What type of bias is missing data?
Missing data can be a major cause of information bias, where certain groups of people are more likely to have missing data. An example where differential recording may occur is in smoking data within medical records.
How can you reduce missing data?
One way to avoid having missing data is to simplify the study design while collecting sufficient data to address the research questions posed. Here are some of our suggestions. Focus on the research objectives of the study and only collect data that are absolutely necessary to fulfill the objectives.
How to reduce bias due to missing data?
The use of auxiliary variables related to the outcome of interest may reduce the bias due to missing data in model estimates by adding information associated with missingness to the model. Auxiliary variables are typically found in external data sources.
How does missing data affect healthcare decision making?
[…] Clinical registries, which capture information about the health and healthcare use of patients with a health condition or treatment, often contain patient-reported outcomes (PROs) that provide insights about the patient’s perspectives on their health. Missing data can affect the value of PRO data for healthcare decision-making.
How does missing data affect precision and precision?
Missing data can substantially affect the precision of estimated change in PRO scores from clinical registry data. Inclusion of auxiliary information in MI models can increase precision and reduce bias, but identifying the optimal auxiliary variable (s) may be challenging.
How to ensure the ignorability of missing data?
Another approach to ensure that the assumption about ignorability of the missing data is plausible is to use auxiliary (i.e., supplementary) variables that are potential correlates of missingness and/or the outcome of interest [ 20 ].