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A01=Patrick Royston
A01=Paul C. Lambert
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Author_Patrick Royston
Author_Paul C. Lambert
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Category1=Non-Fiction
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COP=United States
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Language_English
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Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model

★★★★★
★★★★★

English

By (author): Patrick Royston Paul C. Lambert

Through real-world case studies, this book shows how to use Stata to estimate a class of flexible parametric survival models. It discusses the modeling of time-dependent and continuous covariates and looks at how relative survival can be used to measure mortality associated with a particular disease when the cause of death has not been recorded. The book describes simple quantification of differences between any two covariate patterns through calculation of time-dependent hazard ratios, hazard differences, and survival differences. See more
€84.54
€88.99
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A01=Patrick RoystonA01=Paul C. LambertAge Group_UncategorizedAuthor_Patrick RoystonAuthor_Paul C. Lambertautomatic-updateCategory1=Non-FictionCategory=MBNSCOP=United StatesDelivery_Delivery within 10-20 working daysLanguage_EnglishPA=AvailablePrice_€50 to €100PS=Activesoftlaunch
Delivery/Collection within 10-20 working days
Product Details
  • Weight: 748g
  • Dimensions: 152 x 229mm
  • Publication Date: 04 Aug 2011
  • Publisher: Stata Press
  • Publication City/Country: United States
  • Language: English
  • ISBN13: 9781597180795

About Patrick RoystonPaul C. Lambert

Patrick Royston is a senior medical statistician at the Medical Research Council London UK. He has published research papers on a variety of topics in leading statistics journals. His key interests include multivariable modeling and validation survival analysis design and analysis of clinical trials and statistical computing and algorithms. He is an associate editor of the Stata Journal. Paul Lambert is a reader in medical statistics at Leicester University UK. His main interest is in the development and application of statistical methods in population-based cancer research and related fields. He has published widely in leading statistical and medical journals.

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