{'fraction_non_alphanumeric': 0.018717397974838908, 'fraction_numerical': 0.005216324025774778, 'mean_word_length': 5.848739495798319, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Letter by Nielsen and Søgaard Regarding Article, "Rivaroxaban Versus Apixaban for Stroke Prevention in Atrial Fibrillation: An Instrumental Variable Analysis of a Nationwide Cohort".

August 2020 592 © 2020 American Heart Association, Inc. To the Editor: We read with interest the paper by Bonde et al1 investigating the comparative effectiveness and safety of rivaroxaban versus apixaban among Danish atrial fibrillation patients. Confounding and selection bias are key concerns when (formally or informally) making causal inference claims in nonrandomized studies under different interventions. Under strong and untestable assumptions, instrumental variable analysis can provide potentially unbiased treatment effect estimates, despite strong unmeasured confounders. The challenge is to find strong, valid instruments. In the analytic strategy, Bonde et al formally evaluated and considered the 3 basic assumptions for facility preference to work as an instrument. However, a fourth assumption, monotonicity, has been suggested, realizing that a (facility) preference-based instrumental variable is deterministic in nature. Monotonicity assumes that all patients are compliers, that is, a patient would be prescribed rivaroxaban when seen in a facility that usually prefers rivaroxaban, and apixaban when seen in facilities with preference for apixaban. A simple contingency table can visualize the 4 possible subgroups of patients in terms of counterfactual combinations of assignment and observed exposure (readers are referred to Lousdal2 for details). As an example, consider a facility that usually prescribes rivaroxaban but prescribes apixaban to patients with a propensity for dehydration (affecting kidney function and therefore considers apixaban as a safer choice because of its lower renal excretion). Similarly, consider a facility that usually prescribes apixaban but prescribes rivaroxaban to patients with expected low adherence (assuming that once daily rivaroxaban would yield better adherence than twice-daily apixaban). Any patient with a tendency to dehydration and with poor medication adherence could be seen in either facility and would defy both facility preferences and thereby violate the monotonicity assumption. The above example may be hypothetical but underlines that the fourth assumption can be violated, resulting in biased estimates. Using a novel design by initially conducting a survey among prescribing physicians, Swanson et al3 showed that the monotonicity assumption was violated; this raised a general concern that preference-based instrumental variable estimates should be interpreted cautiously. While the methodology applied by Bonde et al has the potential for unbiased estimation of causal effects, it comes as a gift with strings attached. It is not feasible to verify that assumptions for instrument validity is fulfilled, and ill-defined preference-based instruments may lead to substantial bias. With this caveat in mind, future attempts to investigate average treatment effects of different oral anticoagulant treatments would likely benefit from following the principles in target trial approach to obtain valid causal estimates—under strong assumptions.4 Circulation: Cardiovascular Quality and Outcomes