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HOW TO RUN
SURVIVAL ANALYSIS
TEST IN SPSS

What is the Survival Analysis?

Using Survival Analysis in Research

This easy tutorial will show you how to run the Survival Analysis Test in SPSS, and how to interpret the result.

Survival analysis is one of the advanced research methods used in many different sciences. That is to say, the name of this method reflects its beginnings in medical and demographic studies of mortality. In addition, following the introduction of the proportional hazard model by Cox in 1972, empirical analysis of event history data has become more popular and has become even more widespread.

Survival analysis is a part of statistics that deals with the analysis of time until an event occurs. To clarify, the subject of observation is a variable that having positive values, such as time in death in medicine, length of stay in the hospital, time to recovery when receiving some therapy, or in the economy strike duration, unemployment, etc.

Assumptions for Survival Analysis

When performing a Survival Analysis Test procedure the following assumptions:

  • event status: censored or event (two mutually exclusive states).
  • survival time – time to an event or censorship.
  • independence of censoring and the event.
  • a similar amount and pattern of censorship per group.

An Example: Survival Analysis Test in SPSS

This guide will explain, step by step, how to run Survival analysis Test in SPSS statistical software by using an example.

We use the data from Luke, D.A., & Homan, S.M. (1998). Researchers wanted to examine factors associated with alcohol relapse. Therefore, they had two groups of people:

the first group was people who were submitted to detoxification, and the second group was people who were submitted to another treatment.

Finally, we define the survival time (time to relapse) as the number of days from detoxification or hospitalization until the onset of drinking.

For a Kaplan-Meier survival analysis, we need at least for variables:

  • ID – case identifier; participant;
  • Survival time – For example, defined in weeks;
  • Event status – For example defined as 1 – event and 0 – censored;
  • The between-subjects factor – for example group; consists of two groups that we are comparing; 0 – detoxification and 1 – treatment.

This easy tutorial will show you how to run the Survival Analysis Test in SPSS, and how to interpret the result.

How to Run Survival Analysis Test in SPSS: Explanation Step by Step

From the SPSS menu, choose Analyze – Survival – Kaplan-Meier

Survival Analysis in SPSS

A new window will open (Kaplan-Meier dialogue box).

Kaplan-Meier dialogue box in SPSS

From the left box transfer variable Weeks into Time box, variable Event into the Status box, and variable Group into Factor box.

SPSS Menu for Kaplan Meier

Click on Define Event and a new window will open.

Define Event in SPSS Menu

Click on the Compare factor button and a new window will open.

please Choose Log-rank under test Statistics and Pooled over strata and click on the Continue button.

Compare Factor in SPSS Menu

Click on the Options button and a new window will open.

Choose Survival table(s) and Mean and median survival under Statistics and Survival under Plots.

Compare Factor in SPSS Menu

The output window will open.

Survival Analysis Result in SPSS

How to report Survival Analysis Test results: Explanation Step by Step

How to Report Case Processing Summary Table in SPSS Output?

The first table Case Processing Summary shows the total number of observations in each group.

Survival analysis SPSS Output

How to Report Survival Table in SPSS Output?

Survival table shows time and status in each group.

Survival Analysis SPSS Output

How to Report Means and Medians for Survival Time Table SPSS Output?

It shows mean, median, and standard errors and 95% confidence intervals for each group.

Survival analysis SPSS Output

How to Report Overall Comparisons Table in SPSS output?

The table shows the results of Log Rank (Mantel-Cox), a test of equality of survival distributions for the different levels of the group.

For example, our p-value is 0.112. Therefore, we fail to reject the null hypothesis and conclude that there is no difference in the overall survival distributions between the groups.

Overall Comparosins Table in SPSS Output

How to Interpret a Survival Analysis Results in APA Style?

A Survival analysis (Kaplan-Meier) was used to determine whether there were differences in the survival distribution for the different types of intervention: detoxification and treatment. So, the results were not statistically significant, χ2(1) = 2.528, p = 0.112 < 0.05. Therefore, we fail to reject the null hypothesis and conclude that there is no difference in the overall survival distributions for the two interventions.

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