Kaplan-Meier survival analysis (KMSA) is a technique that involves the generation of the tables and plots of survival or hazard function for event history data. Kaplan-Meier survival analysis (KMSA) has not been designed to assess the effects of the covariates on either function. Kaplan-Meier survival analysis (KMSA) is a descriptive procedure for the time to event variables in cases where time is the most prominent variable.
There are certain terminologies used in Kaplan-Meier survival analysis (KMSA) that help in understanding Kaplan-Meier survival analysis (KMSA).
The censored cases in Kaplan-Meier survival analysis (KMSA) refer to those types of cases where the event that is of interest for the researcher has not yet taken place. Kaplan-Meier survival analysis (KMSA) can estimate the survival functions even in the presence of such cases.
In Kaplan-Meier survival analysis (KMSA), a continuous variable exists that represents the time. The investigator should keep in mind that in Kaplan-Meier survival analysis (KMSA), the initial date must be clearly defined.
The status variable in Kaplan-Meier survival analysis (KMSA) defines the terminal event. For example, the status variable in Kaplan-Meier survival analysis (KMSA) refers to the date when the subject under study presents the first report on symptomatic relief. This variable in Kaplan-Meier survival analysis (KMSA) must be continuous and of categorical type.
The stratification variable in Kaplan-Meier survival analysis (KMSA) is of categorical type, and it represents the grouping effect. In the medical field, the stratification variable in Kaplan-Meier survival analysis (KMSA) can be the types of flu, like swine flu, bird flu, etc.
It is important for the researcher to know that Kaplan-Meier survival analysis (KMSA) will provide inaccurate results if covariates other than time are considered as the important factor in determining the duration of any particular outcome.
The factor variable in Kaplan-Meier survival analysis (KMSA) should be categorical in nature. These variables in Kaplan-Meier survival analysis (KMSA) are used to represent the causal effect of a certain outcome. For example, in the medical field, the factor variable in Kaplan-Meier survival analysis (KMSA) can be the type of a treatment applied to the type of flu.
The researcher and investigator should also keep in mind that the factor variable in Kaplan-Meier survival analysis (KMSA) is the main grouping variable, and the stratification variable in Kaplan-Meier survival analysis (KMSA) is the sub-grouping variable.
Kaplan-Meier survival analysis (KMSA) can be done by the researcher in SPSS. The following steps should be performed in order to do so:
- In SPSS, from the analyze menu, the researcher should select “survival.”
- From survival, the researcher should opt for “Kaplan-Meier survival analysis (KMSA).”
- The “compare factor button” under Kaplan-Meier survival analysis (KMSA) in SPSS helps the researcher to test the equality of the survival function under different levels of factor variables.
- The “log rank test” in Kaplan-Meier survival analysis (KMSA) under SPSS enables the researcher to test the equality of survival functions by weighing their respective time points.
- The “breslow test” in Kaplan-Meier survival analysis (KMSA) under SPSS enables the researcher to test the equality of survival functions by weighing their respective time points with respect to the number of the cases at risk at each time point. The researcher should keep in mind that in this test of Kaplan-Meier survival analysis (KMSA), there are more chances of committing a TYPE II error.
Kaplan-Meier survival analysis (KMSA) also makes assumptions. For one, it is assumed in Kaplan-Meier survival analysis (KMSA) that the events occurring in the survival function are only dependent upon the time. This is because in Kaplan-Meier survival analysis (KMSA), it has been assumed that the survival is based only on time. Therefore, this further implies that both censored and uncensored cases behave in similar fashions in the Kaplan-Meier survival analysis.
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