Introduction to the Kaplan-Meier Method

The Kaplan-Meier method (Kaplan & Meier, 1958), also known as the “product-limit method,” is one of the most widely used non-parametric approaches in survival analysis. It is used to estimate the probability of survival after specific time points and allows for the comparison of survival distributions between two or more groups of a factor. Its application is extensive in clinical studies as well as in research fields where the time until the occurrence of an event, such as death, treatment failure, or implant failure, is important. For example, in a study examining the effect of drug dosage on the survival of cancerous rats, the Kaplan-Meier method can reveal differences between dosages. Similarly, in orthopedic studies, it can be used to investigate whether the time until knee replacement failure is influenced by the level of physical activity.

Assumptions of the Kaplan-Meier Method

Before applying the Kaplan-Meier method in SPSS Statistics, certain assumptions must be met to ensure that the results are valid and reliable. The first assumption is the dichotomous event status, which must include two mutually exclusive and collectively exhaustive states: censorship or event. For example, in a skin cancer study at the end of a five-year period, each participant will either have died or been censored. The second assumption is a clear and precise measurement of survival time. The Kaplan-Meier method requires knowing the exact moment when the event or censorship occurred, rather than only the time interval during which it happened. Time must be defined explicitly, whether in days, months, or years.

The third assumption is the minimization of left-censoring, which occurs when the exact starting point is unknown. If, for instance, survival time is measured from the diagnosis of cancer rather than the actual onset of the disease, then the recorded survival time is underestimated. The fourth assumption is the independence of censorship and event. This means that the reason for censorship should not be related to the probability of the event occurring. If someone withdraws from the study because they are in an advanced stage of the disease, then the independence assumption is violated, and the results may be distorted.

The fifth assumption concerns the absence of secular trends. During long-term studies, external factors such as the introduction of new treatments or national screening programs can affect the likelihood of the event occurring. In this case, the survival distribution is not homogeneous throughout the study period, which may lead to bias. Finally, the sixth assumption is that there must be a similar amount and pattern of censorship across groups. If the groups being compared display different rates of censorship or different timing patterns of censorship, then the results may be misleading and create a false impression of differences in survival.

Application in SPSS Statistics

The Kaplan-Meier analysis can be performed easily using SPSS Statistics, provided the above assumptions are satisfied. The procedure involves defining the survival time variable, which represents the time until the occurrence of the event or censorship, and the event variable, which is coded to distinguish between an event and censorship. Next, the groups to be compared are specified, such as different drug dosages or levels of physical activity. SPSS provides the ability to generate Kaplan-Meier curves, which graphically display the probability of survival over time, and also offers tests such as the log-rank test to examine whether survival distributions differ significantly between groups.

Through the application of the procedure in SPSS, tables can also be produced showing censorship rates per group, along with plots illustrating the censorship pattern. This information is crucial, as it helps verify the assumptions and ensures correct interpretation of the results.

Conclusion

The Kaplan-Meier method is particularly useful in survival analysis and is applied in a wide range of research fields. However, the reliability of its results depends heavily on meeting the assumptions related to the nature of the data, the precise definition of time, the accurate recording of censorship, and the absence of external factors that could influence the findings. SPSS Statistics provides all the necessary tools for applying the method, from estimating survival curves to statistically comparing different groups. A thorough understanding and careful application of the procedure contribute to drawing reliable conclusions and strengthening the validity of research outcomes.