Alanna Weisman1,2, Lauren K King1,2, Muhammad Mamdani1,2,3,4. 1. Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. 2. Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada. 3. Unity Health Toronto, Toronto, Ontario, Canada. 4. Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada.
Abstract
PURPOSE: In pharmacoepidemiologic studies, estimating medication adherence, persistence, and exposure patterns is critical. Constructing medication treatment episodes from prescription claims data involves assumptions related to grace period, carry-over, and lag effect, but there are no guidelines for these assumptions. We evaluated reporting and variability of these parameters in pharmacoepidemiology studies, using a case study of antihyperglycemic medications and major adverse cardiovascular events (MACE). METHODS: We conducted a systemic review using MEDLINE and EMBASE for studies published prior to January 2, 2020 comparing the risk of MACE between dipeptidyl peptidase 4 (DPP-4) inhibitors and active comparators. We extracted study characteristics and results, including grace period, carry-over, and lag effect. Risk of bias was assessed by the Newcastle-Ottawa scale, and assessments for prevalent user, immortal time, time lag, and time window biases. RESULTS: A total of 14/1850 studies identified were included. Grace period was not reported in 5 (35.7%) studies and ranged from 0 days to 180 days when reported. Carry-over was not reported in 10 studies (71.4%). Lag effect was not reported in nine (71.4%) studies and ranged from 0 days to 180 days when reported. No studies conducted sensitivity analyses examining the effects of these assumptions on study findings. Predominant biases were inadequate follow-up time, comparability of cohorts, prevalent use, and lag time bias. CONCLUSIONS: Use of grace period, carry-over, and lag effect were poorly reported and highly variable. Future pharmacoepidemiology studies should improve reporting, justify ranges for these parameters, and conduct sensitivity analyses to evaluate effects of these assumptions.
PURPOSE: In pharmacoepidemiologic studies, estimating medication adherence, persistence, and exposure patterns is critical. Constructing medication treatment episodes from prescription claims data involves assumptions related to grace period, carry-over, and lag effect, but there are no guidelines for these assumptions. We evaluated reporting and variability of these parameters in pharmacoepidemiology studies, using a case study of antihyperglycemic medications and major adverse cardiovascular events (MACE). METHODS: We conducted a systemic review using MEDLINE and EMBASE for studies published prior to January 2, 2020 comparing the risk of MACE between dipeptidyl peptidase 4 (DPP-4) inhibitors and active comparators. We extracted study characteristics and results, including grace period, carry-over, and lag effect. Risk of bias was assessed by the Newcastle-Ottawa scale, and assessments for prevalent user, immortal time, time lag, and time window biases. RESULTS: A total of 14/1850 studies identified were included. Grace period was not reported in 5 (35.7%) studies and ranged from 0 days to 180 days when reported. Carry-over was not reported in 10 studies (71.4%). Lag effect was not reported in nine (71.4%) studies and ranged from 0 days to 180 days when reported. No studies conducted sensitivity analyses examining the effects of these assumptions on study findings. Predominant biases were inadequate follow-up time, comparability of cohorts, prevalent use, and lag time bias. CONCLUSIONS: Use of grace period, carry-over, and lag effect were poorly reported and highly variable. Future pharmacoepidemiology studies should improve reporting, justify ranges for these parameters, and conduct sensitivity analyses to evaluate effects of these assumptions.
Authors: Thomas Wilke; Nils Picker; Sabrina Müller; Anna Stürmlinger; Barthold Deiters; Axel Dittmar; Jens Aberle; Maximilian Gabler Journal: Clinicoecon Outcomes Res Date: 2022-05-02