Spotting quality issues with limited data

(Mar 10, 2014)

In an earlier posting I showed how to use the Kaplan-Meier function to identify subtle data problems.  However, what can you do when you don't have the detailed information to build a full survival curve?  In a recent consulting engagement we were only provided with crude aggregate mortality rates for five-year age bands. This is a nuisance, because such summarisation loses important details in the data.

We had a strong suspicion that the data were of poor quality, and that the problem once again lay with the male-female mortality differential.  We therefore calculated the survival rates for males and females in five-year intervals for the portfolio in question and compared the survival differential with…

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Tags: data validation, survival rates, standard table

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