Possible alternatives if your data violate life table assumptions

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If the populations from which data to be analyzed by a life table were sampled violate one or more of the life table assumptions, the results of the analysis may be incorrect or misleading. For example, if the assumption of independence of censoring times is violated, then the estimates for survival may be biased and unreliable. If there are factors unaccounted for in the analysis that affect survival and/or censoring times, then the life table may not give useful estimates for survival. In such cases, stratification of the data or, if the individual values are available, using a nongrouped test may provide a better analysis.

The best cures for some problems--running an experiment longer or doing more aggressive follow-up to avoid a large proportion of censored values, or using a large enough sample size to lessen the problems of lengthy time intervals or the effects of grouping--are outside the scope of statistical analysis per se.

Alternative procedures:

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