Compare independent groups on the temporal aspects of a dichotomous categorical outcome
Censoring is an important concept that needs to be understood when conducting this type of analysis. Most Kaplan-Meier curves are conducted with 1-year, 3-year, and 5-year time intervals to allow for enough outcomes to occur and generate relevant effects. As people are assigned to groups, they may fall out of the study for any number of reasons such as loss to follow-up, attrition, or various other reasons. These observations are censored in the analysis so as to not bias the results of one group versus another as participants leave the study. Censoring allows for study participants with different times of follow-up to be included in the analysis if they had not experienced they outcome by the time they drop out of the study.
A primary assumption of Kaplan-Meier analysis is that study participants that are censored followed the same progression of the disease state or outcome as participants that were not censored. Thus, censoring must occur at random and independent of the outcome.
When comparing three or more independent groups on their "time-to-event" in a survival analysis, significant main effects from the Log-Rank test are treated the same as regular analyses with pairwise post hoc comparisons. Run all pairwise comparisons using separate Kaplan-Meier curves to understand where among the multiple groups that the significant main effect exists. The Bonferroni correction should be used when testing multiple groups concurrently with the Log-Rank test.
The steps for conducting a Kaplan-Meier in SPSS
2. Click Analyze.
3. Drag the cursor over the Survival drop-down menu.
4. Click on Kaplan-Meier.
5. Click on the "time" variable to highlight it.
6. Click on the arrow button to move the variable into the Time: box.
7. Click on the dichotomous categorical outcome variable to highlight it.
8. Click on the arrow button to move the variable into the Status: box.
9. Click on the Define Event button.
10. In the Single value: box, enter the value or "level" of the dichotomous categorical outcome variable that denotes the event has occurred. Example: "1"
11. Click Continue.
12. Click on the "grouping" or categorical predictor variable to highlight it.
13. Click on the arrow button to move the variable into the Factor: box.
14. Click on the Compare Factor button.
15. Click on the Log rank box to select it.
16. Click Continue.
17. Click the Options button.
18. In the Plots table, click on the Survival box to select it.
19. Click Continue.
20. Click OK.
The steps for interpreting the SPSS output for Kaplan-Meier
In the Means and Medians for Survival Time table, the Mean or Median Estimate of time-to-event for the independent groups are presented. Look specifically at the Estimate column to find the value.
In the Overall Comparisons table, look under the Sig. column. This is the p-value that is interpreted.
If the p-value is LESS THAN .05, then there is a statistically significant difference in time-to-event between the independent groups.
if the p-value is MORE THAN .05, then there is not a significant difference in time-to-event between the independent groups.
Researchers can further assess the independent groups visually using the Survival Functions graph. The different colored lines represent the independent groups. This graph is a curve showing cumulative survival (or event) across the "time" variable.
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