I'm not sure it's clear from the title: this isn't a generalist grad-stats book. It focuses exclusively on time-to-event data, with or without censoring. (But mostly with, because without is getting on toward trivial.)
Do you have much background in working with the formal mathematics behind your models, or in customizing the math to match the idiosyncracies of the data? Because Kim and Tableman are both formalists, and a decent chunk of the text is the derivations that justify the content. (Every so often, the text shouts "and WHY!" -- Kim and Tableman's version of the more traditional, "The derivation is left as an exercise for the reader.") The bulk of the rest of the text is instruction/recipes for getting S or R to crunch your data for you.
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timeasmymeasure
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Date: 2011-01-07 12:17 am (UTC)Do you have much background in working with the formal mathematics behind your models, or in customizing the math to match the idiosyncracies of the data? Because Kim and Tableman are both formalists, and a decent chunk of the text is the derivations that justify the content. (Every so often, the text shouts "and WHY!" -- Kim and Tableman's version of the more traditional, "The derivation is left as an exercise for the reader.") The bulk of the rest of the text is instruction/recipes for getting S or R to crunch your data for you.
...and because you asked about the book, and thus might have the background to be amused: a comic I did about a derivation that was one of the exercises in the book.