|Situations||Type of bias that might arise|
a. Follow-up starts after eligibility criteria completion and treatment assignment.|
This situation happens when the follow-up starts after eligible individuals have started the treatment. The follow-up time is left-truncated, and individuals who experience early outcomes after starting treatment are not captured.
|Prevalent user bias|
b. Follow-up starts at eligibility but after treatment assignment.|
This situation happens when the follow-up starts after individuals have started the treatments, which means that the follow-up time is left-truncated. Additionally, individuals are selected based on post-treatment criteria (e.g., individuals have no outcome that occurred before the start of the follow-up).
|Prevalent user bias and selection bias due to post-treatment eligibility|
c. Follow-up starts before treatment assignment and eligibility.|
This situation happens when individuals need to meet the eligibility criteria after the follow-up has started and individuals have started treatments. For example, patients have to receive at least 2 consecutive prescriptions of treatment to be included in the analysis, but follow-up starts from the first prescription. Those who have an outcome within this time are excluded from the analysis leading to immortal time bias, or those who stop treatment after the first prescription are excluded leading to selection bias.
|Immortal time bias and selection bias due to post-treatment eligibility|
d. Follow-up starts at eligibility, but treatment is assigned later.|
This situation happens in two cases:
1) When there is a grace period, a period from when individuals meet the eligibility to when they start treatments. For example, a study compares no antibiotic use with initiation of antibiotic use within 7 days since diagnosis of urinary tract infection. If an individual starts antibiotics on day 7, it means that they have survived for 7 days leading to immortal time bias.
2) When individuals have to use the treatment for a given period to be classified in the exposed group. For example, individuals have to fill three consecutive prescriptions of aspirin to be classified as an aspirin user group, and non-aspirin users, otherwise. This also leads to immortal time bias.
Another issue that might arise from this situation is the risk of misclassification of treatment. For instance, in the example of initiating antibiotics within 7 days since diagnosis, if the individual has an outcome before day 7 and has not started the antibiotic, we are uncertain to classify her/him to the no-antibiotic user or antibiotic user.
|Immortal time bias and misclassification of treatment|