Empirical validation of the reverse parametric waiting time distribution and standard methods to estimate prescription durations for warfarin

A data-informed approach using individualised dispensing patterns to estimate medicine exposure periods and dose from pharmaceutical claims data

Pharmaceutical claims data are often used as the primary information source to define medicine exposure periods in pharmacoepidemiological studies. However, often critical information on directions for use and the intended duration of medicine supply are not available. In the absence of this information, alternative approaches are needed to support the assignment of exposure periods. This study summarises the key methods commonly used to estimate medicine exposure periods and dose from pharmaceutical claims data; and describes a method using individualised dispensing patterns to define time-dependent estimates of medicine exposure and dose. This method extends on important features of existing methods and also accounts for recent changes in an individual's medicine use. Specifically, this method constructs medicine exposure periods and estimates the dose used by considering characteristics from an individual's prior dispensings, accounting for the time between prior dispensings and the amount supplied at prior dispensings. Guidance on the practical applications of this method is also provided. Although developed primarily for application to databases, which do not contain duration of supply or dose information, use of this method may also facilitate investigations when such information is available and there is a need to consider individualised and/or changing dosing regimens. By shifting the reliance on prescribed duration and dose to determine exposure and dose estimates, individualised dispensing information is used to estimate patterns of exposure and dose for an individual. Reflecting real-world individualised use of medicines with complex and variable dosing regimens, this method offers a pragmatic approach that can be applied to all medicine classes.

The Random Effects Warfarin Days' Supply (REWarDS) Model: Development and Validation of a Novel Method for Estimating Exposure to Warfarin Using Administrative Data

Warfarin's complex dosing is a significant barrier to measurement of its exposure in observational studies using population databases. Using population-based administrative data (1996-2019) from British Columbia, Canada, we developed a method based on statistical modeling (Random Effects Warfarin Days' Supply (REWarDS)) that involves fitting a random-effects linear regression model to patients' cumulative dosage over time for estimation of warfarin exposure. Model parameters included a minimal universally available set of variables from prescription records for estimation of patients' individualized average daily doses of warfarin. REWarDS estimates were validated against a reference standard (manual calculation of the daily dose using the free-text administration instructions entered by the dispensing pharmacist) and compared with alternative methods (fixed window, fixed tablet, defined daily dose, and reverse wait time distribution) using Pearson's correlation coefficient (r), the intraclass correlation coefficient, and the root mean squared error. REWarDS-estimated days' supply showed strong correlation and agreement with the reference standard (r = 0.90 (95% confidence interval (CI): 0.90, 0.90); intraclass correlation coefficient = 0.95 (95% CI: 0.94, 0.95); root mean squared error = 8.24 days) and performed better than all of the alternative methods. REWarDS-estimated days' supply was valid and more accurate than estimates from all other available methods. REWarDS is expected to confer optimal precision in studies measuring warfarin exposure using administrative data.

Rationale and performances of a data-driven method for computing the duration of pharmacological prescriptions using secondary data sources

The assessment of the duration of pharmacological prescriptions is an important phase in pharmacoepidemiologic studies aiming to investigate persistence, effectiveness or safety of treatments. The Sessa Empirical Estimator (SEE) is a new data-driven method which uses k-means algorithm for computing the duration of pharmacological prescriptions in secondary data sources when this information is missing or incomplete. The SEE was used to compute durations of exposure to pharmacological treatments where simulated and real-world data were used to assess its properties comparing the exposure status extrapolated with the method with the “true” exposure status available in the simulated and real-world data. Finally, the SEE was also compared to a Researcher-Defined Duration (RDD) method. When using simulated data, the SEE showed accuracy of 96% and sensitivity of 96%, while when using real-world data, the method showed sensitivity ranging from 78.0 (nortriptyline) to 95.1% (propafenone). When compared to the RDD, the method had a lower median sensitivity of 2.29% (interquartile range 1.21–4.11%). The SEE showed good properties and may represent a promising tool to assess exposure status when information on treatment duration is not available.

Nordic Health Registry-Based Research: A Review of Health Care Systems and Key Registries

The Nordic countries are Denmark, Finland, Iceland, Norway, and Sweden and comprise a total population of approximately 27 million. The countries provide unique opportunities for joint health registry-based research in large populations with long and complete follow-up, facilitated by shared features, such as the tax-funded and public health care systems, the similar population-based registries, and the personal identity number as unique identifier of all citizens. In this review, we provide an introduction to the health care systems, key registries, and how to navigate the practical and ethical aspects of setting up such studies. For each country, we provide an overview of population statistics and health care expenditures, and describe the operational and administrative organization of the health care system. The Nordic registries provide population-based, routine, and prospective data on individuals lives and health with virtually complete follow-up and exact censoring information. We briefly describe the total population registries, birth registries, patient registries, cancer registries, prescription registries, and causes of death registries with a focus on period of coverage, selected key variables, and potential limitations. Lastly, we discuss some practical and legal perspectives. The potential of joint research is not fully exploited, mainly due to legal and practical difficulties in, for example, cross-border sharing of data. Future tasks include clear and transparent legal pathways and a framework by which practical aspects are facilitated.

A new likelihood model for analyses of pharmacoepidemiologic case-control studies which avoids decision rules for determining latent exposure status

BACKGROUND

Case-control studies based on pharmaco-epidemiological databases typically use decision rules to determine exposure status from information on dates of prescription redemptions, although this induces misclassification. The reverse Waiting Time Distribution has been suggested as a likelihood based model to estimate the latent exposure status, and we therefore suggest to extend this into a joint likelihood based model, which incorporates both the latent exposure status and the exposure-outcome association. This will achieve consistency and efficiency of the estimates, i.e. they can be expected to be asymptotically unbiased and have optimal precision.

METHODS

We established a joint likelihood for the observed case-control status and last prescription redemption before the index date. The likelihood combines the ordinary logistic regression likelihood and the reverse Waiting Time Distribution, and allows inclusion of covariates in both parts to adjust for observed confounders. We conducted a simulation study of the new model and standard models based on decision rules for exposure and the probability of being exposed, respectively, to assess the relative bias and variability of estimates. Lastly, we applied the models to a case-control study on use of nonsteroidal anti-inflammatory drugs and risk of upper-gastrointestinal bleeding.

RESULTS

In simulation studies the new model had low relative bias (< 1.4%) and largely retained nominal coverage probabilities (90.2% to 95.1% of nominal 95% confidence intervals), also when moderate misspecification was introduced. All standard methods generally had substantial bias (-21.1% to 17.0%) and low coverage probabilities (0.0% to 68.9%). When analyzing the empirical case-control study, the new method estimated the effect of nonsteroidal anti-inflammatory drugs on risk of with upper-gastrointestinal bleeding hospitalization to 2.52 (1.59 - 3.45), whereas the other methods had estimates ranging from 3.52 (2.19 - 5.65) to 5.17 (2.40 - 11.11).

CONCLUSIONS

Unlike standard methods, the proposed model gave nearly unbiased estimates with adequate coverage probabilities in simulation studies. The developed model demonstrates the potential for the reverse Waiting Time Distribution to be integrated with existing likelihood based analyses in pharmacoepidemiological studies.

Comparative effectiveness of warfarin, dabigatran, rivaroxaban and apixaban in non-valvular atrial fibrillation: A nationwide pharmacoepidemiological study

OBJECTIVE

To compare effectiveness and safety of warfarin and the direct oral anticoagulants (DOAC) dabigatran, rivaroxaban and apixaban in non-valvular atrial fibrillation in routine care.

METHODS

From nationwide registries, we identified treatment-naïve patients initiating warfarin, dabigatran, rivaroxaban or apixaban for non-valvular atrial fibrillation from July 2013 to December 2015 in Norway. We assessed prescription duration using reverse waiting time distribution. Adjusting for confounding in a Cox proportional hazards model, we estimated one-year risks for ischemic stroke, transient ischemic attack (TIA) or systemic embolism, major or clinically relevant non-major bleeding; intracranial; gastrointestinal; and other bleeding. We censored at switch of treatment or 365 days of follow-up.

RESULTS

We included 30,820 treatment-naïve patients. Compared to warfarin, the adjusted hazard ratios (HR) for ischemic stroke, TIA or systemic embolism were 0.96 (95% CI 0.71-1.28) for dabigatran, 1.12 (95% CI 0.87-1.45) for rivaroxaban and 0.97 (95% CI 0.75-1.26) for apixaban. Corresponding hazard ratios for major or clinically relevant non-major bleeding were 0.73 (95% CI 0.62-0.86) for dabigatran, 0.97 (95% CI 0.84-1.12) for rivaroxaban and 0.71 (95% CI 0.62-0.82) for apixaban. Statistically significant differences of other safety outcomes compared to warfarin were fewer intracranial bleedings with dabigatran (HR 0.28, 95% CI 0.14-0.56), rivaroxaban (HR 0.40, 95% CI 0.23-0.69) and apixaban (HR 0.56, 95% CI 0.34-0.92); fewer gastrointestinal bleedings with apixaban (HR 0.70, 95% CI 0.52-0.93); and fewer other bleedings with dabigatran (HR 0.67, 95% CI 0.55-0.81) and apixaban (HR 0.70, 95% CI 0.59-0.83).

CONCLUSION

After 1 year follow-up in treatment-naïve patients initiating oral anticoagulation for non-valvular atrial fibrillation, all DOACs were similarly effective as warfarin in prevention of ischemic stroke, TIA or systemic embolism. Safety from bleedings was similar or better, including fewer intracranial bleedings with all direct oral anticoagulants, fewer gastrointestinal bleedings with apixaban and fewer other bleedings with dabigatran and apixaban.