{"product_id":"competing-risks-a-practical-perspective-22-statistics-in-practice-9780470870686","title":"Competing Risks A Practical Perspective 22","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe need to understand, interpret and analyse competing risk data is key to many areas of science, particularly medical research.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"\u003ci\u003eCompeting Risks: A Practical Perspective\u003c\/i\u003e is a second text in the field that will help statisticians and researchers understand the complexity of the competing-risks problem and to complete the required analysis. I am glad to have it on my shelf. It meets the state goal of the Statistics in Practice series.\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, August 2008)  \u003cp\u003e\"Will help statisticians and researchers understand the complexity of the competing-risks problem and to complete the analysis. I am glad to have it on my shelf.\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, August 2008)\u003c\/p\u003e \u003cp\u003e\"...a concise introduction to the field of competing risks in survival analysis, especially useful for practitioners and researchers in the biostatistics field.\" (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 2007)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003eAcknowledgements.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Historical notes.\u003c\/p\u003e \u003cp\u003e1.2 Defining competing risks.\u003c\/p\u003e \u003cp\u003e1.3 Use of the Kaplan–Meier method in the presence of competing risks.\u003c\/p\u003e \u003cp\u003e1.4 Testing in the competing risk framework.\u003c\/p\u003e \u003cp\u003e1.5 Sample size calculation.\u003c\/p\u003e \u003cp\u003e1.6 Examples.\u003c\/p\u003e \u003cp\u003e1.6.1 Tamoxifen trial.\u003c\/p\u003e \u003cp\u003e1.6.2 Hypoxia study.\u003c\/p\u003e \u003cp\u003e1.6.3 Follicular cell lymphoma study.\u003c\/p\u003e \u003cp\u003e1.6.4 Bone marrow transplant study.\u003c\/p\u003e \u003cp\u003e1.6.5 Hodgkin’s disease study.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Survival – basic concepts.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 Definitions and background formulae.\u003c\/p\u003e \u003cp\u003e2.2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2.2 Basic mathematical formulae.\u003c\/p\u003e \u003cp\u003e2.2.3 Common parametric distributions.\u003c\/p\u003e \u003cp\u003e2.2.4 Censoring and assumptions.\u003c\/p\u003e \u003cp\u003e2.3 Estimation and hypothesis testing.\u003c\/p\u003e \u003cp\u003e2.3.1 Estimating the hazard and survivor functions.\u003c\/p\u003e \u003cp\u003e2.3.2 Nonparametric testing: log-rank and Wilcoxon tests.\u003c\/p\u003e \u003cp\u003e2.3.3 Proportional hazards model.\u003c\/p\u003e \u003cp\u003e2.4 Software for survival analysis.\u003c\/p\u003e \u003cp\u003e2.5 Closing remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Competing risks – definitions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Recognizing competing risks.\u003c\/p\u003e \u003cp\u003e3.1.1 Practical approaches.\u003c\/p\u003e \u003cp\u003e3.1.2 Common endpoints in medical research.\u003c\/p\u003e \u003cp\u003e3.2 Two mathematical definitions.\u003c\/p\u003e \u003cp\u003e3.2.1 Competing risks as bivariate random variable.\u003c\/p\u003e \u003cp\u003e3.2.2 Competing risks as latent failure times.\u003c\/p\u003e \u003cp\u003e3.3 Fundamental concepts.\u003c\/p\u003e \u003cp\u003e3.3.1 Competing risks as bivariate random variable.\u003c\/p\u003e \u003cp\u003e3.3.2 Competing risks as latent failure times.\u003c\/p\u003e \u003cp\u003e3.3.3 Discussion of the two approaches.\u003c\/p\u003e \u003cp\u003e3.4 Closing remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Descriptive methods for competing risks data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Product-limit estimator and competing risks.\u003c\/p\u003e \u003cp\u003e4.2 Cumulative incidence function.\u003c\/p\u003e \u003cp\u003e4.2.1 Heuristic estimation of the CIF.\u003c\/p\u003e \u003cp\u003e4.2.2 Nonparametric maximum likelihood estimation of the CIF.\u003c\/p\u003e \u003cp\u003e4.2.3 Calculating the CIF estimator.\u003c\/p\u003e \u003cp\u003e4.2.4 Variance and confidence interval for the CIF estimator.\u003c\/p\u003e \u003cp\u003e4.3 Software and examples.\u003c\/p\u003e \u003cp\u003e4.3.1 Using R.\u003c\/p\u003e \u003cp\u003e4.3.2 Using SAS.\u003c\/p\u003e \u003cp\u003e4.4 Closing remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Testing a covariate.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Testing a covariate.\u003c\/p\u003e \u003cp\u003e5.2.1 Gray’s method.\u003c\/p\u003e \u003cp\u003e5.2.2 Pepe and Mori’s method.\u003c\/p\u003e \u003cp\u003e5.3 Software and examples.\u003c\/p\u003e \u003cp\u003e5.3.1 Using R.\u003c\/p\u003e \u003cp\u003e5.3.2 Using SAS.\u003c\/p\u003e \u003cp\u003e5.4 Closing remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Modelling in the presence of competing risks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Modelling the hazard of the cumulative incidence function.\u003c\/p\u003e \u003cp\u003e6.2.1 Theoretical details.\u003c\/p\u003e \u003cp\u003e6.2.2 Model-based estimation of the CIF.\u003c\/p\u003e \u003cp\u003e6.2.3 Using R.\u003c\/p\u003e \u003cp\u003e6.3 Cox model and competing risks.\u003c\/p\u003e \u003cp\u003e6.4 Checking the model assumptions.\u003c\/p\u003e \u003cp\u003e6.4.1 Proportionality of the cause-specific hazards.\u003c\/p\u003e \u003cp\u003e6.4.2 Proportionality of the hazards of the CIF.\u003c\/p\u003e \u003cp\u003e6.4.3 Linearity assumption.\u003c\/p\u003e \u003cp\u003e6.5 Closing remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Calculating the power in the presence of competing risks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Sample size calculation when competing risks are not present.\u003c\/p\u003e \u003cp\u003e7.3 Calculating power in the presence of competing risks.\u003c\/p\u003e \u003cp\u003e7.3.1 General formulae.\u003c\/p\u003e \u003cp\u003e7.3.2 Comparing cause-specific hazards.\u003c\/p\u003e \u003cp\u003e7.3.3 Comparing hazards of the subdistributions.\u003c\/p\u003e \u003cp\u003e7.3.4 Probability of event when the exponential distribution is not a valid assumption.\u003c\/p\u003e \u003cp\u003e7.4 Examples.\u003c\/p\u003e \u003cp\u003e7.4.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.4.2 Comparing the cause-specific hazard.\u003c\/p\u003e \u003cp\u003e7.4.3 Comparing the hazard of the subdistribution.\u003c\/p\u003e \u003cp\u003e7.5 Closing remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Other issues in competing risks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Conditional probability function.\u003c\/p\u003e \u003cp\u003e8.1.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.1.2 Nonparametric estimation of the CP function.\u003c\/p\u003e \u003cp\u003e8.1.3 Variance of the CP function estimator.\u003c\/p\u003e \u003cp\u003e8.1.4 Testing a covariate.\u003c\/p\u003e \u003cp\u003e8.1.5 Using R.\u003c\/p\u003e \u003cp\u003e8.1.6 Using SAS.\u003c\/p\u003e \u003cp\u003e8.2 Comparing two types of risk in the same population.\u003c\/p\u003e \u003cp\u003e8.2.1 Theoretical background.\u003c\/p\u003e \u003cp\u003e8.2.2 Using R.\u003c\/p\u003e \u003cp\u003e8.2.3 Discussion.\u003c\/p\u003e \u003cp\u003e8.3 Identifiability and testing independence.\u003c\/p\u003e \u003cp\u003e8.4 Parametric modelling.\u003c\/p\u003e \u003cp\u003e8.4.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.4.2 Modelling the marginal distribution.\u003c\/p\u003e \u003cp\u003e8.4.3 Modelling the Weibull distribution.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Food for thought.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProblem 1: Estimation of the probability of the event of interest.\u003c\/p\u003e \u003cp\u003eProblem 2: Testing a covariate.\u003c\/p\u003e \u003cp\u003eProblem 3: Comparing the event of interest between two groups when the competing risks are different for each group.\u003c\/p\u003e \u003cp\u003eProblem 4: Information needed for sample size calculations.\u003c\/p\u003e \u003cp\u003eProblem 5: The effect of the size of the incidence of competing risks on the coefficient obtained in the model.\u003c\/p\u003e \u003cp\u003eProblem 6: The KLY test and the non-proportionality of hazards.\u003c\/p\u003e \u003cp\u003eProblem 7: The KLY and Wilcoxon tests.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA: Theoretical background.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB: Analysing competing risks data using R and SAS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402449035607,"sku":"9780470870686","price":75.56,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470870686.jpg?v=1730480430","url":"https:\/\/bookcurl.com\/products\/competing-risks-a-practical-perspective-22-statistics-in-practice-9780470870686","provider":"Book Curl","version":"1.0","type":"link"}