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<p>A plot of the Kaplan&ndash;Meier estimate of the survival function is a series of horizontal steps of declining magnitude which, when a large enough sample is taken, approaches the true survival function for that population. The value of the survival function between successive distinct sampled observations (&quot;clicks&quot;) is assumed to be constant.</p>

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<div class="thumbcaption">An example of a Kaplan&ndash;Meier plot for two conditions associated with patient survival</div>

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<p>An important advantage of the Kaplan&ndash;Meier curve is that the method can take into account some types of <a title="Censoring (statistics)" href="/wiki/Censoring_(statistics)"><font color="#0645ad">censored data</font></a>, particularly <i>right-censoring</i>, which occurs if a patient withdraws from a study, i.e. is lost from the sample before the final outcome is observed. On the plot, small vertical tick-marks indicate losses, where a patient's survival time has been right-censored. When no truncation or censoring occurs, the Kaplan&ndash;Meier curve is equivalent to the <a title="Empirical distribution" href="/wiki/Empirical_distribution"><font color="#0645ad">empirical distribution</font></a>.</p>

<p>In <a title="Medical statistics" href="/wiki/Medical_statistics"><font color="#0645ad">medical statistics</font></a>, a typical application might involve grouping patients into categories, for instance, those with Gene A profile and those with Gene B profile. In the graph, patients with Gene B die much more quickly than those with gene A. After two years about 80% of the Gene A patients still survive, but less than half of patients with Gene B.</p>

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