Impact of "time zero" of Follow-Up Settings in a Comparative Effectiveness Study Using Real-World Data with a Non-user Comparator: Comparison of Six Different Settings - Ryozo Wakabayashi; Takahiro Hirano; Thomas Laurent; Yoshiki Kuwatsuru; Ryohei Kuwatsuru (Adis, Springer Healthcare, 2022)
Abstract Background Time-related bias can lead to misleading conclusions. Properly setting the "time zero" of follow-up is crucial for avoiding these biases. However, the time-zero setting is challenging when comparing users and non-users of a study drug because the latter do not have a time point for starting treatment. Objective This methodological study aimed to illustrate the impact of different time-zero settings on effect estimates in a comparative effectiveness study using real-world data with a non-user comparator. Methods Data for type 2 diabetes patients were extracted from an administrative claims database, and the onset of diabetic retinopathy (study outcome) was compared between users (treatment group) and non-users (non-use group) of lipid-lowering agents. We applied six time-zero settings to the same dataset. The adjusted hazard ratio (HR) for the outcome was estimated using a Cox regression model in each time-zero setting, and the obtained results were compared among the settings. Results Of the six settings, three (study entry date [SED] vs SED [naïve approach], treatment initiation [TI] vs SED, TI vs Matched [random order]) showed that the treatment had a reduced risk of the outcome (HR [95% CI]: 0.65 [0.61-0.69], 0.92 [0.86-0.97], and 0.76 [0.71-0.82], respectively), one (TI vs Random) had an increased risk (HR [95% CI]: 1.52 [1.40-1.64]) , and two (SED vs SED [cloning method], and TI vs Matched [systematic order]) had neither increased nor decreased risk (HR [95% CI]: 0.95 [0.93-1.13], and 0.99 [0.93-1.07], respectively). Conclusions This study demonstrates that different time-zero settings can lead to different conclusions, even if the same dataset is analyzed for the same research question, probably because improper settings can introduce bias. To minimize such biases, researchers should carefully define time zero, particularly when designing a non-user comparator study using real-world data.