In the clinical research trial industry, loss to follow-up refers to patients who at one point in time were actively participating in a clinical research trial, but have become lost (either by error in a computer tracking system or by being unreachable) at the point of follow-up in the trial.
How much is too much loss to followup?
A good rule of thumb is that <5% loss leads to little bias, while >20% poses serious threats to validity. However, even less than 20% loss to follow-up can be a problem. Considering a worst-case scenario can help determine whether loss to follow-up poses a potential threat to validity.
What causes loss to follow up?
Common reasons for loss to follow-up were social or structural. These included problems with transportation, finances, and work/child care responsibilities. Among those lost to follow-up, subsequent outcomes were heterogeneous.
How do you reduce loss to follow up bias?
The only way to prevent bias from loss to follow-up is to maintain high follow up rates (>80%).
Is loss to follow up a selection bias?
Selection bias due to loss to follow up is the absolute or relative bias that arises from how participants are selected out of a given risk set 3. Here and throughout this paper, absolute bias refers to bias of an absolute measure, while relative bias pertains to the bias of a relative effect measure.
How many patients are lost to follow up?
In this study, 61% of patients were lost to follow up. The researchers identified several qualities that can be considered risk factors for losing patients to follow up. Those whose primary language isn’t English have nearly two times the risk of being lost to follow up, as do patients between the ages of 56 and 65.
What type of bias is loss to follow up?
Is lost to follow up selection bias?
How does non response cause bias?
Nonresponse bias occurs when some respondents included in the sample do not respond. The key difference here is that the error comes from an absence of respondents instead of the collection of erroneous data. Most often, this form of bias is created by refusals to participate or the inability to reach some respondents.
How do you minimize selection bias?
How to avoid selection biases
- Using random methods when selecting subgroups from populations.
- Ensuring that the subgroups selected are equivalent to the population at large in terms of their key characteristics (this method is less of a protection than the first, since typically the key characteristics are not known).
