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This 3 page paper evaluates methods used in epidemiology. Bibliography lists 5 sources.
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3 pages (~225 words per page)
File: RT13_SA908cmp.rtf
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observation (Singer & Willett, 2002). The Kaplan-Meier method is simply an extension of the discrete time method (Singer & Willett, 2002). The KM method is tied to estimating survivor status.
For example, in epidemiology, the method is employed to guess how long patients will survive. Of course, standard life tables can be used to achieve the same ends. What is
the difference between using the Kaplan-Meier method and the ordinary life tables? Some claim that the Kaplan-Meier method is able to provide data over and above what the standard life
tables show (Woodward, 1999). Basically, Kaplan-Meier is more efficient and takes into account more specific study data. Woodward (1999) for example comments: "In the KM approach the observed event times
for the cohort being studied define the values of t at which s1 is evaluated; in the life table approach these values are usually set in advance of data collection"
(p.212). The Kaplan-Meier approach is able to get more intricately involved in the study data so that the outcome is more pertinent to the specific study as it does not
rely on generalizations as do life tables. The author compares the two methods with more specifically: "Essentially, KM estimation is life table estimation with the life table times (cut-points used
to define the intervals) taken to be equal to the times of events in the cohort" (Woodward, 1999, p.212). Additionally, another way to explain the advantage of Kaplan-Meier is as
follows: " The advantage of the Kaplan-Meier Product-Limit method over the life table method for analyzing survival and failure time data is that the resulting estimates do not depend on
the grouping of the data (into a certain number of time intervals)" ("Survival/Failure Time Analysis," 2008). It seems as if the Kaplan-Meier method is superior to using life tables because
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