New‐user and prevalent‐user designs and the definition of study time origin in pharmacoepidemiology: A review of reporting practices

Abstract

Background: Guidance reports for observational comparative effectiveness and drug safety research recommend implementing a new-user design whenever possible, since it reduces the risk of selection bias in exposure effect estimation compared to a prevalent-user design. The uptake of this guidance has not been studied extensively.Methods: We reviewed 89 observational effectiveness and safety cohort studies published in six pharmacoepidemiological journals in 2018 and 2019. We developed an extraction tool to assess how frequently new-user and prevalent-user designs were reported to be implemented. For studies that implemented a new-user design in both treatment arms, we extracted information about the extent to which the moment of meeting eligibility criteria, treatment initiation, and start of follow-up were reported to be aligned. Results:Of the 89 studies included, 40% reported implementing a new-user design for both the study exposure arm and the comparator arm, while 13% reported implementing a prevalent-user design in both arms. The moment of meeting eligibility criteria, treatment initiation, and start of follow-up were reported to be aligned in both treatment arms in 53% of studies that reported implementing a new-user design. We provided examples of studies that minimized the risk of introducing bias due to unclear definition of time origin in unexposed participants, immortal time, or a time lag.Conclusions: Almost half of the included studies reported implementing a new-user design. Implications of misalignment of study design origin were difficult to assess because it would require explicit reporting of the target estimand in original studies.We recommend that the choice for a particular study time origin is explicitly motivated to enable assessment of validity of the study.

Risk of bias in observational studies using routinely collected data of comparative effectiveness research: a meta-research study

“…Previous studies have also highlighted the incomplete reporting and potential bias in the implementation of observational studies. Luijken et al found that 6% of the evaluated observational studies did not specify if new users or prevalent users were included, and in only half of the studies using new user design, time point of eligibility, treatment initiation, and start of follow-up were synchronized [ 44 ]. Due to these avoidable methodological pitfalls, the results of observational studies could be biased and mislead healthcare decisions [ 45 ].…”

Real-World Effectiveness of Lipid-Lowering Medications on Outcomes after Stroke: Potential Implications of the New-User Design

“…Patients were defined as “users” or “nonusers” based on whether they had at least one dispensing of a lipid-lowering medication during the landmark period (within 90 days of discharge) [14]. Users were further classified as prevalent (continuing) or new users based on whether they were dispensed lipid-lowering medications within 90 days prior to stroke [15, 16]. Dispensing records were obtained from the PBS dataset.…”

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

“…Time-related biases are typically introduced when baseline or follow-up–related time points differ between the study groups. Therefore, aligning the three time points—meeting eligibility, initiating treatment, and starting follow-up—is crucial to reduce these biases in a comparative effectiveness study [ 11 , 12 ]. One solution is to use a new-user, active-comparator design, that is, comparing new users of treatment of interest with new users of alternative therapy (ideally with the same indication) [ 1 , 13 ].…”

“…Researchers may want to compare against non-users for their study purposes, or because of the absence of a proper active comparator. Indeed, of 89 studies on drug effectiveness or safety using RWD, 34% used a non-user comparator [ 12 ]. However, comparison with non-users poses a significant challenge in setting the time zero because non-users do not have a time point for starting treatment [ 12 ].…”

“…Indeed, of 89 studies on drug effectiveness or safety using RWD, 34% used a non-user comparator [ 12 ]. However, comparison with non-users poses a significant challenge in setting the time zero because non-users do not have a time point for starting treatment [ 12 ]. Some solutions related to time-zero problems when compared to non-users were proposed [ 11 ] and previous studies selected time zero for the non-use group in various ways: for example, at cohort entry, which is the same for the treatment group [ 15 ]; or at a different time point compared to the treatment group, including the first day of the first diagnosed month [ 16 ], a randomly selected visit date [ 17 ], and the same date as their matched patient [ 18 ].…”