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Randomized controlled trials (RCTs) are considered the “gold standard” for establishing the safety and efficacy of medical treatments, such as drugs, devices, and procedures. Patients with kidney disease are often excluded from these studies (1), and it is well established that trial participants tend to be healthier than the broader kidney disease population (2). Furthermore, the number of nephrology-specific trials conducted continues to lag behind other subspecialties (3). In the absence of RCT data, nephrology practitioners may look to population-specific observational evidence to guide therapy selection. Observational studies using real-world data ( e.g. , administrative claims and electronic healthcare record data) to evaluate the safety and effectiveness of medical treatments can provide highly generalizable and valuable information to clinicians (4). However, like nonrandomized prospective cohort studies, these studies may suffer from biases that limit their validity, such as confounding. In this commentary, I describe what confounding is and provide a brief overview of common types of confounding that can arise in observational studies of medical treatments. I then highlight some common strategies for addressing confounding and discuss potential sources of residual confounding. In an observational study, confounding occurs when a risk factor for the outcome also affects the exposure of interest, either directly or indirectly. The resultant bias can strengthen, weaken, or completely reverse the true exposure-outcome association. For a factor to be a confounder, it has to be associated with both the study exposure and the study outcome, and temporally precede the exposure ( i.e. , it cannot be an intermediary factor on the causal pathway between the exposure and the outcome) (5). ### Confounding by Indication and Examples of Other Types of Confounding Confounding by indication (6) is one of the most common forms of bias present in observational studies evaluating the safety and effectiveness of medical treatments. It occurs when the clinical indication for treatment, such as the …

Background Individuals receiving maintenance hemodialysis may be particularly susceptible to the lethal cardiac consequences of drug-induced QT prolongation because they have a substantial cardiovascular disease burden and high level of polypharmacy, as well as recurrent exposure to electrolyte shifts during dialysis. Electrophysiologic data indicate that among the selective serotonin reuptake inhibitors (SSRIs), citalopram and escitalopram prolong the QT interval to the greatest extent. However, the relative cardiac safety of SSRIs in the hemodialysis population is unknown. Methods In this retrospective cohort study, we used data from a cohort of Medicare beneficiaries receiving hemodialysis included in the US Renal Data System registry (2007–2014). We used a new-user design to compare the 1-year risk of sudden cardiac death among hemodialysis patients initiating SSRIs with a higher potential for prolonging the QT interval (citalopram, escitalopram) versus the risk among those initiating SSRIs with lower QT-prolonging potential (fluoxetine, fluvoxamine, paroxetine, sertraline). We estimated adjusted hazard ratios using inverse probability of treatment weighted survival models. Nonsudden cardiac death was treated as a competing event. Results The study included 30,932 (47.1%) hemodialysis patients who initiated SSRIs with higher QT-prolonging potential and 34,722 (52.9%) who initiated SSRIs with lower QT-prolonging potential. Initiation of an SSRI with higher versus lower QT-prolonging potential was associated with higher risk of sudden cardiac death (adjusted hazard ratio, 1.18; 95% confidence interval, 1.05 to 1.31). This association was more pronounced among elderly individuals, females, patients with conduction disorders, and those treated with other non-SSRI QT-prolonging medications. Conclusions The heterogeneous QT-prolonging potential of SSRIs may differentially affect cardiac outcomes in the hemodialysis population.