Abstract
To control for protopathic bias, some studies have incorporated the concept of lag-time into their exposure definition (time period before the index date that was not considered in assessing exposure). The objective of this study was to introduce a procedure to identify the best lag-time to be applied in studies where control for protopathic bias is required.
We used data from a case-control study carried out to assess the association between exposure to proton pump inhibitors (PPIs) and risk of gastric cancer, using RAMQ databases. Exposure was defined as the number of defined daily doses of PPIs dispensed during the 5-year period prior to the index date (divided into four quartiles). Thirty-one different lag-times were applied (0-30 months) based on 1-month intervals. Logistic regression was used to estimate the matched odds ratio (OR) for each lag-time. The change point in the ln(ORs) was identified by applying a two-compartmental model and a segmented regression model.
A trend of decreasing ORs was found with the application of an increasing lag-time. As an illustration, the ORs for the 1st quartile of defined daily doses, when applying the 31 different lag-times, ranged between 3.52 when applying a 0 lag-time and 0.97 when applying a 30 months lag-time. Applying the two methods for the different lag-times showed that the ORs stabilized at around 6 months.
For the purpose of controlling for protopathic bias in pharmacoepidemiological studies, we have provided a method to assess the most appropriate lag-time that should be applied for the assessment of drug exposure.