Addressing Limitations in Observational Studies of the Association Between Glucose-Lowering Medications and All-Cause Mortality: A Review

Abstract

A growing body of observational literature on the association between glucose-lowering treatments and all-cause mortality has been accumulating in recent years. However, many investigations present designs or analyses that inadequately address the methodological challenges involved. We conducted a systematic search with a non-systematic extension to identify observational studies published between 2000 and 2012 that evaluated the effects of glucose-lowering medications on all-cause mortality. We reviewed these studies and assessed the design and analysis methods used, with a focus on their ability to address specific methodological challenges. We described these methodological issues and their potential impact on observed associations, providing examples from the reviewed literature, and suggested possible approaches to manage these methodological challenges. We evaluated 67 publications of observational studies evaluating the association between glucose-lowering treatments and all-cause mortality. The identified methodological challenges included trade-offs associated with the outcome of all-cause mortality, incorrect temporal sequencing in administrative databases, inadequate treatment of time-varying hazards and treatment duration effects, unclear definition of the exposure risk window, improper handling of time-varying exposures, and incomplete accounting for confounding by indication. Most of these methodological challenges may be adequately addressed through the application of appropriate methods. Observational research plays an increasingly important role in assessing the clinical effects of diabetes therapy. The implementation of suitable research methods can reduce the potential for spurious findings, and thus the risk of misleading the medical community about benefits and harms of diabetes therapy.

Claims‐based studies of oral glucose‐lowering medications can achieve balance in critical clinical variables only observed in electronic health records

“…An incident user design, which includes patients initiating a treatment of interest or comparator agents, avoids the problem of studying prevalent users who are “survivors” of early adverse effects, allows the evaluation of drug effects that vary over time, and ensures that baseline covariates are assessed before treatment initiation and are not affected by treatment itself . These choices resulted in fair balance in EHR‐based covariates even before PS‐matching, as a testament to the major role played by a proper study design for the achievement of overall study validity . Once adequate study design choices have been made, our findings also suggest that the inclusion of a large number of patient characteristics in the estimation of the PS can further lead to balance of unmeasured but correlated variables by proxy, and as a result, mitigate confounding by the same unmeasured covariates, as previously observed .…”

“…It is often assumed that the absence of this information in pharmacoepidemiological studies could be largely addressed by the application of state‐of‐the‐art study design and analytical choices. For example, in the context of safety and effectiveness research on diabetes therapy, using a new‐user study design enhanced by the proper choice of an active comparator drug, which tends to be used by patients at a similar stage of diabetes, could better distinguish drug effects from diabetes disease effects . Additionally, adjusting analyses via propensity score (PS) matching could leverage the vast information recorded in large claims databases to estimate treatment effects in a population with clinical equipoise regarding many aspects of care, including characteristics that may act as proxies for unmeasured information .…”

“…For example, in the context of safety and effectiveness research on diabetes therapy, using a new-user study design enhanced by the proper choice of an active comparator drug, which tends to be used by patients at a similar stage of diabetes, could better distinguish drug effects from diabetes disease effects. 14,15 Additionally, adjusting analyses via propensity score (PS) matching could leverage the vast information recorded in large claims databases to estimate treatment effects in a population with clinical equipoise regarding many aspects of care, including characteristics that may act as proxies for unmeasured information. 16,17 These strategies combined are thought to improve balance in patient characteristics, and thus mitigate the potential confounding in non-interventional pharmacoepidemiological studies.…”

Risk of acute myocardial infarction with real‐world NSAIDs depends on dose and timing of exposure

“…Weighted cumulative exposure modeling proved to be useful as a companion analysis tool. The standard model suggested that timing of past NSAID exposure mattered and WCE models emphasize a key epidemiological concept: Risk should be assessed over a time window that is etiologically relevant.…”

Acute vs cumulative benefits of metformin use in patients with type 2 diabetes and heart failure

“…Accordingly, metformin has been considered as first‐line therapy in this patient population, similarly to other populations with type 2 diabetes . Nonetheless, observational studies related to the safety and effectiveness of metformin have faced a number of important methodological challenges, including how best to model time‐varying drug exposure because treatment with metformin varies considerably both between patients and within‐patients over time . Metformin use has typically been modeled using a range of time‐fixed measures (ever‐use or total days of use), which do not fully account for the time‐varying nature of the treatment regimens and also largely misclassify exposed person‐time.…”