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We briefly discuss four types of biases including immortal time bias, immeasurable time bias, calendar time bias and confounding by contraindication.
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A parallel change in the detection of septal heart defects could lead to bias if calendar time was not considered for example, an increased or improved use of ultrasonography before or after birth could lead to a false association between SSRIs and heart defects.
To minimize such bias, local calendars were used during interviews to help the respondent to precisely remember the birthdate and the date of death of their children [ 9, 15].
Calendar time bias refers to a bias that may be caused by time-trends that may lead to differential prescribing of one drug over another.
Variations in patient entry criteria, treatments received, lack of randomization, residual confounding and the potential for calendar time bias undermines the validity of these results.
The ICRP noted "the potential for such biases probably increased with calendar time because treatment of childhood cancers, especially childhood leukaemia, has become more and more successful" (ICRP 2003a).
Using data from an entire calendar year avoids bias due to seasonal changes in the disease.
This trial design may be biased however because the effect of calendar time is unbalanced between control and intervention periods.
One further issue is potential bias when the age distribution changes over calendar time [ 28, 29].
We used the time-stratified case-crossover with a fixed and disjointed window (e.g., calendar month), avoiding the overlap bias (Janes et al. 2005).
We are not aware of any calendar, deprivation, or age/sex related bias in missing data that could confound our results, although this cannot be entirely discounted.
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