Applied Surrogate Endpoint Evaluation Methods with SAS and R

When a true endpoint is difficult to use because of such factors as long follow-up times or prohibitive cost, it is sometimes possible to use a surrogate endpoint that can be measured in a more convenient or cost-effective way. This book focuses on the use of surrogate endpoint evaluation methods in practice, using SAS and R.

An important factor that affects the duration, complexity and cost of a clinical trial is the endpoint used to study the treatment's efficacy. When a true endpoint is difficult to use because of such factors as long follow-up times or prohibitive cost, it is sometimes possible to use a surrogate endpoint that can be measured in a more convenient or cost-effective way. This book focuses on the use of surrogate endpoint evaluation methods in practice, using SAS and R.

Autorentext

Ariel Alonso, Theophile Bigirumurame, Tomasz Burzykowski, Marc Buyse, Geert Molenberghs, Leacky Muchene, Nolen Joy Perualila, Ziv Shkedy, Wim Van der Elst

Inhalt

Introductory Material. Introduction. Notation and Example Datasets. The History of Surrogate Endpoint Validation. Contemporary Surrogate Endpoint Evaluation Methods. Multiple-Trial Surrogate Endpoint Evaluation Methods. Two Continuous Outcomes. Two Survival Endpoints. Two Categorical Endpoints. A Categorical and a Continuous Endpoint. A Survival and a Continuous Endpoint. A Survival and a Categorical Endpoint. Two Longitudinal Endpoints. A Longitudinal and a Survival Endpoint. Additional Considerations and Further Topics. Software Details. An Alternative Surrogate Endpoint Evaluation Framework: Causal-Inference. Surrogate Endpoint Evaluation Methods in Small Samples. Construction and Evaluation of Genetic Biomarkers in Early Drug Development Experiments. Additional Considerations.