Building transparency and reproducibility into the practice of pharmacoepidemiology and outcomes research.

Real-world evidence (RWE) studies are increasingly used to inform policy and clinical decisions. However, there remain concerns about the credibility and reproducibility of RWE studies. Observational researchers should highlight the level of transparency of their studies by providing a succinct statement addressing study transparency with the publication of every paper, poster, or presentation that reports on a RWE study. In this paper, we propose a framework for an explicit transparency statement that declares the level of transparency a given RWE study has achieved across five key domains: 1) protocol, 2) pre-registration, 3) data, 4) code sharing, and 5) reporting checklists.

Tree-based scan statistics to generate drug repurposing hypotheses: a test case using sodium-glucose cotransporter-2 inhibitors.

Most drug repurposing studies using real-world data focused on validating, instead of generating, hypotheses. We used tree-based scan statistics to generate repurposing hypotheses for sodium-glucose cotransporter-2 inhibitors (SGLT2i). We used an active-comparator, new-user design to create a 1:1 propensity-score matched cohort of SGLT2i and dipeptidyl peptidase-4 inhibitors (DPP4i) initiators in the MerativeTM MarketScan® Research Databases. Tree-based scan statistics were estimated across an ICD-10-CM-based hierarchical outcome tree using incident outcomes identified from hospital and outpatient diagnoses. We used an adjusted P≤0.01 as the threshold for statistical alert to prioritize associations for evaluation as repurposing signals. We varied the analyses by tree size, scanning level, and clinical settings for outcomes. There were 80,510 matched SGLT2i-DPP4i initiator pairs with 215,333 outcomes among SGLT2i initiators and 223,428 outcomes among DPP4i initiators. There were 18 prioritized associations, which included chronic kidney disease (P=0.0001), an expected signal, and anemia (P=0.0001). Heart failure (P=0.0167), another expected signal, was identified slightly beyond the statistical alert threshold. Narrowing the outcome tree, scanning at different tree levels, and including outcomes from different clinical settings influenced the scan statistics. We identified signals aligning with recently approved indications of SGLT2i, plus potential repurposing signals supported by existing evidence but requiring future validation.

Hierarchical Clustering Analysis to Inform Classification of Congenital Malformations for Surveillance of Medication Safety in Pregnancy.

There is growing interest in the secondary use of healthcare data to evaluate medication safety in pregnancy. Tree-based scan statistics (TBSS) offer an innovative approach to help identify potential safety signals. TBSS utilize hierarchically organized outcomes, generally based on existing clinical coding systems that group outcomes by organ system. When assessing teratogenicity, such groupings often lack a sound embryologic basis given the etiologic heterogeneity of congenital malformations. The study objective was to enhance the grouping of congenital malformations to be used in scanning approaches through implementation of hierarchical clustering analysis (HCA) and to pilot test an HCA-enhanced TBSS approach for medication safety surveillance in pregnancy in two test cases using >4.2 million mother-child dyads from two US-nationwide databases. HCA identified (1) malformation combinations belonging to the same organ system already grouped in existing classifications, (2) known combinations across different organ systems not previously grouped, (3) unknown combinations not previously grouped, and (4) malformations seemingly standing on their own. Testing the approach with valproate and topiramate identified expected signals, and a signal for an HCA-cluster missed by traditional classification. Augmenting existing classifications with clusters identified through large data exploration may be promising when defining phenotypes for surveillance and causal inference studies.

Targeted Learning with an Undersmoothed Lasso Propensity Score Model for Large-Scale Covariate Adjustment in Healthcare Database Studies.

Lasso regression is widely used for large-scale propensity score (PS) estimation in healthcare database studies. In these settings, previous work has shown that undersmoothing (overfitting) Lasso PS models can improve confounding control, but it can also cause problems of non-overlap in covariate distributions. It remains unclear how to select the degree of undersmoothing when fitting large-scale Lasso PS models to improve confounding control while avoiding issues that can result from reduced covariate overlap. Here, we used simulations to evaluate the performance of using collaborative-controlled targeted learning to data-adaptively select the degree of undersmoothing when fitting large-scale PS models within both singly and doubly robust frameworks to reduce bias in causal estimators. Simulations showed that collaborative learning can data-adaptively select the degree of undersmoothing to reduce bias in estimated treatment effects. Results further showed that when fitting undersmoothed Lasso PS-models, the use of cross-fitting was important for avoiding non-overlap in covariate distributions and reducing bias in causal estimates.

An open-source implementation of tree-based scan statistics.

PURPOSE: We develop an open-source R package to implement tree-based scan statistics (TBSS) analyses. METHODS: TBSS are data mining methods used by the United States Food and Drug Administration and the Centers for Disease Control. They simultaneously screen thousands of hierarchically aggregated outcomes to identify unsuspected adverse effects of drugs or vaccines, accounting for multiple comparisons. The general structure of TBSS is highly adaptable, with four essential components: (1) a hierarchical outcome structure, (2) a test statistic to be computed for each element of the hierarchy, (3) an algorithm to generate data replicates under a null distribution, and (4) observed outcomes at the lower level of the hierarchy. We encode the general TBSS framework in a convenient R package that offers user-friendly functions for the most used TBSS methods. To illustrate the performance of our software, we evaluated two examples of archetypical TBSS analyses previously analyzed using proprietary, closed-source TreeScan™ software. The first considers the risk of congenital malformations associated with first-trimester exposure to valproate, and the second compares exposure to newly prescribed canagliflozin with a dipeptidyl peptidase 4 inhibitor in adults affected by type 2 diabetes. RESULTS: The results of the original studies are replicated. CONCLUSIONS: The diffusion of an open-source implementation of TBSS can enhance innovation of TBSS methods and foster collaborations. We offer an intuitive R package implementing standard TBSS methods with accompanying tutorials. Our unified object-oriented implementation allows expert users to extend the framework, introduce new features, or enhance existing ones.

The FAIRification of research in real-world evidence: A practical introduction to reproducible analytic workflows using Git and R.

Transparency and reproducibility are major prerequisites for conducting meaningful real-world evidence (RWE) studies that are fit for decision-making. Many advances have been made in the documentation and reporting of study protocols and results, but the principles for version control and sharing of analytic code in RWE are not yet as established as in other quantitative disciplines like computational biology and health informatics. In this practical tutorial, we aim to give an introduction to distributed version control systems (VCS) tailored toward the FAIR (Findable, Accessible, Interoperable, and Reproducible) implementation of RWE studies. To ease adoption, we provide detailed step-by-step instructions with practical examples on how the Git VCS and R programming language can be implemented into RWE study workflows to facilitate reproducible analyzes. We further discuss and showcase how these tools can be used to track changes, collaborate, disseminate, and archive RWE studies through dedicated project repositories that maintain a complete audit trail of all relevant study documents.

Emulation of randomized trials of direct oral anticoagulants with claims data and implications for new Factor XI inhibitors.

Direct oral anticoagulants (DOACs) revolutionized the management of thromboembolic disorders. Clinical care may be further improved as Factor XIs undergo large-scale outcome trials. What role can non-randomized database studies play in expediting understanding of these drugs in clinical practice? The RCT-DUPLICATIVE Initiative emulated the design of eight DOAC randomized clinical trials (RCT) using non-randomized claims database studies. RCT study design parameters and measurements were closely emulated by the database studies and produced highly concordant results. The results of the single database study that did not meet all agreement metrics with the specific RCT it was emulating were aligned with a meta-analysis of six trials studying similar questions, suggesting the trial result was an outlier. Well-designed database studies using fit-for-purpose data came to the same conclusions as DOAC trials, illustrating how database studies could complement RCTs for Factor XI inhibitors-by accelerating insights in underrepresented populations, demonstrating effectiveness and safety in clinical practice, and testing broader indications.

A Dynamic Prognostic Model for Identifying Vulnerable COVID-19 Patients at High Risk of Rapid Deterioration.

PURPOSE: We aimed to validate and, if performance was unsatisfactory, update the previously published prognostic model to predict clinical deterioration in patients hospitalized for COVID-19, using data following vaccine availability. METHODS: Using electronic health records of patients ≥18 years, with laboratory-confirmed COVID-19, from a large care-delivery network in Massachusetts, USA, from March 2020 to November 2021, we tested the performance of the previously developed prediction model and updated the prediction model by incorporating data after availability of COVID-19 vaccines. We randomly divided data into development (70%) and validation (30%) cohorts. We built a model predicting worsening in a published severity scale in 24 h by LASSO regression and evaluated performance by c-statistic and Brier score. RESULTS: Our study cohort consisted of 8185 patients (Development: 5730 patients [mean age: 62; 44% female] and Validation: 2455 patients [mean age: 62; 45% female]). The previously published model had suboptimal performance using data after November 2020 (N = 4973, c-statistic = 0.60. Brier score = 0.11). After retraining with the new data, the updated model included 38 predictors including 18 changing biomarkers. Patients hospitalized after Jun 1st, 2021 (when COVID-19 vaccines became widely available in Massachusetts) were younger and had fewer comorbidities than those hospitalized before. The c-statistic and Brier score were 0.77 and 0.13 in the development cohort, and 0.73 and 0.14 in the validation cohort. CONCLUSION: The characteristics of patients hospitalized for COVID-19 differed substantially over time. We developed a new dynamic model for rapid progression with satisfactory performance in the validation set.

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.

Comparative Effectiveness and Safety of Generic Versus Brand-Name Fluticasone-Salmeterol to Treat Chronic Obstructive Pulmonary Disease.

BACKGROUND: In 2019, the U.S. Food and Drug Administration (FDA) approved the first generic maintenance inhaler for asthma and chronic obstructive pulmonary disease (COPD). The inhaler, Wixela Inhub (fluticasone-salmeterol; Viatris), is a substitutable version of the dry powder inhaler Advair Diskus (fluticasone-salmeterol; GlaxoSmithKline). When approving complex generic products like inhalers, the FDA applies a special "weight-of-evidence" approach. In this case, manufacturers were required to perform a randomized controlled trial in patients with asthma but not COPD, although the product received approval for both indications. OBJECTIVE: To compare the effectiveness and safety of generic (Wixela Inhub) and brand-name (Advair Diskus) fluticasone-salmeterol among patients with COPD treated in routine care. DESIGN: A 1:1 propensity score-matched cohort study. SETTING: A large, longitudinal health care database. PATIENTS: Adults older than 40 years with a diagnosis of COPD. MEASUREMENTS: Incidence of first moderate or severe COPD exacerbation (effectiveness outcome) and incidence of first pneumonia hospitalization (safety outcome) in the 365 days after cohort entry. RESULTS: Among 45 369 patients (27 305 Advair Diskus users and 18 064 Wixela Inhub users), 10 012 matched pairs were identified for the primary analysis. Compared with Advair Diskus use, Wixela Inhub use was associated with a nearly identical incidence of first moderate or severe COPD exacerbation (hazard ratio [HR], 0.97 [95% CI, 0.90 to 1.04]) and first pneumonia hospitalization (HR, 0.99 [CI, 0.86 to 1.15]). LIMITATIONS: Follow-up times were short, reflecting real-world clinical practice. The possibility of residual confounding cannot be completely excluded. CONCLUSION: Use of generic and brand-name fluticasone-salmeterol was associated with similar outcomes among patients with COPD treated in routine practice. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute.

Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.

BACKGROUND: We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19. METHODS: We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including >40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 h. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration. RESULTS: Overall, 3706 patients (2587 in the development and 1119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79-0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78. CONCLUSIONS: Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors.

HARmonized Protocol Template to Enhance Reproducibility of hypothesis evaluating real-world evidence studies on treatment effects: A good practices report of a joint ISPE/ISPOR task force.

PROBLEM: Ambiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias. WHAT WE DID: The International Society for Pharmacoepidemiology (ISPE) and ISPOR-The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The overarching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility. STRATEGIES TO DISSEMINATE AND FACILITATE USE: Recognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years. CONCLUSION: The HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions.

Emulation of Randomized Clinical Trials With Nonrandomized Database Analyses: Results of 32 Clinical Trials.

Importance: Nonrandomized studies using insurance claims databases can be analyzed to produce real-world evidence on the effectiveness of medical products. Given the lack of baseline randomization and measurement issues, concerns exist about whether such studies produce unbiased treatment effect estimates. Objective: To emulate the design of 30 completed and 2 ongoing randomized clinical trials (RCTs) of medications with database studies using observational analogues of the RCT design parameters (population, intervention, comparator, outcome, time [PICOT]) and to quantify agreement in RCT-database study pairs. Design, Setting, and Participants: New-user cohort studies with propensity score matching using 3 US claims databases (Optum Clinformatics, MarketScan, and Medicare). Inclusion-exclusion criteria for each database study were prespecified to emulate the corresponding RCT. RCTs were explicitly selected based on feasibility, including power, key confounders, and end points more likely to be emulated with real-world data. All 32 protocols were registered on ClinicalTrials.gov before conducting analyses. Emulations were conducted from 2017 through 2022. Exposures: Therapies for multiple clinical conditions were included. Main Outcomes and Measures: Database study emulations focused on the primary outcome of the corresponding RCT. Findings of database studies were compared with RCTs using predefined metrics, including Pearson correlation coefficients and binary metrics based on statistical significance agreement, estimate agreement, and standardized difference. Results: In these highly selected RCTs, the overall observed agreement between the RCT and the database emulation results was a Pearson correlation of 0.82 (95% CI, 0.64-0.91), with 75% meeting statistical significance, 66% estimate agreement, and 75% standardized difference agreement. In a post hoc analysis limited to 16 RCTs with closer emulation of trial design and measurements, concordance was higher (Pearson r, 0.93; 95% CI, 0.79-0.97; 94% meeting statistical significance, 88% estimate agreement, 88% standardized difference agreement). Weaker concordance occurred among 16 RCTs for which close emulation of certain design elements that define the research question (PICOT) with data from insurance claims was not possible (Pearson r, 0.53; 95% CI, 0.00-0.83; 56% meeting statistical significance, 50% estimate agreement, 69% standardized difference agreement). Conclusions and Relevance: Real-world evidence studies can reach similar conclusions as RCTs when design and measurements can be closely emulated, but this may be difficult to achieve. Concordance in results varied depending on the agreement metric. Emulation differences, chance, and residual confounding can contribute to divergence in results and are difficult to disentangle.

HARmonized Protocol Template to Enhance Reproducibility of Hypothesis Evaluating Real-World Evidence Studies on Treatment Effects: A Good Practices Report of a Joint ISPE/ISPOR Task Force.

OBJECTIVES: Ambiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias. METHODS: The International Society for Pharmacoepidemiology (ISPE) and ISPOR-The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The over-arching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility. STRATEGIES TO DISSEMINATE AND FACILITATE USE: Recognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years. CONCLUSION: The HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions.

Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting.

Transparency is increasingly promoted to instill trust in nonrandomized studies using real-world data. Graphics and data visualizations support transparency by aiding communication and understanding, and can inform study design and analysis decisions. However, other than graphical representation of a study design and flow diagrams (e.g., a Consolidated Standards of Reporting Trials [CONSORT] like diagram), specific standards on how to maximize validity and transparency with visualization are needed. This paper provides guidance on how to use visualizations throughout the life cycle of a pharmacoepidemiology study-from initial study design to final report-to facilitate rationalized and transparent decision-making about study design and implementation, and clear communication of study findings. Our intent is to help researchers align their practices with current consensus statements on transparency.

Transparency of high-dimensional propensity score analyses: Guidance for diagnostics and reporting.

PURPOSE: The high-dimensional propensity score (HDPS) is a semi-automated procedure for confounder identification, prioritisation and adjustment in large healthcare databases that requires investigators to specify data dimensions, prioritisation strategy and tuning parameters. In practice, reporting of these decisions is inconsistent and this can undermine the transparency, and reproducibility of results obtained. We illustrate reporting tools, graphical displays and sensitivity analyses to increase transparency and facilitate evaluation of the robustness of analyses involving HDPS. METHODS: Using a study from the UK Clinical Practice Research Datalink that implemented HDPS we demonstrate the application of the proposed recommendations. RESULTS: We identify seven considerations surrounding the implementation of HDPS, such as the identification of data dimensions, method for code prioritisation and number of variables selected. Graphical diagnostic tools include assessing the balance of key confounders before and after adjusting for empirically selected HDPS covariates and the identification of potentially influential covariates. Sensitivity analyses include varying the number of covariates selected and assessing the impact of covariates behaving empirically as instrumental variables. In our example, results were robust to both the number of covariates selected and the inclusion of potentially influential covariates. Furthermore, our HDPS models achieved good balance in key confounders. CONCLUSIONS: The data-adaptive approach of HDPS and the resulting benefits have led to its popularity as a method for confounder adjustment in pharmacoepidemiological studies. Reporting of HDPS analyses in practice may be improved by the considerations and tools proposed here to increase the transparency and reproducibility of study results.

Active Surveillance of the Safety of Medications Used During Pregnancy.

The scientific community relies on postmarketing approaches to define the risk of using medications in pregnancy because information available at the time of drug approval is limited. Most studies carried out in pregnancy focus on a single outcome or selected outcomes. However, women must balance the benefit of treatment against all possible adverse effects. We aimed to apply and evaluate a tree-based scan statistic data-mining method (TreeScan; Martin Kulldorff, Harvard Medical School, Boston, Massachusetts) as a safety surveillance approach that allows for simultaneous evaluation of a comprehensive range of adverse pregnancy outcomes, while preserving the overall rate of false-positive alerts. We evaluated TreeScan with a cohort design and adjustment via propensity score techniques, using 2 test cases: 1) opioids and neonatal opioid withdrawal syndrome and 2) valproate and congenital malformations, implemented in pregnancy cohorts nested within the Medicaid Analytic eXtract (January 1, 2000-December 31, 2014) and the IBM MarketScan Research Database (IBM, Armonk, New York) (January 1, 2003-September 30, 2015). In both cases, we identified known safety concerns, with only 1 previously unreported alert at the preset statistical alerting threshold. This evaluation shows the promise of TreeScan-based approaches for systematic drug safety monitoring in pregnancy. A targeted screening approach followed by deeper investigation to refine understanding of potential signals will ensure that pregnant women and their physicians have access to the best available evidence to inform treatment decisions.

A General Propensity Score for Signal Identification Using Tree-Based Scan Statistics.

The tree-based scan statistic (TreeScan; Martin Kulldorff, Harvard Medical School, Boston, Massachusetts) is a data-mining method that adjusts for multiple testing of correlated hypotheses when screening thousands of potential adverse events for signal identification. Simulation has demonstrated the promise of TreeScan with a propensity score (PS)-matched cohort design. However, it is unclear which variables to include in a PS for applied signal identification studies to simultaneously adjust for confounding across potential outcomes. We selected 4 pairs of medications with well-understood safety profiles. For each pair, we evaluated 5 candidate PSs with different combinations of 1) predefined general covariates (comorbidity, frailty, utilization), 2) empirically selected (data-driven) covariates, and 3) covariates tailored to the drug pair. For each pair, statistical alerting patterns were similar with alternative PSs (≤11 alerts in 7,996 outcomes scanned). Inclusion of covariates tailored to exposure did not appreciably affect screening results. Inclusion of empirically selected covariates can provide better proxy coverage for confounders but can also decrease statistical power. Unlike tailored covariates, empirical and predefined general covariates can be applied "out of the box" for signal identification. The choice of PS depends on the level of concern about residual confounding versus loss of power. Potential signals should be followed by pharmacoepidemiologic assessment where confounding control is tailored to the specific outcome(s) under investigation.

Screening Medications for Association with Progression to Wet Age-Related Macular Degeneration.

PURPOSE: There is an urgent need for treatments that prevent or delay development to advanced age-related macular degeneration (AMD). Drugs already on the market for other conditions could affect progression to neovascular AMD (nAMD). If identified, these drugs could provide insights for drug development targets. The objective of this study was to use a novel data mining method that can simultaneously evaluate thousands of correlated hypotheses, while adjusting for multiple testing, to screen for associations between drugs and delayed progression to nAMD. DESIGN: We applied a nested case-control study to administrative insurance claims data to identify cases with nAMD and risk-set sampled controls that were 1:4 variable ratio matched on age, gender, and recent healthcare use. PARTICIPANTS: The study population included cases with nAMD and risk set matched controls. METHODS: We used a tree-based scanning method to evaluate associations between hierarchical classifications of drugs that patients were exposed to within 6 months, 7 to 24 months, or ever before their index date. The index date was the date of first nAMD diagnosis in cases. Risk-set sampled controls were assigned the same index date as the case to which they were matched. The study was implemented using Medicare data from New Jersey and Pennsylvania, and national data from IBM MarketScan Research Database. We set an a priori threshold for statistical alerting at P ≤ 0.01 and focused on associations with large magnitude (relative risks ≥ 2.0). MAIN OUTCOME MEASURES: Progression to nAMD. RESULTS: Of approximately 4000 generic drugs and drug classes evaluated, the method detected 19 distinct drug exposures with statistically significant, large relative risks indicating that cases were less frequently exposed than controls. These included (1) drugs with prior evidence for a causal relationship (e.g., megestrol); (2) drugs without prior evidence for a causal relationship, but potentially worth further exploration (e.g., donepezil, epoetin alfa); (3) drugs with alternative biologic explanations for the association (e.g., sevelamer); and (4) drugs that may have resulted in statistical alerts due to their correlation with drugs that alerted for other reasons. CONCLUSIONS: This exploratory drug-screening study identified several potential targets for follow-up studies to further evaluate and determine if they may prevent or delay progression to advanced AMD.

Control yourself: ISPE-endorsed guidance in the application of self-controlled study designs in pharmacoepidemiology.

PURPOSE: Consensus is needed on conceptual foundations, terminology and relationships among the various self-controlled "trigger" study designs that control for time-invariant confounding factors and target the association between transient exposures (potential triggers) and abrupt outcomes. The International Society for Pharmacoepidemiology (ISPE) funded a working group of ISPE members to develop guidance material for the application and reporting of self-controlled study designs, similar to Standards of Reporting Observational Epidemiology (STROBE). This first paper focuses on navigation between the types of self-controlled designs to permit a foundational understanding with guiding principles. METHODS: We leveraged a systematic review of applications of these designs, that we term Self-controlled Crossover Observational PharmacoEpidemiologic (SCOPE) studies. Starting from first principles and using case examples, we reviewed outcome-anchored (case-crossover [CCO], case-time control [CTC], case-case-time control [CCTC]) and exposure-anchored (self-controlled case-series [SCCS]) study designs. RESULTS: Key methodological features related to exposure, outcome and time-related concerns were clarified, and a common language and worksheet to facilitate the design of SCOPE studies is introduced. CONCLUSIONS: Consensus on conceptual foundations, terminology and relationships among SCOPE designs will facilitate understanding and critical appraisal of published studies, as well as help in the design, analysis and review of new SCOPE studies. This manuscript is endorsed by ISPE.