Abstract
A common problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect post-dropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many.
Highlights
With the exception of counting deaths from all causes, a common problem in clinical trials is the missing data caused by patients who do not complete the study in full schedule and drop out of the study without further measurements
The issue of what to do about missing data caused by dropouts in clinical trials is a research topic that is still under development in statistical literature
As has been noted in the International Conference on Harmonization (ICH)-E9 guideline [9], 'no universally applicable methods of handling missing values can be recommended.' The issue of handling missing data is intrinsically difficult because it requires a large proportion of missing data to investigate a method
Summary
With the exception of counting deaths from all causes, a common problem in clinical trials is the missing data caused by patients who do not complete the study in full schedule and drop out of the study without further measurements. The Nuremberg Code, adopted in 1947, established principles of ethical conduct in such trials. These principles demand that the subject be given the choice stop participating at any time during the clinical study. Under these principles, the investigator is obliged to stop the experiment if injury seems likely. I highlight just a few findings from recent articles in the area of cardiovascular medicine for illustration
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