Pharmacoepidemiology and Drug Safety最新文献

{"title":"Modeling Versus Balancing Approaches to Addressing Instrumental Variables in Weighting: A Comparison of the Outcome-Adaptive Lasso, Stable Balancing Weighting, and Stable Confounder Selection.","authors":"Byeong Yeob Choi, M Alan Brookhart","doi":"10.1002/pds.70173","DOIUrl":"10.1002/pds.70173","url":null,"abstract":"<p><strong>Background: </strong>Variable selection is essential for propensity score (PS)-weighted estimators. Recent work shows that including instrumental variables (IVs), associated with only treatment but not with the outcome, can impact both the bias and precision of the PS-weighted estimators.</p><p><strong>Methods: </strong>The outcome-adaptive lasso (OAL) is an innovative model-based method adapting the popular adaptive lasso variable selection to causal inference. It attempts to identify IVs, so one can exclude them from the PS model. Unlike the model-based approach, stable balancing weighting (SBW) estimates inverse probability weights directly while minimizing the variance of the weights and covariate imbalance simultaneously. Based on its variance optimization algorithm, SBW may provide some protection against the impact of IVs. Lastly, we considered stable confounder selection (SCS), which assesses the stability of model-based effect estimates.</p><p><strong>Results: </strong>The authors present the results of simulation studies to investigate which method performs the best when moderate or strong IVs are used. The simulation studies consider IVs and spurious variables to generate extreme PSs. In simulations, SBW generally outperformed OAL and SCS in terms of reducing mean squared error, notably when the IVs were strong, and many covariates were highly correlated. Our empirical application to the effect of abciximab treatment demonstrates that SBW is a robust method to effectively handle limited overlap.</p><p><strong>Conclusions: </strong>Our numerical results support the use of SBW in situations where IVs or near-IVs may lead to practical violations of positivity assumptions.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 7","pages":"e70173"},"PeriodicalIF":2.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144507330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Core Concepts in Pharmacoepidemiology: Multi-Database Distributed Data Networks.","authors":"Rachelle Haber, Michael Webster-Clark, Nicole Pratt, Nicola Barclay, Xue Li, Judith C Maro, Robert W Platt, Daniel Prieto-Alhambra, Kristian B Filion","doi":"10.1002/pds.70177","DOIUrl":"10.1002/pds.70177","url":null,"abstract":"<p><p>Multi-database distributed data networks for post-marketing surveillance of drug safety and effectiveness use two main approaches: common data models (CDMs) and common protocols. Networks such as the U.S. Sentinel System, the Observational Health Data Sciences and Informatics (OHDSI) network, and the Data Analysis and Real-World Interrogation Network in Europe (DARWIN-EU) use a CDM approach in which participating databases are translated into a standardized structure so that a single, common analytic program can be used. On the other hand, the common protocol approach involves applying a single common protocol to site-specific data maintained in their native format, with analytic programs tailored to each data source. Some networks, such as the Canadian Network for Observational Drug Effect Studies (CNODES) and the Asian Pharmacoepidemiology Network (AsPEN), use a variety of approaches for multi-database studies. Regardless of the approach, distributed networks support comprehensive pharmacoepidemiologic studies by leveraging large-scale health data. For example, utilization studies can uncover prescribing trends in different jurisdictions and the impact of policy changes on drug use, while safety and effectiveness studies benefit from large, diverse patient populations, leading to increased precision, representativeness, and potential early detection of safety threats. Challenges include varying coding practices and data heterogeneity, which complicate the standardization of evidence and the comparability and generalizability of findings. In this Core Concepts paper, we review the purpose and different types of distributed data networks in pharmacoepidemiology, discuss their advantages and disadvantages, and describe commonly faced challenges and opportunities in conducting research using multi-database networks.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 7","pages":"e70177"},"PeriodicalIF":2.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12230205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144575986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Dimensional Disease Risk Score for Dealing With Residual Confounding Bias in Estimating Treatment Effects With a Survival Outcome.","authors":"Md Belal Hossain, Hubert Wong, Mohsen Sadatsafavi, Victoria J Cook, James C Johnston, Mohammad Ehsanul Karim","doi":"10.1002/pds.70172","DOIUrl":"10.1002/pds.70172","url":null,"abstract":"<p><strong>Purpose: </strong>Health administrative databases often contain no information on some important confounders, leading to residual confounding in the effect estimate. We aimed to explore the performance of high-dimensional disease risk score (hdDRS) to deal with residual confounding bias for estimating causal effects with survival outcomes.</p><p><strong>Methods: </strong>We used health administrative data of 49 197 individuals in British Columbia to examine the relationship between tuberculosis infection and time-to-development of cardiovascular disease (CVD). We designed a plasmode simulation exploring the performance of eight hdDRS methods that varied by different approaches to fit the risk score model and also examined results from high-dimensional propensity score (hdPS) and traditional regression adjustment. The log-hazard ratio (log-HR) was the target parameter with a true value of log(3).</p><p><strong>Results: </strong>In the presence of strong unmeasured confounding, the bias observed was -0.11 for the traditional method and -0.047 for the hdPS method. The bias ranged from -0.051 to -0.058 for hdDRS methods when risk score models were fitted to the full cohort and -0.045 to -0.049 when risk score models were fitted only to unexposed individuals. All methods showed comparable standard errors and nominal bias-eliminated coverage probabilities. With weak unmeasured confounding, hdDRS and hdPS produced approximately unbiased estimates. Our data analysis, after addressing residual confounding, revealed an 8%-11% higher CVD risk associated with tuberculosis infection.</p><p><strong>Conclusions: </strong>Our findings support the use of selected hdDRS methods to address residual confounding bias when estimating treatment effects with survival outcomes. In particular, the hdDRS method using rate-based risk score modeling on unexposed individuals consistently exhibited the least bias. However, the hdPS method showed comparable performance across most evaluated scenarios. We share reproducible R codes to facilitate researchers' adoption and further evaluation of these methods.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 7","pages":"e70172"},"PeriodicalIF":2.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144575987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prescribing Patterns of SGLT2 Inhibitors and GLP-1 Receptor Agonists in Patients With T2DM and ASCVD in South Korea.","authors":"Yeong Rok Eom, Hajung Joo, Seung Eun Chae, Nam Kyung Je","doi":"10.1002/pds.70183","DOIUrl":"10.1002/pds.70183","url":null,"abstract":"<p><strong>Background: </strong>Despite the cardiovascular benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1RA) in patients with type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD), their utilization remains low globally. This study aimed to evaluate the utilization of SGLT2i and GLP1RA in patients with T2DM and ASCVD, as well as the factors associated with their use in South Korea.</p><p><strong>Methods: </strong>We conducted a retrospective study using the National Patient Sample claims data from 2015 to 2020. Adults aged 20 years or older with confirmed diagnoses of both T2DM and ASCVD between March 1 and October 31 of each year were included. The utilization of SGLT2i and GLP1RA was assessed based on prescriptions filled within 60 days of the index date. Multivariable logistic regression was used to identify factors associated with their use. Annual trends in utilization were evaluated using the Cochran-Armitage trend test.</p><p><strong>Results: </strong>In our study of 57 576 study population, the use of SGLT2i increased from 1.20% in 2015 to 10.51% by 2020. GLP1RA usage increased from 0% to 1.17% over the same period. Older age, chronic kidney disease (OR 0.52, 95% CI 0.41-0.66), and concurrent use of dipeptidyl peptidase 4 inhibitors (DPP4i) (OR 0.09, 95% CI 0.09-0.10) significantly reduced the likelihood of SGLT2i use. In contrast, factors such as comorbid dyslipidemia (OR 1.41, 95% CI 1.25-1.60), heart failure (OR 1.22, 95% CI 1.09-1.37), concurrent use of sulfonylurea (SU) (OR 1.30, 95% CI 1.20-1.40), and prescriptions from cardiologists (OR 1.22, 95% CI 1.07-1.40) were positively associated with higher SGLT2i usage. For GLP1RA, negative influences included older age, concurrent DPP4i use (OR 0.12, 95% CI 0.08-0.16), and non-endocrinologist prescription, whereas female sex (OR 1.35, 95% CI 1.06-1.73), dyslipidemia (OR 1.68, 95% CI 1.10-2.66), and the use of insulin (OR 3.71, 95% CI 2.83-4.85), or SU (OR 3.13, 95% CI 2.44-4.02) use were positive factors.</p><p><strong>Conclusions: </strong>Despite the known cardiovascular benefits and increasing utilization trends of SGLT2i and GLP1RA, our findings reveal that 88.35% of eligible patients with T2DM and ASCVD remained untreated with these agents as of 2020. This study suggests disparities in the use of these agents based on patients' characteristics and physician specialties. Further efforts to explore and address potential barriers to the use of these agents could enhance their clinical benefits by improving access for high-risk patients.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 7","pages":"e70183"},"PeriodicalIF":2.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144529212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Supply Chain Disruptions and Drug Shortages on Drug Utilization: A Scoping Review.","authors":"Araniy Santhireswaran, Shanzeh Chaudhry, Martin Ho, Kaitlin Fuller, Etienne Gaudette, Lisa Burry, Mina Tadrous","doi":"10.1002/pds.70178","DOIUrl":"10.1002/pds.70178","url":null,"abstract":"<p><strong>Purpose: </strong>Drug shortages are a growing challenge in health systems across the world. A better understanding of the impacts of shortages on patient drug access and use will guide policies aimed at mitigating shortages. This scoping review aims to summarize observational literature assessing the impact of drug shortages on drug utilization trends.</p><p><strong>Methods: </strong>We searched Ovid MEDLINE and Ovid EMBASE for studies published between 1946 and September 17, 2024. An extensive grey literature search was conducted through targeted website searches, grey literature databases, and the Google search engine. Observational studies examining the impacts of drug shortages on drug use were included. Study screening and extraction were conducted by two independent reviewers.</p><p><strong>Results: </strong>We identified 55 published articles and five gray literature reports. Most studies were conducted in North America (n = 42, 70%). Population-level data were most used (n = 34, 57%), and most studies used drug prescription data to assess changes in use (n = 30, 55%). Most studies reported changes in drug use as a percent change, and the magnitude in decreases ranged from 1% to 99%. Many different data sources, methods, and measures were used to study the impact of drug shortages on drug utilization, and a broad range of decreases in drug utilization following the shortages were reported.</p><p><strong>Conclusions: </strong>It is important to synthesize findings across studies to understand how different drugs and settings are affected by shortages. The findings here will inform future studies aimed at filling this knowledge gap, ultimately yielding real-world evidence that can guide policy decisions to address drug supply challenges.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 7","pages":"e70178"},"PeriodicalIF":2.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12215599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144541822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.","authors":"Yuchen Guo, Victoria Y Strauss, Sara Khalid, Daniel Prieto-Alhambra","doi":"10.1002/pds.70165","DOIUrl":"10.1002/pds.70165","url":null,"abstract":"<p><strong>Purpose: </strong>The surge of treatments for COVID-19 in the second quarter of 2020 had a low prevalence of treatment and high outcome risk. Motivated by that, we conducted a simulation study comparing disease risk scores (DRS) and propensity scores (PS) using a range of scenarios with different treatment prevalences and outcome risks.</p><p><strong>Method: </strong>Four methods were used to estimate PS and DRS: logistic regression (reference method), least absolute shrinkage and selection operator (LASSO), multilayer perceptron (MLP), and XgBoost. Monte Carlo simulations generated data across 25 scenarios varying in treatment prevalence, outcome risk, data complexity, and sample size. Average treatment effects were calculated after matching. Relative bias and average absolute standardized mean difference (ASMD) were reported.</p><p><strong>Result: </strong>Estimation bias increased as treatment prevalence decreased. DRS showed lower bias than PS when treatment prevalence was below 0.1, especially in nonlinear data. However, DRS did not outperform PS in linear or small sample data. PS had comparable or lower bias than DRS when treatment prevalence was 0.1-0.5. Three machine learning (ML) methods performed similarly, with LASSO and XgBoost outperforming the reference method in some nonlinear scenarios. ASMD results indicated that DRS was less impacted by decreasing treatment prevalence compared to PS.</p><p><strong>Conclusion: </strong>Under nonlinear data, DRS reduced bias compared to PS in scenarios with low treatment prevalence, while PS was preferable for data with treatment prevalence greater than 0.1, regardless of the outcome risk. ML methods can outperform the logistic regression method for PS and DRS estimation. Both decreasing sample size and adding nonlinearity and nonadditivity in data increased bias for all methods tested.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 6","pages":"e70165"},"PeriodicalIF":2.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal Trend in Selective Cyclooxygenase-2 Inhibitors Sales in Brazilian Drugstores.","authors":"Tayanny Margarida Menezes Almeida Biase, Marcus Tolentino Silva, Larissa Lopes, Taís Freire Galvao","doi":"10.1002/pds.70168","DOIUrl":"10.1002/pds.70168","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the trends in selective cyclooxygenase-2 inhibitor anti-inflammatory drugs (coxibs) sales in Brazil from 2014 to 2021.</p><p><strong>Methods: </strong>A time trend analysis of coxibs sales in Brazil from January 2014 to December 2021 was conducted using the Brazilian National Controlled Products Management System. Primary outcomes consisted of coxibs sales in defined daily dose (DDD) and DDD per 1000 inhabitants per day (DID), analyzed by Brazilian region (North, Northeast, South, Southeast, and Midwest). The trends in coxib consumption were analyzed using a segmented regression model, and the average annual percent change (AAPC) with a 95% confidence interval (95% CI) was calculated.</p><p><strong>Results: </strong>Celecoxib and etoricoxib sales increased in Brazil from 2014 to 2021. Celecoxib sales rose from 0.2 to 0.4 DID (AAPC 15.0; 95% CI 8.9, 21.5), particularly in the South, from 0.4 in 2014 to 0.7 DID in 2021 (AAPC 11.8; 95% CI 6.9, 16.9). Etoricoxib sales increased from 0.1 to 0.2, especially in the Midwest (AAPC 10.6; 95% CI 5.5, 16.0). Northern Brazil presented the lowest coxibs' consumption, which also increased in the period. National changes in etoricoxib sales were observed between 2014 and 2018 (annual percent change, APC 12.2; 95% CI -2.0, 28.4) and 2018 and 2021 (APC -3.6; 95% CI -22.1, 19.3).</p><p><strong>Conclusion: </strong>Coxibs sales in Brazil increased from 2014 to 2021, and celecoxib was the most used coxib. Shift changes in sales were observed in some regions for both coxibs, and nationally for etoricoxib. Prescription retention requirements for coxibs sales, instituted a decade before this analysis, potentially did not reduce consumption.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 6","pages":"e70168"},"PeriodicalIF":2.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12144667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Valproic Acid-Induced Congenital Disorders, an Analysis in EudraVigilance and a Literature Review of Valproic Acid Utilization During Pregnancy.","authors":"Mădălina Hurubă, Andreea Farcaș, Daniel Leucuța, Cristina Mogoșan","doi":"10.1002/pds.70134","DOIUrl":"10.1002/pds.70134","url":null,"abstract":"<p><strong>Purpose: </strong>Valproic acid (VPA) is known to increase the risk of congenital malformations during pregnancy. We evaluated VPA-induced congenital disorder reports in EudraVigilance (EV), before and after the most recent EU risk minimization recommendations in 2018, and conducted a literature review of studies capturing pregnancy exposure data to VPA.</p><p><strong>Methods: </strong>EV public database of reports was searched for congenital ADRs for VPA, between Jan 2013 and Jun 2023. Descriptive analysis was performed to evaluate demographic data, year trends, outcomes, indications, and concomitant folic acid. A structured review of the literature based on explicit key words and eligibility criteria was conducted in PubMed and EU PAS Register (now HMA-EMA Catalogue for RWD sources) to capture pregnancy exposure to VPA. Data on study design, objectives, and results, particularly pregnancy exposure, were extracted.</p><p><strong>Results: </strong>A total of 2021 reports containing 3210 VPA-induced congenital disorders were identified (2068 congenital malformations, 634 neural tube defects, 614 cardiac malformations). Most ages reported were between 0 and 1 months (n, 387) and for the male sex (n, 833). Generally, the outcome was \"not recovered/not resolved,\" with 2.8% fatal ADRs reported. The number of reports slightly decreased through the years (lowest in 2015 [n, 154]; highest in 2013 [n, 669]). The literature review included 24 eligible studies, depicting varying levels of pregnancy exposure depending on the study aims and population, with an overall vast heterogeneity in presenting the study results.</p><p><strong>Conclusions: </strong>The number of VPA-induced congenital disorder reports slightly decreased. Despite absolute contraindications, pregnancy exposure to VPA continues to occur. The present analysis signals that VPA use during pregnancy is still an actual issue despite constant risk mitigation measures.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 6","pages":"e70134"},"PeriodicalIF":2.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144174479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of the Duration of Antihypertensive Prescriptions: Validation of a Data-Driven Approach Using Rotterdam Study Data.","authors":"Chau L B Ho, David Youens, Walter P Abhayaratna, Max K Bulsara, Jeff Hughes, Rachael Moorin, Sallie-Anne Pearson, David B Preen, Christopher M Reid, Rikje Ruiter, Christobel M Saunders, Bruno H Stricker, John Stubbs, Frank J A van Rooij, Cameron Wright, Ninh Thi Ha","doi":"10.1002/pds.70164","DOIUrl":"10.1002/pds.70164","url":null,"abstract":"<p><strong>Objectives: </strong>Administrative medicine dispensing data often omit prescribed duration, which is important for research on adherence or other pharmacoepidemiological topics. While the reverse waiting time distribution (rWTD) method has been widely used to estimate prescribed durations, its accuracy in real-world dispensing data is unknown. We assessed the performance of the rWTD method against the actual prescribed duration recorded in the Rotterdam Study.</p><p><strong>Methods: </strong>100 725 antihypertensive (AHT) prescriptions from 2018 to 2019 were extracted from the Rotterdam Study's medicine data. Data were constructed into five scenarios with increasing variability in the number of medicines included and variations in prescribed duration. The rWTD with 10 random index dates with or without adjustment for the quantity of dispensed medicine was conducted in all scenarios. Relative differences and limit of agreement ratio based on Bland-Altman analysis were used to examine agreement between estimated and actual prescribed durations.</p><p><strong>Results: </strong>rWTD models without adjustment for the quantity of dispensed medicine performed well only in the most homogenous scenario. In scenarios with greater data variability, performance improved significantly when adjusted for the quantity of dispensed medicine. Relative difference decreased from ≥ 65% in models without covariates to ≤ 20% with covariates, and the limit of agreement ratio decreased from ≥ 36.8 in models without covariates to ≤ 5.3 with covariates. Stratification analysis by subclass of the AHT medicines provided similar results.</p><p><strong>Conclusions: </strong>The study demonstrated that as data variability increased, the accuracy of the rWTD estimations decreased. However, the rWTD can produce good estimates (relative difference from 0% to 28%) of prescribed duration for AHT medicines, with the highest accuracy in the model adjusting for the quantity of dispensed medicine or stratification of the data with a relative difference less than 20% and the limit of agreement ratio less than 5.3 for the estimation at the 90th percentile of inter-arrival density. Since this validation was limited to antihypertensive medicines, generalizing the finding to other chronic-use medicines should be undertaken with caution.</p>","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 6","pages":"e70164"},"PeriodicalIF":2.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to \"Hospital Discharge Prescription of Drugs That May Raise Blood Pressure in Patients With Hypertension\".","authors":"","doi":"10.1002/pds.70171","DOIUrl":"https://doi.org/10.1002/pds.70171","url":null,"abstract":"","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"34 6","pages":"e70171"},"PeriodicalIF":2.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144326504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}