Aims Drug exposure status based on routinely collected data might be misclassified when the database contains only prescriptions from 1 type of prescriber (e.g. general practitioner and not... Show moreAims Drug exposure status based on routinely collected data might be misclassified when the database contains only prescriptions from 1 type of prescriber (e.g. general practitioner and not specialist). This study aims to quantify the impact of such exposure misclassification on the risk of major bleeding and stroke/transient ischaemic attack (TIA)associated with direct oral anticoagulants (DOACs) vs. vitamin K antagonists (VKAs).Methods Incident anticoagulant users (>12 mo free of anticoagulation use) in the Dutch PHARMO Database Network between 2008 and 2017 were included. Drug exposure was assessed using pharmacy dispensing information. The risks of hospital admission of major bleeding for DOAC vs. VKA users was assessed with Cox regression analysis, where exposure was based on all dispensings, on general practitioner (GP)-prescribed dispensings only or on specialist-prescribed dispensings only. Hazard ratios (HRs) were estimated also for hospitalization for gastrointestinal bleeding, intracranial bleeding and stroke/TIA.Results We included 99 182 VKA-initiators and 21 795 DOAC-initiators. Use of DOAC was associated with a lower risk of major bleeding compared to VKA use; HR 0.79 (95% confidence interval 0.70-0.90), 0.78 (0.68-0.91) and 0.62 (0.50-0.76), for exposure based on complete dispensing information, only GP- and only specialist-prescribed dispensings, respectively. Similar results were found for the other bleeding outcomes. For stroke/TIA the HRs were 0.96 (0.84-1.09), 1.00 (0.84-1.18) and 0.72 (0.58-0.90), respectively.Conclusion Including only GP-prescribed anticoagulant dispensings in this case did not materially impact the effect estimates compared to including all anticoagulant dispensings. Including only specialist-prescribed dispensings, however, strengthened the effect estimates. Show less
In evidence-based medicine, clinical research questions may be addressed by different study designs. This article describes when randomized controlled trials (RCT) are needed and when observational... Show moreIn evidence-based medicine, clinical research questions may be addressed by different study designs. This article describes when randomized controlled trials (RCT) are needed and when observational studies are more suitable. According to the Centre for Evidence-Based Medicine, study designs can be divided into analytic and non-analytic (descriptive) study designs. Analytic studies aim to quantify the association of an intervention (eg, treatment) or a naturally occurring exposure with an outcome. They can be subdivided into experimental (ie, RCT) and observational studies. The RCT is the best study design to evaluate theintendedeffect of an intervention, because the randomization procedure breaks the link between the allocation of the intervention and patient prognosis. If the randomization of the intervention or exposure is not possible, one needs to depend on observational analytic studies, but these studies usually suffer from bias and confounding. If the study focuses onunintendedeffects of interventions (ie, effects of an intervention that are not intended or foreseen), observational analytic studies are the most suitable study designs, provided that there is no link between the allocation of the intervention and the unintended effect. Furthermore, non-analytic studies (ie, descriptive studies) also rely on observational study designs. In summary, RCTs and observational study designs are inherently different, and depending on the study aim, they each have their own strengths and weaknesses. Show less
In nephrology, a great deal of information is measured repeatedly in patients over time, often alongside data on events of clinical interest. In this introductory article we discuss how these two... Show moreIn nephrology, a great deal of information is measured repeatedly in patients over time, often alongside data on events of clinical interest. In this introductory article we discuss how these two types of data can be simultaneously analysed using the joint model (JM) framework, illustrated by clinical examples from nephrology. As classical survival analysis and linear mixed models form the two main components of the JM framework, we will also briefly revisit these techniques. Show less
