Missing data are a problem that is almost universally encountered by researchers at one point or another of their work. Understanding the reasons why data may be missing and the appropriate way to recover the lost data becomes the major question for the data analyst. To avoid bias caused by an incomplete data set, using missing data methods to impute missing values properly becomes crucial during data analysis to preserve power. Thus imputation methods can help researchers to better analyze data and yield more accurate results. ^ Missing values are a common occurrence in self-reported measurements and questionnaires of behavior studies. In many social behavior studies, self efficacy is one of the key concepts. Self efficacy refers to a ...
Includes bibliographical references (p. 190-201).Clinical trial endpoints are traditionally either p...
Self-report measures are extensively used in nursing research. Data derived from such reports can be...
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the ...
Missing data are a problem that is almost universally encountered by researchers at one point or ano...
Background: Methods for handling missing data in clinical research have been getting more attentions...
When a new treatment has similar efficacy compared to standard therapy in medical or social studies,...
Imputation methods for missing data on a time-dependent variable within time-dependent Cox models ar...
Background: We already showed the superiority of imputation of missing data (via Multivariable Imput...
Objective To compare several methods of missing data imputation for function (Health Assessment Ques...
Objective To compare several methods of missing data imputation for function (Health Assessment Ques...
Background: Multifactorial regression models are frequently used in medicine to estimate survival ra...
BACKGROUND: Longitudinal studies almost always have some individuals with missing outcomes. Inapprop...
<div><p>Background</p><p>In randomised trials of medical interventions, the most reliable analysis f...
Missing data is an eternal problem in data analysis. It is widely recognised that data is costly to ...
Missing data are prevalent in many public health studies for various reasons. For example, some subj...
Includes bibliographical references (p. 190-201).Clinical trial endpoints are traditionally either p...
Self-report measures are extensively used in nursing research. Data derived from such reports can be...
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the ...
Missing data are a problem that is almost universally encountered by researchers at one point or ano...
Background: Methods for handling missing data in clinical research have been getting more attentions...
When a new treatment has similar efficacy compared to standard therapy in medical or social studies,...
Imputation methods for missing data on a time-dependent variable within time-dependent Cox models ar...
Background: We already showed the superiority of imputation of missing data (via Multivariable Imput...
Objective To compare several methods of missing data imputation for function (Health Assessment Ques...
Objective To compare several methods of missing data imputation for function (Health Assessment Ques...
Background: Multifactorial regression models are frequently used in medicine to estimate survival ra...
BACKGROUND: Longitudinal studies almost always have some individuals with missing outcomes. Inapprop...
<div><p>Background</p><p>In randomised trials of medical interventions, the most reliable analysis f...
Missing data is an eternal problem in data analysis. It is widely recognised that data is costly to ...
Missing data are prevalent in many public health studies for various reasons. For example, some subj...
Includes bibliographical references (p. 190-201).Clinical trial endpoints are traditionally either p...
Self-report measures are extensively used in nursing research. Data derived from such reports can be...
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the ...