The Population-level Effect of Adjuvant Therapies on Breast Cancer Recurrence: Application of the Trend-in-Trend Design.

PURPOSE: Breast cancer has an average 10-year relative survival reaching 84%. This favorable survival is due, in part, to the introduction of biomarker-guided therapies. We estimated the population-level effect of the introduction of two adjuvant therapies-tamoxifen and trastuzumab-on recurrence using the trend-in-trend pharmacoepidemiologic study design. METHODS: We ascertained data on women diagnosed with nonmetastatic breast cancer who were registered in the Danish Breast Cancer Group clinical database. We used the trend-in-trend design to estimate the population-level effect of the introduction of (1) tamoxifen for postmenopausal women with estrogen receptor (ER)-positive breast cancer in 1982, (2) tamoxifen for premenopausal women diagnosed with ER-positive breast cancer in 1999, and (3) trastuzumab for women <60 years diagnosed with human epidermal growth factor receptor 2-positive breast cancer in 2007. RESULTS: For the population-level effect of the introduction of tamoxifen among premenopausal women diagnosed with ER-positive breast cancer in 1999, the risk of recurrence decreased by nearly one-half (OR = 0.52), consistent with evidence from clinical trials; however, the estimate was imprecise (95% confidence interval [CI] = 0.25, 1.85). We observed an imprecise association between tamoxifen use and recurrence from the time it was introduced in 1982 (OR = 1.24 95% CI = 0.46, 5.11), inconsistent with prior knowledge from clinical trials. For the introduction of trastuzumab in 2007, the estimate was also consistent with trial evidence, though imprecise (OR = 0.51; 95% CI = 0.21, 22.4). CONCLUSIONS: We demonstrated how novel pharmacoepidemiologic analytic designs can be used to evaluate the routine clinical care and effectiveness of therapeutic advancements in a population-based setting while considering some limitations of the approach.

Rhekiss-The German Register for Child Wish and Pregnancies in Inflammatory Rheumatic Diseases.

In pharmacoepidemiology, robust data are needed to judge the impact of drug treatment on pregnancy, pregnancy outcomes and breast-fed infants. As pregnant and breastfeeding women are usually excluded from randomised clinical trials, observational studies are required. One of those data sources are pregnancy registers specifically developed to focus on certain diseases or disease groups. The German Rhekiss register investigates pregnancies in women with chronic inflammatory rheumatic diseases (IRD). Rhekiss is a nationwide, multicentre, longitudinal study, in which women aged 18 years or older with an underlying IRD can be enrolled by a rheumatologist either when planning a pregnancy or in the first half of pregnancy. Data are collected prospectively at regular follow-up visits. Rheumatologists and patients provide information in a web-based system before conception (if enrolment was at the time of pregnancy planning), during and after pregnancy. A smartphone app is available for patients. Maternal and clinical information, general laboratory markers, treatment with antirheumatic and other drugs, adverse events, items related to course and outcome of pregnancy and the health of the child are uniformly assessed for all diseases. Individual information on the IRD includes classification criteria, diagnosis-specific laboratory parameters, clinical parameters and validated instruments to measure disease activity or damage. Furthermore, patient-reported outcome measures are captured. A total of 2013 individual patients have been enrolled in the register, and data on 1801 completed pregnancies are available. In summary, Rhekiss is a comprehensive and complex register that can answer various research questions about pregnancy in women with chronic IRDs.

Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Development and Pilot of a Metadata List and Catalogue in Europe.

PURPOSE: Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency-funded project (EUPAS39322), defined a set of metadata to describe real-world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct. METHODS: A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe. RESULTS: A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype. CONCLUSIONS: A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe.

The Medicines Intelligence Data Platform: A Population-Based Data Resource From New South Wales, Australia.

BACKGROUND: The Medicines Intelligence (MedIntel) Data Platform is an anonymised linked data resource designed to generate real-world evidence on prescribed medicine use, effectiveness, safety, costs and cost-effectiveness in Australia. RESULTS: The platform comprises Medicare-eligible people who are ≥18 years and residing in New South Wales (NSW), Australia, any time during 2005-2020, with linked administrative data on dispensed prescription medicines (Pharmaceutical Benefits Scheme), health service use (Medicare Benefits Schedule), emergency department visits (NSW Emergency Department Data Collection), hospitalisations (NSW Admitted Patient Data Collection) plus death (National Death Index) and cancer registrations (NSW Cancer Registry). Data are currently available to 2022, with approval to update the cohort and data collections annually. The platform includes 7.4 million unique people across all years, covering 36.9% of the Australian adult population; the overall population increased from 4.8 M in 2005 to 6.0 M in 2020. As of 1 January 2019 (the last pre-pandemic year), the cohort had a mean age of 48.7 years (51.1% female), with most people (4.4 M, 74.7%) residing in a major city. In 2019, 4.4 M people (73.3%) were dispensed a medicine, 1.2 M (20.5%) were hospitalised, 5.3 M (89.4%) had a GP or specialist appointment, and 54 003 people died. Anti-infectives were the most prevalent medicines dispensed to the cohort in 2019 (43.1%), followed by nervous system (32.2%) and cardiovascular system medicines (30.2%). CONCLUSION: The MedIntel Data Platform creates opportunities for national and international research collaborations and enables us to address contemporary clinically- and policy-relevant research questions about quality use of medicines and health outcomes in Australia and globally.

Assessing the Benefits and Harms of Pharmacotherapy in Older Adults with Frailty: Insights from Pharmacoepidemiologic Studies of Routine Health Care Data.

The objective of this review is to summarize and appraise the research methodology, emerging findings, and future directions in pharmacoepidemiologic studies assessing the benefits and harms of pharmacotherapies in older adults with different levels of frailty. Older adults living with frailty are at elevated risk for poor health outcomes and adverse effects from pharmacotherapy. However, current evidence is limited due to the under-enrollment of frail older adults and the lack of validated frailty assessments in clinical trials. Recent advancements in measuring frailty in administrative claims and electronic health records (database-derived frailty scores) have enabled researchers to identify patients with frailty and to evaluate the heterogeneity of treatment effects by patients' frailty levels using routine health care data. When selecting a database-derived frailty score, researchers must consider the type of data (e.g., different coding systems), the length of the predictor assessment period, the extent of validation against clinically validated frailty measures, and the possibility of surveillance bias arising from unequal access to care. We reviewed 13 pharmacoepidemiologic studies published on PubMed from 2013 to 2023 that evaluated the benefits and harms of cardiovascular medications, diabetes medications, anti-neoplastic agents, antipsychotic medications, and vaccines by frailty levels. These studies suggest that, while greater frailty is positively associated with adverse treatment outcomes, older adults with frailty can still benefit from pharmacotherapy. Therefore, we recommend routine frailty subgroup analyses in pharmacoepidemiologic studies. Despite data and design limitations, the findings from such studies may be informative to tailor pharmacotherapy for older adults across the frailty spectrum.

A pharmacoepidemiological nested case-control study of risk factors for venous thromboembolism with the focus on diabetes, cancer, socioeconomic group, medications, and comorbidities.

OBJECTIVES: A pharmacoepidemiological study to assess VTE risk factors in a diabetes-rich population. METHODS: The study comprised 299,590 individuals. We observed 3450 VTEs and matched them with 15,875 controls using a nested case-control approach and collected data on comorbidities and prescriptions. By multivariable conditional logistic regression, we calculated ORs with 95%CIs for comorbidities and medications to evaluate their associations with VTE. RESULTS: Diabetes (aOR 2.16; 95%CI 1.99-2.34), inflammatory bowel disease (1.84; 1.27-2.66), and severe psychiatric disorders (1.72; 1.43-2.05) had the strongest associations among the non-cancer comorbidities. Pancreatic (12.32; 7.11-21.36), stomach (8.57; 4.07-18.03), lung and bronchus (6.26; 4.16-9.43), and ovarian (6.72; 2.95-15.10) cancers were ranked as high-risk for VTE. Corticosteroids, gabapentinoids, psychotropic drugs, risedronic acid, and pramipexole were most strongly associated (aOR exceeding 1.5) with VTE. Insulin (3.86; 3.33-4.47) and sulphonylureas (2.62; 2.18-3.16) had stronger associations than metformin (1.65; 1.49-1.83). Statins and lercanidipine (0.78; 0.62-0.98) were associated with a lowered risk of VTE. CONCLUSIONS: In this cohort, with 50% diabetes prevalence, pancreatic, stomach, lung and bronchus, and ovarian cancers were strongly associated with VTE. Corticosteroids, gabapentinoids, and psychotropic medications had the strongest associations with VTE among medications. This may be valuable for generating hypotheses for the further research. Lercanidipine may be a novel protective medication against VTE.

Describing diversity of real world data sources in pharmacoepidemiologic studies: The DIVERSE scoping review.

PURPOSE: Real-world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real-world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence. METHODS: In a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes. RESULTS: Across the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty-six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization. CONCLUSIONS: The dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE.

Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model.

PURPOSE: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI). MATERIALS AND METHODS: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics® Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. RESULTS: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to >85% of drug records in all but one of the assessed databases. CONCLUSION: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.

Application of a data continuity prediction algorithm to an electronic health record-based pharmacoepidemiology study.

BACKGROUND AND OBJECTIVES: Use of algorithms to identify patients with high data-continuity in electronic health records (EHRs) may increase study validity. Practical experience with this approach remains limited. METHODS: We developed and validated four algorithms to identify patients with high data continuity in an EHR-based data source. Selected algorithms were then applied to a pharmacoepidemiologic study comparing rates of COVID-19 hospitalization in patients exposed to insulin versus noninsulin antidiabetic drugs. RESULTS: A model using a short list of five EHR-derived variables performed as well as more complex models to distinguish high- from low-data continuity patients. Higher data continuity was associated with more accurate ascertainment of key variables. In the pharmacoepidemiologic study, patients with higher data continuity had higher observed rates of the COVID-19 outcome and a large unadjusted association between insulin use and the outcome, but no association after propensity score adjustment. DISCUSSION: We found that a simple, portable algorithm to predict data continuity gave comparable performance to more complex methods. Use of the algorithm significantly impacted the results of an empirical study, with evidence of more valid results at higher levels of data continuity.

Transparency, reproducibility, and replicability of pharmacoepidemiology studies in a distributed network environment.

PURPOSE: Our objective is to describe how the U.S. Food and Drug Administration (FDA)'s Sentinel System implements best practices to ensure trust in drug safety studies using real-world data from disparate sources. METHODS: We present a stepwise schematic for Sentinel's data harmonization, data quality check, query design and implementation, and reporting practices, and describe approaches to enhancing the transparency, reproducibility, and replicability of studies at each step. CONCLUSIONS: Each Sentinel data partner converts its source data into the Sentinel Common Data Model. The transformed data undergoes rigorous quality checks before it can be used for Sentinel queries. The Sentinel Common Data Model framework, data transformation codes for several data sources, and data quality assurance packages are publicly available. Designed to run against the Sentinel Common Data Model, Sentinel's querying system comprises a suite of pre-tested, parametrizable computer programs that allow users to perform sophisticated descriptive and inferential analysis without having to exchange individual-level data across sites. Detailed documentation of capabilities of the programs as well as the codes and information required to execute them are publicly available on the Sentinel website. Sentinel also provides public trainings and online resources to facilitate use of its data model and querying system. Its study specifications conform to established reporting frameworks aimed at facilitating reproducibility and replicability of real-world data studies. Reports from Sentinel queries and associated design and analytic specifications are available for download on the Sentinel website. Sentinel is an example of how real-world data can be used to generate regulatory-grade evidence at scale using a transparent, reproducible, and replicable process.

Lessons learned from a multi-data source research collaboration: The mirabegron post-authorization safety study program.

BACKGROUND: Many factors contribute to developing and conducting a successful multi-data source, non-interventional, post-authorization safety study (NI-PASS) for submission to multiple health authorities. Such studies are often large undertakings; evaluating and sharing lessons learned can provide useful insights to others considering similar studies. OBJECTIVES: We discuss challenges and key methodological and organizational factors that led to the delivery of a successful post-marketing requirement (PMR)/PASS program investigating the risk of cardiovascular and cancer events among users of mirabegron, an oral medication for the treatment of overactive bladder. RESULTS: We provide context and share learnings, including sections on research program collaboration, scientific transparency, organizational approach, mitigation of uncertainty around potential delays, validity of study outcomes, selection of data sources and optimizing patient numbers, choice of comparator groups and enhancing precision of estimates of associations, potential confounding and generalizability of study findings, and interpretation of results. CONCLUSIONS: This large PMR/PASS program was a long-term commitment from all parties and benefited from an effective coordinating center and extensive scientific interactions across research partners, scientific advisory board, study sponsor, and health authorities, and delivered useful learnings related to the design and organization of multi-data source NI-PASS.

Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.

INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medicines involved. The label of a medicine is an approved guide that indicates how to prescribe the drug in a safe and effective manner. AREAS COVERED: The label for a medicine may evolve as new information on drug safety and effectiveness emerges, leading to the addition or removal of warnings, drug-drug interactions, or to permit new indications. However, the speed at which these updates are made to these AI/ML recommendation systems may be delayed and could influence the safety of prescribing decisions. This article explores the need to keep AI/ML tools 'in sync' with any label changes. Additionally, challenges relating to medicine availability and geographical suitability are discussed. EXPERT OPINION: These considerations highlight the important role that pharmacoepidemiologists and drug safety professionals must play within the monitoring and use of these tools. Furthermore, these issues highlight the guiding role that regulators need to have in planning and oversight of these tools.

Artificial intelligence or machine learning (AI/ML) based systems that guide the prescription of medications have the potential to vastly improve patient care, but these tools should only provide recommendations that are in line with the label of a medicine. With a constantly evolving medication label, this is likely to be a challenge, and this also has implications for the off-label use of medicines.

Updated core competencies in pharmacoepidemiology to inform contemporary curricula and training for academia, government, and industry.

PURPOSE: The first paper to specify the core content of pharmacoepidemiology as a profession was published by an ISPE (International Society for Pharmacoepidemiology) workgroup in 2012 (Jones JK et al. PDS 2012; 21[7]:677-689). Due to the broader and evolving scope of pharmacoepidemiology, ISPE considers it important to proactively identify, update and expand the list of core competencies to inform curricula of education programs; thus, better positioning pharmacoepidemiologists across academic, government (including regulatory), and industry positions. The aim of this project was to update the list of core competencies in pharmacoepidemiology. METHODS: To ensure applicability of findings to multiple areas, a working group was established consisting of ISPE members with positions in academia, industry, government, and other settings. All competencies outlined by Jones et al. were extracted from the initial manuscript and presented to the working group for review. Expert-based judgments were collated and used to identify consensus. It was noted that some competencies could contribute to multiple groups and could be directly or indirectly related to a group. RESULTS: Five core domains were proposed: (1) Epidemiology, (2) Clinical Pharmacology, (3) Regulatory Science, (4) Statistics and data science, and (5) Communication and other professional skills. In total, 55 individual competencies were proposed, of which 25 were new competencies. No competencies from the original work were dropped but aggregation or amendments were made where considered necessary. CONCLUSIONS: While many core competencies in pharmacoepidemiology have remained the same over the past 10 years, there have also been several updates to reflect new and emerging concepts in the field.

Quasi-experimental methods for pharmacoepidemiology: difference-in-differences and synthetic control methods with case studies for vaccine evaluation.

Difference-in-differences and synthetic control methods have become common study designs for evaluating the effects of changes in policies, including health policies. They also have potential for providing real-world effectiveness and safety evidence in pharmacoepidemiology. To effectively add to the toolkit of the field, however, designs-including both their benefits and drawbacks-must be well understood. Quasi-experimental designs provide an opportunity to estimate the average treatment effect on the treated without requiring the measurement of all possible confounding factors, and to assess population-level effects. This requires, however, other key assumptions, including the parallel trends or stable weighting assumptions, a lack of other concurrent events that could alter time trends, and an absence of contamination between exposed and unexposed units. The targeted estimands are also highly specific to the settings of the study, and combining across units or time periods can be challenging. Case studies are presented for 3 vaccine evaluation studies, showcasing some of these challenges and opportunities in a specific field of pharmacoepidemiology. These methods provide feasible and valuable sources of evidence in various pharmacoepidemiologic settings and can be improved through research to identify and weigh the advantages and disadvantages in those settings. This article is part of a Special Collection on Pharmacoepidemiology.