Missing Data in clinical trials

Clinical trials produce significant data and aid in the advancement of the healthcare system. The missing data is shared in trials, and missing data are values that are not accessible but would be helpful for analysis if they were seen. Clinical research has been associated with a variety of factors, including trial duration, with the likelihood of missing data being higher in more extended studies, participant failure or unwillingness to keep up with evaluation appointments, difficulty in following study protocol, inadequate communication or interpersonal relationship with study subjects, unpleasant or adverse outcomes, and so on.

The missing data can be grouped into three types:

Missing completely at random (MCAR): The missing data are unrelated to the research variables, such as participant migration.

Missing not at random (MNAR): Missing data dependent on an unseen variable.

For example, individuals resistant to therapy may not come with observation.

Missing at random (MAR): In this case, the chance of missing data is related to observable variables but not unseen ones. For example, dropout owing to a documented adverse effect.

Missing data may introduce bias, contaminate the data, and invalidate the results, leading to rejection by regulatory authorities; hence, preventative procedures should be implemented throughout the trial.

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