Group Sequential Methods with Applications to Clinical Trials
Par : ,Formats :
- Nombre de pages390
- PrésentationRelié
- FormatGrand Format
- Poids0.765 kg
- Dimensions16,0 cm × 24,2 cm × 2,8 cm
- ISBN0-8493-0316-8
- EAN9780849303166
- Date de parution01/01/2000
- ÉditeurCRC Press
Résumé
In clinical trials of new medical treatments, it is standard practice to monitor accumulating data on patient response at regular intervals. Specialized statistical methods are needed to balance the ethical and financial advantages of stopping a study early against the risk of an incorrect conclusion. These are referred to as "group sequential" methods. Research in group sequential analysis has progressed rapidly over the past 20 years.
Theory, computation and simulation now provide a firm foundation for methods meeting the key practical needs. Group Sequential Methods with Applications to Clinical Trials surveys and extends current methods for planning and conducting interim analyses. It describes group sequential stopping rules which can reduce average study length while controlling error probabilities. Procedures are presented in a manner which allows their easy implementation to the wide variety of data types arising from clinical trials.
Group Sequential Methods with Applications to Clinical Trials is an invaluable aid for medical statisticians in the pharmaceutical industry, medical research institutes and hospitals, as well as academic statisticians in biostatistics and epidemiology.
Theory, computation and simulation now provide a firm foundation for methods meeting the key practical needs. Group Sequential Methods with Applications to Clinical Trials surveys and extends current methods for planning and conducting interim analyses. It describes group sequential stopping rules which can reduce average study length while controlling error probabilities. Procedures are presented in a manner which allows their easy implementation to the wide variety of data types arising from clinical trials.
Group Sequential Methods with Applications to Clinical Trials is an invaluable aid for medical statisticians in the pharmaceutical industry, medical research institutes and hospitals, as well as academic statisticians in biostatistics and epidemiology.
In clinical trials of new medical treatments, it is standard practice to monitor accumulating data on patient response at regular intervals. Specialized statistical methods are needed to balance the ethical and financial advantages of stopping a study early against the risk of an incorrect conclusion. These are referred to as "group sequential" methods. Research in group sequential analysis has progressed rapidly over the past 20 years.
Theory, computation and simulation now provide a firm foundation for methods meeting the key practical needs. Group Sequential Methods with Applications to Clinical Trials surveys and extends current methods for planning and conducting interim analyses. It describes group sequential stopping rules which can reduce average study length while controlling error probabilities. Procedures are presented in a manner which allows their easy implementation to the wide variety of data types arising from clinical trials.
Group Sequential Methods with Applications to Clinical Trials is an invaluable aid for medical statisticians in the pharmaceutical industry, medical research institutes and hospitals, as well as academic statisticians in biostatistics and epidemiology.
Theory, computation and simulation now provide a firm foundation for methods meeting the key practical needs. Group Sequential Methods with Applications to Clinical Trials surveys and extends current methods for planning and conducting interim analyses. It describes group sequential stopping rules which can reduce average study length while controlling error probabilities. Procedures are presented in a manner which allows their easy implementation to the wide variety of data types arising from clinical trials.
Group Sequential Methods with Applications to Clinical Trials is an invaluable aid for medical statisticians in the pharmaceutical industry, medical research institutes and hospitals, as well as academic statisticians in biostatistics and epidemiology.