Analyzing Longitudinal Clinical Trial Data
portes grátis
Analyzing Longitudinal Clinical Trial Data
A Practical Guide
Mallinckrodt, Craig; Lipkovich, Ilya
Taylor & Francis Ltd
12/2020
332
Mole
Inglês
9780367736583
15 a 20 dias
453
Descrição não disponível.
Background and Setting. Introduction. Objectives and estimands-determining what to estimate. Study design-collecting the intended data. Example data. Mixed effects models review.
Modeling the observed data. Choice of dependent variable and statistical test. modeling covariance (correlation). Modeling means over time. Accounting for covariates. Categorical data. Model checking and verification.
Methods for dealing with missing Data. Overview of missing data. Simple and ad hoc Approaches for dealing with missing data. Direct maximum likelihood. Multiple imputation. Inverse probability. Methods for incomplete categorical data weighted generalized estimated equations. Doubly robust methods. MNAR methods. Methods for incomplete categorical data.
A comprehensive approach to study development and analyses. Developing statistical analysis plans. Example analyses of clinical trial data.
Modeling the observed data. Choice of dependent variable and statistical test. modeling covariance (correlation). Modeling means over time. Accounting for covariates. Categorical data. Model checking and verification.
Methods for dealing with missing Data. Overview of missing data. Simple and ad hoc Approaches for dealing with missing data. Direct maximum likelihood. Multiple imputation. Inverse probability. Methods for incomplete categorical data weighted generalized estimated equations. Doubly robust methods. MNAR methods. Methods for incomplete categorical data.
A comprehensive approach to study development and analyses. Developing statistical analysis plans. Example analyses of clinical trial data.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
mixed effects models;clinical trials;model formulation;missing data;MCAR;MAR
Background and Setting. Introduction. Objectives and estimands-determining what to estimate. Study design-collecting the intended data. Example data. Mixed effects models review.
Modeling the observed data. Choice of dependent variable and statistical test. modeling covariance (correlation). Modeling means over time. Accounting for covariates. Categorical data. Model checking and verification.
Methods for dealing with missing Data. Overview of missing data. Simple and ad hoc Approaches for dealing with missing data. Direct maximum likelihood. Multiple imputation. Inverse probability. Methods for incomplete categorical data weighted generalized estimated equations. Doubly robust methods. MNAR methods. Methods for incomplete categorical data.
A comprehensive approach to study development and analyses. Developing statistical analysis plans. Example analyses of clinical trial data.
Modeling the observed data. Choice of dependent variable and statistical test. modeling covariance (correlation). Modeling means over time. Accounting for covariates. Categorical data. Model checking and verification.
Methods for dealing with missing Data. Overview of missing data. Simple and ad hoc Approaches for dealing with missing data. Direct maximum likelihood. Multiple imputation. Inverse probability. Methods for incomplete categorical data weighted generalized estimated equations. Doubly robust methods. MNAR methods. Methods for incomplete categorical data.
A comprehensive approach to study development and analyses. Developing statistical analysis plans. Example analyses of clinical trial data.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
