While there is so much spending on clinical trials for evaluation of drugs, medical devices, and other biologics, they are just as good as the gathered data and analysis. But imagine what happens when a set of data is missing. Does it result in a biased assumption, misleading the results of the treatment under development?
Missing data is a very common occurrence and can significantly impact the conclusions to be drawn from the data. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation, along with the errors in the manual data entry procedures, equipment errors, and incorrect measurements. One of the major reasons for missing data recently, has been due the Covid-19 pandemic.
It is important to have appropriate statistical methods that can be applied to accommodate this set of missing data. Also, the analysis initially planned, should be reviewed against the new structure of the data available.
We spoke about “Statistical methods for handling missing data in clinical trials during COVID-19” and provided more information about:
- Types of missing data that may occur.
- Examples of missing data patterns in impacted studies.
- Practical statistical approaches to accommodate the various types and structures of missing data.
So if you missed the webinar or wish to review the session, please find the link to the recorded webinar session below.