[Identification process of time-related bias in pharmacoepidemiologic research based on a scoping review].

Objective: To summarize the characteristics of pharmacoepidemiologic research involving diabetes patients, which were published in recent years, in terms of study design and analysis, and develop an identification process for time-related biases in pharmacoepidemiologic research. Methods: PubMed, Embase, CNKI and Wanfang were used for a systematical literature retrieval of relevant study papers published between January 1,2012 and September 26, 2022. Literature screening and data extraction were performed independently by two reviewers. Based on the mechanisms of different time-related biases and the characteristics of included study papers in terms of study design and analysis methods, an identification process for all types of time-related biases was developed. Results: A total of 281 study papers were included, of which 58 (20.64%) specifically mentioned certain time-related biases considered in the study. Based on the scoping review results, key points to identify time-related biases were summarized, involving data source, study design, control selection, comparator drugs, matching the duration of diabetes, identification of the washout period, identification of the induction/latency period, identification of the initiation of follow-up, identification of time window, statistical analysis methods, sensitivity analysis, and other design and analytical elements, in the identification process for time-related biases in pharmacoepidemiologic research. Conclusions: Time-related biases are common in pharmacoepidemiologic research and might significantly impact the study results. Based on scoping review results, this study further developed an identification process for time-related biases in pharmacoepidemiologic research, which will help researchers identify and avoid time-related biases and improve the reliability of related evidence in pharmacoepidemiologic research.

A Two-Step Framework for Validating Causal Effect Estimates.

BACKGROUND: Comparing causal effect estimates obtained using observational data to those obtained from the gold standard (i.e., randomized controlled trials [RCTs]) helps assess the validity of these estimates. However, comparisons are challenging due to differences between observational data and RCT generated data. The unknown treatment assignment mechanism in the observational data and varying sampling mechanisms between the RCT and the observational data can lead to confounding and sampling bias, respectively. AIMS: The objective of this study is to propose a two-step framework to validate causal effect estimates obtained from observational data by adjusting for both mechanisms. MATERIALS AND METHODS: An estimator of causal effects related to the two mechanisms is constructed. A two-step framework for comparing causal effect estimates is derived from the estimator. An R package RCTrep is developed to implement the framework in practice. RESULTS: A simulation study is conducted to show that using our framework observational data can produce causal effect estimates similar to those of an RCT. A real-world application of the framework to validate treatment effects of adjuvant chemotherapy obtained from registry data is demonstrated. CONCLUSION: This  study constructs a framework for comparing causal effect estimates between observational data and RCT data, facilitating the assessment of the validity of causal effect estimates obtained from observational data.

Sharing Is Caring? International Society for Pharmacoepidemiology Review and Recommendations for Sharing Programming Code.

PURPOSE: There is increasing recognition of the importance of transparency and reproducibility in scientific research. This study aimed to quantify the extent to which programming code is publicly shared in pharmacoepidemiology, and to develop a set of recommendations on this topic. METHODS: We conducted a literature review identifying all studies published in Pharmacoepidemiology and Drug Safety (PDS) between 2017 and 2022. Data were extracted on the frequency and types of programming code shared, and other key open science practices (clinical codelist sharing, data sharing, study preregistration, and stated use of reporting guidelines and preprinting). We developed six recommendations for investigators who choose to share code and gathered feedback from members of the International Society for Pharmacoepidemiology (ISPE). RESULTS: Programming code sharing by articles published in PDS ranged from 1.8% in 2017 to 9.5% in 2022. It was more prevalent among articles with a methodological focus, simulation studies, and papers which also shared record-level data. CONCLUSION: Programming code sharing is rare but increasing in pharmacoepidemiology studies published in PDS. We recommend improved reporting of whether code is shared and how available code can be accessed. When sharing programming code, we recommend the use of permanent digital identifiers, appropriate licenses, and, where possible, adherence to good software practices around the provision of metadata and documentation, computational reproducibility, and data privacy.

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.

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.

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.

Contemporary Practice and Considerations for Real-World Data Source Identification and Feasibility Assessment.

PURPOSE: There has been rapid growth in the variety and number of real-world data (RWD) sources, as well as the number of regulatory documents that provide guidance for assessing the suitability of RWD sources for pharmacoepidemiology studies. This study aims to assess differences in RWD guidance and variability in current practice for identifying and assessing RWD for studies with regulatory purpose. METHODS: Key criteria for feasibility assessment were mapped against relevant regulatory guidance documents across US, EU, and Asia-Pacific regions. An online survey was designed and deployed to International Society for Pharmacoepidemiology members to understand current practice. Findings were summarized and used to inform key considerations and recommendations. RESULTS: Eleven RWD guidance documents were identified and mapped against 14 RWD assessment criteria. Variability was seen across these documents in guidance for these criteria. Between December 2022 and January 2023, 37 survey respondents reported having used RWD for post-marketing commitments (34, 92%) and/or background epidemiology (28, 76%). RWD were mostly identified through literature (33, 89%) and data landscaping (26, 70%); guidance documents referenced included: Food and Drug Administration (20, 54%), European Network for Centres for Pharmacoepidemiology and Pharmacovigilance (17, 46%), European Medical Agency (16, 43%), and Structured Process to Identify Fit-For-Purpose Data (11, 30%). Challenges for conducting feasibility assessments included RWD accessibility, ability to complete validation, and RWD provider responsiveness. CONCLUSIONS: Existing guidelines are used extensively by researchers, but key criteria for RWD identification and feasibility assessment are not reflected consistently and challenges remain. Recommendations have been made reflecting study findings.

Pharmacoepidemiology evaluation of bumetanide as a potential candidate for drug repurposing for Alzheimer's disease.

INTRODUCTION: Bumetanide, a loop diuretic, was identified as a candidate drug for repurposing for Alzheimer's disease (AD) based on its effects on transcriptomic apolipoprotein E signatures. Cross-sectional analyses of electronic health records suggest that bumetanide is associated with decreased prevalence of AD; however, temporality between bumetanide exposure and AD development has not been established. METHODS: We evaluated Medicare claims data using Cox proportional hazards regression to evaluate the association between time-dependent use of bumetanide and time to first AD diagnosis while controlling for patient characteristics. Multiple sensitivity analyses were conducted to test the robustness of the findings. RESULTS: We sampled 833,561 Medicare beneficiaries, 60.8% female, with mean (standard deviation) age of 70.4 (12). Bumetanide use was not significantly associated with AD risk (hazard ratio 1.05; 95% confidence interval, 0.99-1.10). DISCUSSION: Using a nationwide dataset and a retrospective cohort study design, we were not able to identify a time-dependent effect of bumetanide lowering AD risk. HIGHLIGHTS: Bumetanide was identified as a candidate for repurposing for Alzheimer's disease (AD). We evaluated the association between bumetanide use and risk of AD. We used Medicare data and accounted for duration of bumetanide use. Bumetanide use was not significantly associated with risk of AD.

Traditional Methods Hold Their Ground Against Machine Learning in Predicting Potentially Inappropriate Medication Use in Older Adults.

OBJECTIVES: Machine learning methods have gained much attention in health sciences for predicting various health outcomes but are scarcely used in pharmacoepidemiology. The ability to identify predictors of suboptimal medication use is essential for conducting interventions aimed at improving medication outcomes. It remains uncertain whether machine learning methods could enhance the identification of potentially inappropriate medication use among older adults compared with traditional methods. This study aimed to (1) to compare the performances of machine learning models in predicting use of potentially inappropriate medications and (2) to quantify and compare the relative importance of predictors in a population of community-dwelling older adults (>65 years) in the province of Québec, Canada. METHODS: We used the Québec Integrated Chronic Disease Surveillance System and selected a cohort of 1 105 295 older adults of whom 533 719 were potentially inappropriate medication users. Potentially inappropriate medications were defined according to the Beers list. We compared performances between 5 popular machine learning models (gradient boosting machines, logistic regression, naive Bayes, neural networks, and random forests) based on receiver operating characteristic curves and other performance criteria, using a set of sociodemographic and medical predictors. RESULTS: No model clearly outperformed the others. All models except neural networks were in agreement regarding the top predictors (sex and anxiety-depressive disorders and schizophrenia) and the bottom predictors (rurality and social and material deprivation indices). CONCLUSIONS: Including other types of predictors (eg, unstructured data) may be more useful for increasing performance in prediction of potentially inappropriate medication use.

Phosphodiesterase-5 Inhibitors and Dementia Risk: Confounding by Indication in Real-World Studies.

Pharmacoepidemiologic studies using routinely collected data allow researchers to propose drugs for repurposing trials for dementia prevention or treatment. A recent cohort study reported a 54% lower dementia risk among users of sildenafil compared to users of certain cardiovascular medications. We caution that "confounding by indication" can arise when outcomes are compared between a drug of interest and an inappropriate comparator. Here, we emphasize important considerations in selecting an active comparator. We assess the implications of substantial risk of confounding by indication in pharmacoepidemiologic studies linking phosphodiesterase-5 inhibitors to lower dementia risk.

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.

[Validity assessment of self-reported medication use in a pharmacoepidemiologic study by comparison with prescription record review].

Objectives　Although self-reported questionnaires are widely used to collect information on medication use in epidemiological studies, their validity for studies involving older adults has not been sufficiently assessed. This study evaluated the validity of self-reported medication use using questionnaires in comparison with drug notebooks.Methods　The study enrolled 370 older community dwellers who participated in an aging sub-study survey of the Tsuruoka Metabolomics Cohort Study between April 2019 and March 2021. Medication use was assessed by comparing self-reported questionnaire data with drug notebook records. We analyzed medications used for hypertension, dyslipidemia, myocardial infarction, angina, diabetes, rheumatism, osteoporosis/metabolic bone disease, constipation, anxiety/depression, dementia, asthma, allergy, thrombosis, and thyroid disease. Moreover, gastrointestinal (GI) medications, steroids, and antipyretic analgesics were assessed, and data on injectable medications for osteoporosis/metabolic bone disease was collected. Using drug notebook records, we identified regular medication users by assessing whether they had received oral medication prescriptions covering over 28 days and took the medication within the 90 days preceding the day of their survey. To define medication categories, we used Anatomical Therapeutic Chemical (ATC) classification codes. Sensitivity, specificity, and kappa statistics were calculated for each medication using drug notebooks as standards. Those who did not bring their drug notebooks on the day of the survey were defined as non-medication users.Results　The mean age (standard deviation) of the 370 participants (146 men and 224 women) was 73.3 (4.0) years. The sensitivity and specificity for each medication were as follows: hypertension (0.97, 0.97), dyslipidemia (0.93, 0.98), myocardial infarction (0.24, 0.99), diabetes (0.94, 1.00), rheumatism (1.00, 1.00), osteoporosis/metabolic bone disease (0.82, 0.99), constipation (0.71, 0.98), GI conditions (0.63, 0.97), anxiety/depression (0.36, 1.00), dementia (0.67, 1.00), asthma (0.67, 0.98), allergy (0.57, 0.99), thrombosis (0.88, 0.98), steroids (0.80, 0.99), thyroid disease (1.00, 1.00) and antipyretic analgesics (0.75, 0.96).Conclusions　Although sensitivity and specificity differed by medication categories, the results of our population-based cohort study suggested that self-reported questionnaires on medication use among older adults are valid, especially for medications with high sensitivity (≥ 0.8).