The paired availability design requires the following four assumptions that justify analyzing data from the two time periods as if they were data from two randomization groups.
Assumption 1. Stable Ancillary Care. Between the two time periods, there are no systematic changes in patient management unrelated to the treatment of interest that would affect the probability of outcome (after any adjustment).
Assumption 2. Stable Disease Natural History. Between the two time periods, there are no systematic changes in the timing of disease-related events or the spectrum of manifestations of disease in the absence of treatment.
Assumption 3. Stable Population. Between the two time periods, there are no changes in the characteristics of the eligible population that would affect the probability of outcome.
Assumption 4. Stable Evaluation. Eligibility criteria and definitions of outcome are constant over time.
In the application to obstetric anesthesiology, Assumption 1 says that, between the two time periods, there are no systematic changes in obstetric practice unrelated to epidural analgesia that could affect the probability of C/S. Even changes in billing or reimbursement rules can have an effect on the validity of this assumption. Assumption 1 is plausible because medical centers were situated in various geographic locations and data collection took place at various times. If data were available from additional medical centers with no change in availability of epidural analgesia, investigators could estimate the change in the probability of C/S due to changes in care unrelated to the change in availability of epidural analgesia. Then Assumption 1 would say that this estimate is sufficient to adjust for any possible bias due to systematic changes in care.
We cannot think of any examples where Assumption 2 would be seriously questioned in the application involving obstetric anesthesiology. In other settings, Assumption 2 could be violated by an increase or decrease in prevalence of resistant bacteria between the two time periods.
In the application to obstetric anesthesiology, Assumption 3 says that, between the two time periods, there are no changes in the characteristics of the eligible population that would affect the probability of C/S. Assumption 3 is plausible because medical centers were restricted to those serving a closed population, such as an army medical center or the only hospital in a geographic region. In other words, Assumption 3 is plausible because it was unlikely that a woman in labor would go to a, considerably less convenient, hospital in order to receive epidural analgesia.
In the application to obstetric anesthesiology, Assumption 4 was plausible because there was no change between the two time periods in the eligibility criterion of being in labor and the determination of the outcome of Cesarean section. In contrast, in an application in oncology where eligibility is determined by stage of cancer, the use of a new or more sensitive radiologic test to stage cancer in the second time period may artifactually improve prognosis of each stage even if the treatment in the two time periods was the same (Feinstein et al. 1985
). Also in the field of oncology, the definition of an outcome of disease progression can change over time, for example with the increasing use “biochemical failure” rather than symptoms of recurrence, such as bone pain.