (CI): The 95% confidence interval for the hazard ratio provides an estimate of the uncertainty of the HR. If the CI does not include 1, the effect is statistically significant.
p-value: A p-value less than 0.05 indicates that the variable significantly affects survival.

Conclusion

Survival analysis in SPSS is a powerful tool for analyzing time-to-event data. By understanding the basic methods of Kaplan-Meier estimation and Cox regression, researchers and analysts can draw valuable insights about survival rates and the impact of various factors on survival time. SPSS makes it easy to conduct these analyses, offering intuitive menus and detailed outputs. Whether you are calculating the 5-year survival rate or examining the effects of multiple covariates on survival, survival analysis in SPSS provides the necessary tools to analyze your data effectively.

This guide outlines the basics of how to run survival analysis tests in SPSS, including key techniques like Kaplan-Meier estimation, Cox regression, and interpreting results. By following these steps, you will be well-equipped to perform survival analysis and make informed decisions based on your findings.