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Missing Data in Longitudinal Trials Part B, Analytic Issues June Kiddie, DPH; C. Hendricks Brown, PhD; Donald Seeker, PhD; Nashua Dual, PhD; Robert D. Gibbons, PhD; Jeanne Miranda, PhD; and Philip
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01
Identify the missing data in the longitudinal dataset.
02
Decide on the appropriate method for filling in the missing data based on the characteristics of the dataset.
03
Consider using techniques such as mean imputation, regression imputation, or multiple imputation to fill in the missing data.
04
Implement the chosen method to fill in the missing data.
05
Validate the imputed data to ensure it is reasonable and accurate.
06
Analyze and interpret the filled longitudinal dataset for further analysis or modeling.

Who needs missing data in longitudinal?

01
Researchers and analysts who are working with longitudinal data.
02
Data scientists and statisticians analyzing trends and patterns over time.
03
Organizations and institutions conducting studies or research involving longitudinal data.
04
Any individual or group working on data analysis projects where missing data in the longitudinal dataset needs to be addressed.
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Missing data in longitudinal refers to information that is not available or not recorded in a series of measurements or observations taken over a period of time.
Researchers or organizations conducting longitudinal studies are required to file missing data.
Missing data in longitudinal can be filled out using various statistical techniques such as imputation or modeling.
The purpose of reporting missing data in longitudinal is to ensure the accuracy and reliability of the study results.
Information such as the reason for the missing data, the method used to handle missing data, and any potential impact on the study results must be reported.
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