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This work provides descriptions, explanations and examples of the Bayesian approach to statistics, demonstrating the utility of Bayesian methods for analyzing real-world problems in the health sciences. The work considers the individual components of Bayesian analysis.;College or university bookstores may order five or more copies at a special student price, available on request from Marcel Dekker, Inc.
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This work provides descriptions, explanations and examples of the Bayesian approach to statistics, demonstrating the utility of Bayesian methods for analyzing real-world problems in the health sciences. The work considers the individual components of Bayesian analysis.;College or university bookstores may order five or more copies at a special student price, available on request from Marcel Dekker, Inc.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis eBooks
- Seitenzahl: 704
- Erscheinungstermin: 3. Oktober 2018
- Englisch
- ISBN-13: 9781351990752
- Artikelnr.: 73200979
- Verlag: Taylor & Francis eBooks
- Seitenzahl: 704
- Erscheinungstermin: 3. Oktober 2018
- Englisch
- ISBN-13: 9781351990752
- Artikelnr.: 73200979
- Herstellerkennzeichnung Die Herstellerinformationen sind derzeit nicht verfügbar.
Berry, Donald A.; Stangl, Dalene
Part 1 General overview: Bayesian methods in health-related research;
Bayesian approaches to randomized trials; Bayesian epidemiology. Part 2
Assessing probabilities: elicitation of prior distributions; priors for the
design and analysis of clinical trials. Part 3 Decision problems: a Weibull
model for survival data - using prediction to decide when to stop a
clinical trial; decision models in clinical recommendations development -
the stroke prevention policy model; dose-response analysis of toxic
chemicals; expected utility as a policy making tool - an environmental
health example. Part 4 Design: Bayesian hypothesis testing - interim
analysis of a clinical trial evaluating phenytoin for the prophylaxis of
early post-traumatic seizures in children; inference and design strategies
for a hierarchical logistic regression model. Part 5 Model selection: model
selection for generalized linear models via GLIB - application to nutrition
and breast cancer. Part 6 Hierarchical models: Bayesian analysis of
population pharmacokinetic and instantaneous pharmacodynamic relationships;
Bayesian and frequentist analysis of an in vivo experiment in tumor
hemodynamics; Bayesian meta-analysis of randomized trials using graphical
models for assessing the effect of extreme cold weather on schizophrenic
births; fitting and checking a two-level Poisson model - modelling patient
mortality rates in heart transplant patients. Part 7 Other topics:
analyzing rodent tumorigencitiy experiments using expert knowledge;
assessing drug interactions - tamoxifen and cyclophosphamide; Bayesian
subset analysis of a clinical trial for the treatment of HIV infections;
Bayesian modelling of binary repeated measures data with application to
crossover trials; a comparative study of perinatal mortality using a
two-component mixture model; change-point analysis of a randomized trial on
the effects of calcium supplementation on blood pressure; Bayesian
predictive inference for a binary random variable - survey
Bayesian approaches to randomized trials; Bayesian epidemiology. Part 2
Assessing probabilities: elicitation of prior distributions; priors for the
design and analysis of clinical trials. Part 3 Decision problems: a Weibull
model for survival data - using prediction to decide when to stop a
clinical trial; decision models in clinical recommendations development -
the stroke prevention policy model; dose-response analysis of toxic
chemicals; expected utility as a policy making tool - an environmental
health example. Part 4 Design: Bayesian hypothesis testing - interim
analysis of a clinical trial evaluating phenytoin for the prophylaxis of
early post-traumatic seizures in children; inference and design strategies
for a hierarchical logistic regression model. Part 5 Model selection: model
selection for generalized linear models via GLIB - application to nutrition
and breast cancer. Part 6 Hierarchical models: Bayesian analysis of
population pharmacokinetic and instantaneous pharmacodynamic relationships;
Bayesian and frequentist analysis of an in vivo experiment in tumor
hemodynamics; Bayesian meta-analysis of randomized trials using graphical
models for assessing the effect of extreme cold weather on schizophrenic
births; fitting and checking a two-level Poisson model - modelling patient
mortality rates in heart transplant patients. Part 7 Other topics:
analyzing rodent tumorigencitiy experiments using expert knowledge;
assessing drug interactions - tamoxifen and cyclophosphamide; Bayesian
subset analysis of a clinical trial for the treatment of HIV infections;
Bayesian modelling of binary repeated measures data with application to
crossover trials; a comparative study of perinatal mortality using a
two-component mixture model; change-point analysis of a randomized trial on
the effects of calcium supplementation on blood pressure; Bayesian
predictive inference for a binary random variable - survey
Part 1 General overview: Bayesian methods in health-related research;
Bayesian approaches to randomized trials; Bayesian epidemiology. Part 2
Assessing probabilities: elicitation of prior distributions; priors for the
design and analysis of clinical trials. Part 3 Decision problems: a Weibull
model for survival data - using prediction to decide when to stop a
clinical trial; decision models in clinical recommendations development -
the stroke prevention policy model; dose-response analysis of toxic
chemicals; expected utility as a policy making tool - an environmental
health example. Part 4 Design: Bayesian hypothesis testing - interim
analysis of a clinical trial evaluating phenytoin for the prophylaxis of
early post-traumatic seizures in children; inference and design strategies
for a hierarchical logistic regression model. Part 5 Model selection: model
selection for generalized linear models via GLIB - application to nutrition
and breast cancer. Part 6 Hierarchical models: Bayesian analysis of
population pharmacokinetic and instantaneous pharmacodynamic relationships;
Bayesian and frequentist analysis of an in vivo experiment in tumor
hemodynamics; Bayesian meta-analysis of randomized trials using graphical
models for assessing the effect of extreme cold weather on schizophrenic
births; fitting and checking a two-level Poisson model - modelling patient
mortality rates in heart transplant patients. Part 7 Other topics:
analyzing rodent tumorigencitiy experiments using expert knowledge;
assessing drug interactions - tamoxifen and cyclophosphamide; Bayesian
subset analysis of a clinical trial for the treatment of HIV infections;
Bayesian modelling of binary repeated measures data with application to
crossover trials; a comparative study of perinatal mortality using a
two-component mixture model; change-point analysis of a randomized trial on
the effects of calcium supplementation on blood pressure; Bayesian
predictive inference for a binary random variable - survey
Bayesian approaches to randomized trials; Bayesian epidemiology. Part 2
Assessing probabilities: elicitation of prior distributions; priors for the
design and analysis of clinical trials. Part 3 Decision problems: a Weibull
model for survival data - using prediction to decide when to stop a
clinical trial; decision models in clinical recommendations development -
the stroke prevention policy model; dose-response analysis of toxic
chemicals; expected utility as a policy making tool - an environmental
health example. Part 4 Design: Bayesian hypothesis testing - interim
analysis of a clinical trial evaluating phenytoin for the prophylaxis of
early post-traumatic seizures in children; inference and design strategies
for a hierarchical logistic regression model. Part 5 Model selection: model
selection for generalized linear models via GLIB - application to nutrition
and breast cancer. Part 6 Hierarchical models: Bayesian analysis of
population pharmacokinetic and instantaneous pharmacodynamic relationships;
Bayesian and frequentist analysis of an in vivo experiment in tumor
hemodynamics; Bayesian meta-analysis of randomized trials using graphical
models for assessing the effect of extreme cold weather on schizophrenic
births; fitting and checking a two-level Poisson model - modelling patient
mortality rates in heart transplant patients. Part 7 Other topics:
analyzing rodent tumorigencitiy experiments using expert knowledge;
assessing drug interactions - tamoxifen and cyclophosphamide; Bayesian
subset analysis of a clinical trial for the treatment of HIV infections;
Bayesian modelling of binary repeated measures data with application to
crossover trials; a comparative study of perinatal mortality using a
two-component mixture model; change-point analysis of a randomized trial on
the effects of calcium supplementation on blood pressure; Bayesian
predictive inference for a binary random variable - survey