Over time, people have decreasing survival probability {survival function}| {survival analysis}. Survival-function estimates for ungrouped data, for example, individual patients, multiply probability of surviving interval by probability of surviving next interval, for all intervals {Kaplan Meier Survival Curve} {product limit}. Kaplan-Meier curve falls rapidly between 70% and 30% surviving and ends below 50% survival. Survival-function estimates for grouped data, for example, grouped by time interval, are number surviving at end divided by number at beginning minus half number censored for each interval, multiplying interval probabilities {life table estimate, survival} {actuarial method, survival}.
tests
Tests {log rank test} can have null hypothesis that there is no difference in survival between two groups. Mortality rate in one group is typically always higher than mortality rate in another group, and mortality-rate ratio can stay constant over time {proportional hazards}. If ratio is high enough, difference in groups is significant. Tests {stratified log-rank test} can compare two groups if there is another variable. Tests {generalized Wilcoxon test} can give more weight to early deaths.
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4-Medicine-Medical Treatments-Medical Testing
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Date Modified: 2022.0224