Master Kaplan-Meier Survival Analysis Using SPSS Statistics
In this tutorial, we will guide you through the process of conducting Kaplan-Meier survival analysis using SPSS Statistics. This method is commonly used to estimate the survival function from lifetime data.
What is Kaplan-Meier Analysis?
The Kaplan-Meier estimator, also known as the product-limit estimator, is a non-parametric statistic used to estimate the survival function from observed survival times. It is particularly useful in medical research to measure the fraction of patients living for a certain amount of time after treatment.
When and How is Kaplan-Meier Analysis Used?
Kaplan-Meier analysis is used when the goal is to estimate survival probabilities over time, especially when the data involves right-censored observations. It is commonly used in clinical trials, reliability engineering, and other fields where time-to-event data is analyzed.
Preparing Your Data in SPSS
Before performing Kaplan-Meier analysis, ensure your data is appropriately formatted. You will need:
- A time variable representing the duration until the event or censoring.
- A status variable indicating whether the event of interest occurred (coded as 1) or the observation was censored (coded as 0).
Steps to Perform Kaplan-Meier Analysis in SPSS
Follow these steps to perform Kaplan-Meier analysis in SPSS:
- Open your dataset in SPSS.
- Go to Analyze > Survival > Kaplan-Meier.
- In the Kaplan-Meier dialog box, move your time variable to the Time box and your status variable to the Status box.
- Under the Status box, click Define Event and specify the value that indicates the occurrence of the event (usually 1).
- To include a grouping variable, move the variable into the Factor box. This allows you to compare survival curves between different groups.
- Click Options to set additional parameters, such as plotting survival functions or displaying the survival table.
- Click OK to run the analysis.
Understanding the SPSS Output
The output includes several tables and plots, with the most important ones being the survival table and the survival plot.
Survival Table
The survival table shows the number of subjects at risk, the number of events, the cumulative proportion surviving, and the standard error at each time point. Here’s an example table:
| Time | Number at Risk | Number of Events | Cumulative Proportion Surviving | Standard Error |
|---|---|---|---|---|
| 1 | 50 | 2 | 0.96 | 0.03 |
| 2 | 48 | 3 | 0.92 | 0.04 |
Survival Plot
The survival plot provides a visual representation of the survival distribution. It shows the estimated survival probability over time. Below is an example of a survival plot:
Options for Kaplan-Meier Analysis in SPSS
When performing Kaplan-Meier analysis, you can specify various options:
- Display: Choose to display survival tables and/or plots.
- Statistics: Select options to display median survival times, survival functions, etc.
- Plots: Customize the appearance of survival plots, including the display of confidence intervals.
APA Style Results Interpretation
When reporting the results of a Kaplan-Meier analysis, include the following information:
- The median survival time.
- The survival probabilities at specific time points.
- The number of subjects at risk at each time point.
Example: The median survival time was 15 months. The survival probability at 6 months was 0.85 (SE = 0.05), and at 12 months it was 0.60 (SE = 0.07).