Figure 2 .

(a) Directed acyclic graph depicting potential live birth bias: an exposure of interest A is a cause of both a birth outcome of interest Y and pregnancy loss C, with the latter two also sharing an unmeasured common cause U. Live birth datasets are by definition conditioned on C creating a biased path from the exposure to outcome of interest via collider C and U. (b) Potential for bias by controlling for intermediates in the exposure–outcome relationship. A commonly controlled for mediator M for an exposure A and outcome of interest Y is gestational age. If gestational age and the outcome of interest share an unmeasured common cause U, then controlling for M will induce a biased path from A to Y via collider M and U. In addition, the effect of A on Y via M that may be of interest is also controlled for. (c) Example of time varying confounder L(t) for the relationship of an exposure A(t) and outcome of interest Y. If the confounder is also affected by past exposure and shares an unmeasured common cause U with Y, traditional regression methods cannot account for this type of confounder and use of estimation approaches such as g-methods is recommended