A Closer Look at the Kaplan-Meier and Life Table Models in Survival Analysis

Survival analysis comprises a set of statistical methods deployed in studying the timing and occurrence of events. This paper studied survival functions with particular reference to Kaplan-Meier (K-M) estimators and Life Tables. Secondary data of In-patients who reported cases of malaria (origin state) to time until death or recovering (censored) was extracted and analyzed using Kaplan-Meir and Life Table functions in SPSS. Through this discourse, we showed how survival probabilities could be obtained and graphed. We inferred from the data provided in this paper that the mean years of life left for a new born child e0 was 61.9 years. The expectation of life was equal to 0.016, which translates to 160 deaths per 100,000 person-years. Again, the number of new born children dying before age twenty (20) was given by l₀ - l₂₀ = 100000 - 97127 = 2873. The probability of new born children dying before age twenty (20) years was 0.0287. The population survival curves for the two classes of users of ITN, after adjusting for gender, gave us a p-value of 0.002 < 0.05 which was highly significant, implying that there was a statistically significant difference between the population survival curves. For the patients who subscribed to ITNs, the probability that they would survive for at least a day after admission was 0.9, while for non-users of ITNs, the probability was 0.8, again, the probability that patients who use ITNs will survive for at least 30 days after admission was 0.6, while for non-users the probability was 0.2. It should be noted that survival analysis is suitable for studies that has to do with time until the occurrence of events. It could also be used to identify factors which significantly influence an event.