Prevention and handling of missing data in clinical trials / 药学学报
Acta Pharmaceutica Sinica
;
(12): 1402-1407, 2015.
Article
in Chinese
| WPRIM
| ID: wpr-320069
ABSTRACT
Missing data is a common but unavoidable issue in clinical trials. It not only lowers the trial power, but brings the bias to the trial results. Therefore, on one hand, the missing data handling methods are employed in data analysis. On the other hand, it is vital to prevent the missing data in the trials. Prevention of missing data should take the first place. From the perspective of data, firstly, some measures should be taken at the stages of protocol design, data collection and data check to enhance the patients' compliance and reduce the unnecessary missing data. Secondly, the causes of confirmed missing data in the trials should be notified and recorded in detail, which are very important to determine the mechanism of missing data and choose the suitable missing data handling methods, e.g., last observation carried forward (LOCF); multiple imputation (MI); mixed-effect model repeated measure (MMRM), etc.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Reference Standards
/
Research Design
/
Data Collection
/
Clinical Trials as Topic
/
Methods
/
Models, Theoretical
Type of study:
Practice guideline
/
Prognostic study
Limits:
Humans
Language:
Chinese
Journal:
Acta Pharmaceutica Sinica
Year:
2015
Type:
Article
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