New PDF release: An Introduction to Survival Analysis Using Stata

By Mario Cleves, Visit Amazon's William Gould Page, search results, Learn about Author Central, William Gould, , Yulia Marchenko

ISBN-10: 1597180742

ISBN-13: 9781597180740

An advent to Survival research utilizing Stata, 3rd Edition offers the basis to appreciate numerous methods for reading time-to-event info. it's not just a educational for studying survival research but in addition a worthy reference for utilizing Stata to investigate survival info. even though the ebook assumes wisdom of statistical ideas, easy likelihood, and easy Stata, it takes a pragmatic, instead of mathematical, method of the subject.

This up to date 3rd variation highlights new good points of Stata eleven, together with competing-risks research and the remedy of lacking values through a number of imputation. different additions contain new diagnostic measures after Cox regression, Stata’s new remedy of specific variables and interactions, and a brand new syntax for acquiring prediction and diagnostics after Cox regression.

After studying this e-book, you'll comprehend the formulation and achieve instinct approximately how numerous survival research estimators paintings and what info they make the most. additionally, you will collect deeper, extra accomplished wisdom of the syntax, positive aspects, and underpinnings of Stata’s survival research routines.

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Additional resources for An Introduction to Survival Analysis Using Stata

Sample text

You are studying the time to death for a certain type of cancer patient. When do two identical patients face the same risk? You decide it is at the same time since the onset of cancer. Fine, but when is the onset of the cancer, at detection? Are two patients really the same when, at t = 0, one reports to the clinic with metastatic cancer invading other organs and the other with a small, barely detectable tumor? Or is the onset of risk when the first cancer cell differentiates? If so, how would you know when that was?

Assume that a patient visits a clinic at month 6 and then at month 7. At the 6-month evaluation, the patient was negative for the event of interest; at month 7, she was positive. That is, the failure event occurred at some point between the sixth and seventh evaluations. Because we do not know exactly when the failure event occurred, this observation is interval-censored. Stata does not directly handle this kind of censoring, but a strong argument can be made that it should. This kind of censoring is easy to handle in parametric models and difficult in semiparametric models.

The basic idea behind maximum likelihood estimation is that, given a set of observations (h, t 2 , ... , tn), the best estimate of f3 is the one that maximizes the probability, or likelihood, of observing those particular data. Maximum likelihood estimates also have nice statistical properties that make inference and testing analogous to what you would see in simple OLS regression; see Casella and Berger (2002) or another graduate text on mathematical statistics for a thorough discussion of likelihood theory.

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An Introduction to Survival Analysis Using Stata by Mario Cleves, Visit Amazon's William Gould Page, search results, Learn about Author Central, William Gould, , Yulia Marchenko


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