Exposure: obstetricians exposed to midwifery training opportunities during residency (binary variable: no midwives vs yes midwives; and a multilevel categorical variable grouped into quartiles: estimated percentage of normal deliveries supervised by midwives).
Outcome: one’s personal caesarean rate over the course of a clinical career (continuous outcome).
Regression models: I would choose a regression model for the “critical period”, with the hypothesis that residency training is a formative time for new physicians’ clinical decision-making style. Previous evidence has suggested that exposure to abortion training in residency predicts future provision of abortions. The cesarean rate varies dramatically over time and over institution. It would be important to understand whether elements of residency training predict stability of clinical decision making over time, as residency training may be an important target for interventions to reduce the cesarean rate.
Data sets: I don’t know of any data sets that could satisfy this answer. The American Board of Obstetrics and Gynecology does conduct periodic surveys of newly certified ob/gyn’s, and their email list was used in the above-mentioned abortion research. However, this doesn’t allow for reaching later-career Ob/Gyn’s to assess the course of an entire career. Additionally, it is difficult for Ob/Gyn’s to know and self-report their own personal caesarean rate. I could consider asking them to self-report the institution/practice caesarean rate. However, this would introduce a meso-level variable, and I am not sure how the “critical period” regression model mixes with multi-level models.