Custom «Non-Parametric Measures of Survival» Essay Paper Sample

The standard life tables and the Kaplan-Meier method are both non-parametric methods used in the measure of survival. Even though both standard life tables and the Kaplan-Meier method describe a time to event data and seem to be closely related thus, similar in a way they also differ in one way or the other. As a result, of the difference between the two non-parametric methods one has an advantage over the other as a measure of survival thus, becomes the most frequent method that describes a time to event data. It is therefore, necessary to understand the similarities and differences between standard life tables and the Kaplan-Meier method besides assessing the best non-parametric methods.

The Kaplan-Meier technique as a non-parametric method uses individual survival times besides undertaking an assumption that gagging is independent of survival time. This is because Kaplan-Meier method usually measures the segment of patients in healthcare facilities that are able to live for a definite amount of time after treatment. Kaplan-Meier method, therefore, is a method that stands behind an observation censored with no relation to the cause of failure (Lee & Wang, 2003). Kaplan-Meier method is typically collective as it uses precise survival times rather than intervals in most cases to analyze survival. Kaplan-Meier method focuses mainly on the assumptions that for the respective time, the number of individual’s existence at the start of the period is accustomed as it confers to the number of individuals censored. On the other hand, Kaplan-Meier method also looks at the number of individuals who have an experience of the event as compared to the interest in the previous period.

The standard life table method is also known as the Cutler Ederer or actuarial method. The use of the standard life table method in demography is common as it estimates the survivor function thus, holds an outstanding place in the description of human mortality. More significantly, standard life table method approximates the Kaplan-Meier method thus; it becomes the most straightforward approach used to describe the survival time of a patient in a sample. Over time, standard life table technique has come up as one of the ancient approaches for evaluating the survival or failure time in a given data (Allison, 2010). This is because this technique focuses on clustered survival times that are subsequently divided into a certain number of intervals and as a result, it becomes suitable for large data sets.

The similarity between standard life tables and the Kaplan-Meier method as non-parametric methods is because they have no theoretical distribution that satisfactorily fits the data given. In addition, both methods tabulate the collected time to event data, which is solved graphically using survival curves. Likewise, both the Kaplan-Meier method and standard life tables provide an estimate of the standard errors that describe the survival function.

Even though, the standard life tables and the Kaplan-Meier method are both non-parametric methods, they vary in one way or the other. A significant difference between the two methods is during analysis as the cases of the division is based on-time intervals in the standard life tables, while the Kaplan-Meier method estimates the survival function on individual cases without any aggregation (Lee & Wang, 2003). In addition, Kaplan-Meier method reservation in the past was for small sets of data while the standard life table is common in a larger set of data, especially as a method of presenting data in randomized clinical trials.

In addition, the fact that the Kaplan-Meier method as a survival function has its results independently are not governed by grouping of data into intervals differs from that of the standard life tables as it depends on the grouping data (Allison, 2010). This is because the grouping of the data into intervals influences the standard life tables as an estimate of the survival function unlike the Kaplan-Meier method, as it is suitable for a large set of data. In addition, the product limit method of the standard life table gives a single observation in each interval.

Kaplan-Meier method is the most widely used method in the measure of survival. This is because the Kaplan-Meier estimator gives the commonly known estimator survivor function that gives a solid theoretical justification of the method as a measure of survival (Riegelman, 2005). Indeed, the Kaplan-Meier Product-Limit method has an advantage over the life table method, especially when undertaking an analysis of survival and failure time data. This is because the subsequent estimates after the measure of analysis do not depend on the grouping of the data, thus, do not make clusters into a certain number of time intervals.

The outstanding feature of the Kaplan-Meier method is that it is applicable when there is the existence of the censored data or not. This is essential as Kaplan-Meier method focuses mainly on the right censoring unlike the standard life table. As a result, it can track patients who withdraw from a study before the observations of the outcome (Kahn & Sempos, 2003). Kaplan-Meier method is different from the standard life tables as it uses arbitrary small intervals that entail individuals with exact survival times. In addition, as a product limit method it uses a smaller time interval as compared to the standard life tables thus, eliminates the need for assumptions on whether withdrawals from the observation occur uniformly during the analysis besides considering the fact that the risk is constant over the interval.

Given that Kaplan-Meier method is typically collective as it uses precise survival times rather than intervals in most cases to analyze survival it stands out as the common measure of survival. However, the use of standard life tables is still common as it gives close results as compared to that of Kaplan-Meier method. There is no doubt that as an individual carries out the measure of the survival time they must always take into consideration the assumptions.