Evaluating large-scale propensity score performance through real-world and synthetic data experiments

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Association between pre-diagnostic serum uric acid levels in patients with newly diagnosed epilepsy and conversion rate to drug-resistant epilepsy within 5 years: A common data model analysis

PURPOSE

Drug-resistant epilepsy (DRE) poses a significant challenge in epilepsy management, and reliable biomarkers for identifying patients at risk of DRE are lacking. This study aimed to investigate the association between serum uric acid (UA) levels and the conversion rate to DRE.

METHODS

A retrospective cohort study was conducted using a common data model database. The study included patients newly diagnosed with epilepsy, with prediagnostic serum UA levels within a six-month window. Patients were categorized into hyperUA (≥7.0 mg/dL), normoUA (<7.0 and >2.0 mg/dL), and hypoUA (≤2.0 mg/dL) groups based on their prediagnostic UA levels. The outcome was the conversion rate to DRE within five years of epilepsy diagnosis.

RESULTS

The study included 5,672 patients with epilepsy and overall conversion rate to DRE was 19.4%. The hyperUA group had a lower DRE conversion rate compared to the normoUA group (HR: 0.81 [95% CI: 0.69-0.96]), while the hypoUA group had a higher conversion rate (HR: 1.88 [95% CI: 1.38-2.55]).

CONCLUSIONS

Serum UA levels have the potential to serve as a biomarker for identifying patients at risk of DRE, indicating a potential avenue for novel therapeutic strategies aimed at preventing DRE conversion.

Assessing Covariate Balance with Small Sample Sizes

Propensity score adjustment addresses confounding by balancing covariates in subject treatment groups through matching, stratification, inverse probability weighting, etc. Diagnostics ensure that the adjustment has been effective. A common technique is to check whether the standardized mean difference for each relevant covariate is less than a threshold like 0.1. For small sample sizes, the probability of falsely rejecting the validity of a study because of chance imbalance when no underlying balance exists approaches 1. We propose an alternative diagnostic that checks whether the standardized mean difference statistically significantly exceeds the threshold. Through simulation and real-world data, we find that this diagnostic achieves a better trade-off of type 1 error rate and power than standard nominal threshold tests and not testing for sample sizes from 250 to 4000 and for 20 to 100,000 covariates. In network studies, meta-analysis of effect estimates must be accompanied by meta-analysis of the diagnostics or else systematic confounding may overwhelm the estimated effect. Our procedure for statistically testing balance at both the database level and the meta-analysis level achieves the best balance of type-1 error rate and power. Our procedure supports the review of large numbers of covariates, enabling more rigorous diagnostics.

Comparative safety and effectiveness of angiotensin converting enzyme inhibitors and thiazides and thiazide-like diuretics under strict monotherapy

Previous work comparing safety and effectiveness outcomes for new initiators of angiotensin converting-enzyme inhibitors (ACEi) and thiazides demonstrated more favorable outcomes for thiazides, although cohort definitions allowed for addition of a second antihypertensive medication after a week of monotherapy. Here, we modify the monotherapy definition, imposing exit from cohorts upon addition of another antihypertensive medication. We determine hazard ratios (HR) for 55 safety and effectiveness outcomes over six databases and compare results to earlier findings. We find, for all primary outcomes, statistically significant differences in effectiveness between ACEi and thiazides were not replicated (HRs: 1.11, 1.06, 1.12 for acute myocardial infarction, hospitalization with heart failure and stroke, respectively). While statistical significance is similarly lost for several safety outcomes, the safety profile of thiazides remains more favorable. Our results indicate a less striking difference in effectiveness of thiazides compared to ACEi and reflect some sensitivity to the monotherapy cohort definition modification.

Impact of COVID-19 on the development of major mental disorders in patients visiting a university hospital: a retrospective observational study

BACKGROUND

This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on the development of major mental disorders in patients visiting a university hospital.

METHODS

The study participants were patients with COVID-19 (n=5,006) and those without COVID-19 (n=367,162) registered in the database of Keimyung University Dongsan Hospital and standardized with the Observational Medical Outcomes Partnership Common Data Model. Data on major mental disorders that developed in both groups over the 5-year follow-up period were extracted using the FeederNet computer program. A multivariate Cox proportional hazards model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for the incidence of major mental disorders.

RESULTS

The incidences of dementia and sleep, anxiety, and depressive disorders were significantly higher in the COVID-19 group than in the control group. The incidence rates per 1,000 patient-years in the COVID-19 group vs. the control group were 12.71 vs. 3.76 for dementia, 17.42 vs. 7.91 for sleep disorders, 6.15 vs. 3.41 for anxiety disorders, and 8.30 vs. 5.78 for depressive disorders. There was no significant difference in the incidence of schizophrenia or bipolar disorder between the two groups. COVID-19 infection increased the risk of mental disorders in the following order: dementia (HR, 3.49; 95% CI, 2.45-4.98), sleep disorders (HR, 2.27; 95% CI, 1.76-2.91), anxiety disorders (HR, 1.90; 95% CI, 1.25-2.84), and depressive disorders (HR, 1.54; 95% CI, 1.09-2.15).

CONCLUSION

This study showed that the major mental disorders associated with COVID-19 were dementia and sleep, anxiety, and depressive disorders.

Association between prolactin increasing antipsychotic use and the risk of breast cancer: a retrospective observational cohort study in a United States Medicaid population

Introduction

Results of retrospective studies examining the relationship between prolactin increasing antipsychotics and incident breast cancer have been inconsistent. This study assessed the association between use of high prolactin increasing antipsychotics (HPD) and the incidence of breast cancer using best practices in pharmacoepidemiology.

Methods

Using administrative claims data from the MarketScan Medicaid database, schizophrenia patients initiating antipsychotics were identified. Those initiating HPD were compared with new users of non/low prolactin increasing drugs (NPD). Two definitions of breast cancer, two at-risk periods, and two large-scale propensity score (PS) adjustment methods were used in separate analyses. PS models included all previously diagnosed conditions, medication use, demographics, and other available medical history. Negative control outcomes were used for empirical calibration.

Results

Five analysis variants passed all diagnostics for sufficient statistical power and balance across all covariates. Four of the five variants used an intent-to-treat (ITT) approach. Between 4,256 and 6,341 patients were included in each group for the ITT analyses, and patients contributed approximately four years of follow-up time on average. There was no statistically significant association between exposure to HPD and risk of incident breast cancer in any analysis, and hazard ratios remained close to 1.0, ranging from 0.96 (95% confidence interval 0.62 - 1.48) to 1.28 (0.40 - 4.07).

Discussion

Using multiple PS methods, outcome definitions and at-risk periods provided robust and consistent results which found no evidence of an association between use of HPD and risk of breast cancer.

Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study

PURPOSE

To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab.

DESIGN

Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network.

SUBJECTS

Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion).

METHODS

The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate.

MAIN OUTCOME MEASURES

Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure.

RESULTS

Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60).

CONCLUSIONS

There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents.

FINANCIAL DISCLOSURES

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Comparative estimation of the effects of antihypertensive medications on schizophrenia occurrence: a multinational observational cohort study

BACKGROUND

The association between antihypertensive medication and schizophrenia has received increasing attention; however, evidence of the impact of antihypertensive medication on subsequent schizophrenia based on large-scale observational studies is limited. We aimed to compare the schizophrenia risk in large claims-based US and Korea cohort of patients with hypertension using angiotensin-converting enzyme (ACE) inhibitors versus those using angiotensin receptor blockers (ARBs) or thiazide diuretics.

METHODS

Adults aged 18 years who were newly diagnosed with hypertension and received ACE inhibitors, ARBs, or thiazide diuretics as first-line antihypertensive medications were included. The study population was sub-grouped based on age (> 45 years). The comparison groups were matched using a large-scale propensity score (PS)-matching algorithm. The primary endpoint was incidence of schizophrenia.

RESULTS

5,907,522; 2,923,423; and 1,971,549 patients used ACE inhibitors, ARBs, and thiazide diuretics, respectively. After PS matching, the risk of schizophrenia was not significantly different among the groups (ACE inhibitor vs. ARB: summary hazard ratio [HR] 1.15 [95% confidence interval, CI, 0.99-1.33]; ACE inhibitor vs. thiazide diuretics: summary HR 0.91 [95% CI, 0.78-1.07]). In the older subgroup, there was no significant difference between ACE inhibitors and thiazide diuretics (summary HR, 0.91 [95% CI, 0.71-1.16]). The risk for schizophrenia was significantly higher in the ACE inhibitor group than in the ARB group (summary HR, 1.23 [95% CI, 1.05-1.43]).

CONCLUSIONS

The risk of schizophrenia was not significantly different between the ACE inhibitor vs. ARB and ACE inhibitor vs. thiazide diuretic groups. Further investigations are needed to determine the risk of schizophrenia associated with antihypertensive drugs, especially in people aged > 45 years.

Body mass index changes and their association with SARS-CoV-2 infection: a real-world analysis

Objective

To study body mass index (BMI) changes among individuals aged 18-99 years with and without SARS-CoV-2 infection.

Subjects/Methods

Using real-world data from the OneFlorida+ Clinical Research Network of the National Patient-Centered Clinical Research Network, we compared changes over time in BMI in an Exposed cohort (positive SARS-CoV-2 test between March 2020 - January 2022), to a contemporary Unexposed cohort (negative SARS-CoV-2 tests), and an age/sex-matched Historical control cohort (March 2018 - January 2020). Body mass index (kg/m2) was retrieved from objective measures of height and weight in electronic health records. We used target trial approaches to estimate BMI at baseline and change per 100 days of follow-up for Unexposed and Historical cohorts relative to the Exposed cohort by categories of sex, race-ethnicity, age, and hospitalization status.

Results

The study sample consisted of 44,436 (Exposed cohort), 164,118 (Unexposed cohort), and 41,189 (Historical cohort). Cumulatively, 62% were women, 21.5% Non-Hispanic Black, 21.4% Hispanic and 5.6% Non-Hispanic Other. Patients had an average age of 51.9 years (SD: 18.9). At baseline, relative to the Exposed cohort (mean BMI: 29.3 kg/m2 [95%CI: 29.0, 29.7]), the Unexposed (-0.07 kg/m2 [95%CI; -0.12, -0.01]) and Historical controls (-0.27 kg/m2 [95%CI; -0.34, -0.20]) had lower BMI. Relative to no change in the Exposed over 100 days (0.00 kg/m2 [95%CI; -0.03,0.03]), the BMI of those Unexposed decreased (-0.04 kg/m2 [95%CI; -0.06, -0.01]) while the Historical cohort's BMI increased (+0.03 kg/m2 [95%CI;0.00,0.06]). BMI changes were consistent between Exposed and Unexposed cohorts for most population groups, except at start of follow-up period among Males and those 65 years or older, and in changes over 100 days among Males and Hispanics.

Conclusions

In a diverse real-world cohort of adults, mean BMI of those with and without SARS-CoV2 infection varied in their trajectories. The mechanisms and implications of weight retention following SARS-CoV-2 infection remain unclear.

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study

Background

SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials.

Methods

Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis.

Findings

Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]).

Interpretation

In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD.

Funding

National Institutes of Health, United States Department of Veterans Affairs.

Observational methods for COVID-19 vaccine effectiveness research: an empirical evaluation and target trial emulation

BACKGROUND

There are scarce data on best practices to control for confounding in observational studies assessing vaccine effectiveness to prevent COVID-19. We compared the performance of three well-established methods [overlap weighting, inverse probability treatment weighting and propensity score (PS) matching] to minimize confounding when comparing vaccinated and unvaccinated people. Subsequently, we conducted a target trial emulation to study the ability of these methods to replicate COVID-19 vaccine trials.

METHODS

We included all individuals aged ≥75 from primary care records from the UK [Clinical Practice Research Datalink (CPRD) AURUM], who were not infected with or vaccinated against SARS-CoV-2 as of 4 January 2021. Vaccination status was then defined based on first COVID-19 vaccine dose exposure between 4 January 2021 and 28 January 2021. Lasso regression was used to calculate PS. Location, age, prior observation time, regional vaccination rates, testing effort and COVID-19 incidence rates at index date were forced into the PS. Following PS weighting and matching, the three methods were compared for remaining covariate imbalance and residual confounding. Last, a target trial emulation comparing COVID-19 at 3 and 12 weeks after first vaccine dose vs unvaccinated was conducted.

RESULTS

Vaccinated and unvaccinated cohorts comprised 583 813 and 332 315 individuals for weighting, respectively, and 459 000 individuals in the matched cohorts. Overlap weighting performed best in terms of minimizing confounding and systematic error. Overlap weighting successfully replicated estimates from clinical trials for vaccine effectiveness for ChAdOx1 (57%) and BNT162b2 (75%) at 12 weeks.

CONCLUSION

Overlap weighting performed best in our setting. Our results based on overlap weighting replicate previous pivotal trials for the two first COVID-19 vaccines approved in Europe.

The Risk of COVID-19 and Its Outcomes in Korean Patients With Gout: A Multicenter, Retrospective, Observational Study

This retrospective cohort study aimed to compare coronavirus disease 2019 (COVID-19)-related clinical outcomes between patients with and without gout. Electronic health record-based data from two centers (Seoul National University Hospital [SNUH] and Boramae Medical Center [BMC]), from January 2021 to April 2022, were mapped to a common data model. Patients with and without gout were matched using a large-scale propensity-score algorithm based on population-level estimation methods. At the SNUH, the risk for COVID-19 diagnosis was not significantly different between patients with and without gout (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.59-1.84). Within 30 days after COVID-19 diagnosis, no significant difference was observed in terms of hospitalization (HR, 0.57; 95% CI, 0.03-3.90), severe outcomes (HR, 2.90; 95% CI, 0.54-13.71), or mortality (HR, 1.35; 95% CI, 0.06-16.24). Similar results were obtained from the BMC database, suggesting that gout does not increase the risk for COVID-19 diagnosis or severe outcomes.

Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research

The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared. Designed to run across a wide array of technical environments, including different operating systems and database platforms, HADES uses continuous integration with a large set of unit tests to maintain reliability. HADES implements OHDSI best practices, and is used in almost all published OHDSI studies, including some that have directly informed regulatory decisions.

Combining Super Learner with high-dimensional propensity score to improve confounding adjustment: A real-world application in chronic lymphocytic leukemia

PURPOSE

High-dimensional propensity score (hdPS) is a semiautomated method that leverages a vast number of covariates available in healthcare databases to improve confounding adjustment. A novel combined Super Learner (SL)-hdPS approach was proposed to assist with selecting the number of covariates for propensity score inclusion, and was found in plasmode simulation studies to improve bias reduction and precision compared to hdPS alone. However, the approach has not been examined in the applied setting.

METHODS

We compared SL-hdPS's performance with that of several hdPS models, each with prespecified covariates and a different number of empirically-identified covariates, using a cohort study comparing real-world bleeding rates between ibrutinib- and bendamustine-rituximab (BR)-treated individuals with chronic lymphocytic leukemia in Optum's de-identified Clinformatics® Data Mart commercial claims database (2013-2020). We used inverse probability of treatment weighting for confounding adjustment and Cox proportional hazards regression to estimate hazard ratios (HRs) for bleeding outcomes. Parameters of interest included prespecified and empirically-identified covariate balance (absolute standardized difference [ASD] thresholds of <0.10 and <0.05) and outcome HR precision (95% confidence intervals).

RESULTS

We identified 2423 ibrutinib- and 1102 BR-treated individuals. Including >200 empirically-identified covariates in the hdPS model compromised covariate balance at both ASD thresholds. SL-hdPS balanced more covariates than all individual hdPS models at both ASD thresholds. The bleeding HR 95% confidence intervals were generally narrower with SL-hdPS than with individual hdPS models.

CONCLUSION

In a real-world application, hdPS was sensitive to the number of covariates included, while use of SL for covariate selection resulted in improved covariate balance and possibly improved precision.

Patient-Centered Economic Burden of Exudative Age-Related Macular Degeneration: Retrospective Cohort Study

BACKGROUND

Exudative age-related macular degeneration (AMD), one of the leading causes of blindness, requires expensive drugs such as anti-vascular endothelial growth factor (VEGF) agents. The long-term regular use of effective but expensive drugs causes an economic burden for patients with exudative AMD. However, there are no studies on the long-term patient-centered economic burden of exudative AMD after reimbursement of anti-VEGFs.

OBJECTIVE

This study aimed to evaluate the patient-centered economic burden of exudative AMD for 2 years, including nonreimbursement and out-of-pocket costs, compared with nonexudative AMD using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).

METHODS

This retrospective cohort study was conducted using the OMOP CDM, which included 2,006,478 patients who visited Seoul National University Bundang Hospital from June 2003 to July 2019. We defined the exudative AMD group as patients aged >50 years with a diagnosis of exudative AMD and a prescription for anti-VEGFs or verteporfin. The control group was defined as patients aged >50 years without a diagnosis of exudative AMD or a prescription for anti-VEGFs or verteporfin. To adjust for selection bias, controls were matched by propensity scores using regularized logistic regression with a Laplace prior. We measured any medical cost occurring in the hospital as the economic burden of exudative AMD during a 2-year follow-up period using 4 categories: total medical cost, reimbursement cost, nonreimbursement cost, and out-of-pocket cost. To estimate the average cost by adjusting the confounding variable and overcoming the positive skewness of costs, we used an exponential conditional model with a generalized linear model.

RESULTS

We identified 931 patients with exudative AMD and matched 783 (84.1%) with 2918 patients with nonexudative AMD. In the exponential conditional model, the total medical, reimbursement, nonreimbursement, and out-of-pocket incremental costs were estimated at US $3426, US $3130, US $366, and US $561, respectively, in the first year and US $1829, US $1461, US $373, and US $507, respectively, in the second year. All incremental costs in the exudative AMD group were 1.89 to 4.25 and 3.50 to 5.09 times higher in the first and second year, respectively, than those in the control group (P<.001 in all cases).

CONCLUSIONS

Exudative AMD had a significantly greater economic impact (P<.001) for 2 years on reimbursement, nonreimbursement, and out-of-pocket costs than nonexudative AMD after adjusting for baseline demographic and clinical characteristics using the OMOP CDM. Although economic policies could relieve the economic burden of patients with exudative AMD over time, the out-of-pocket cost of exudative AMD was still higher than that of nonexudative AMD for 2 years. Our findings support the need for expanding reimbursement strategies for patients with exudative AMD given the significant economic burden faced by patients with incurable and fatal diseases both in South Korea and worldwide.

Proton pump inhibitors and chronic kidney disease risk: a comparative study with histamine-2 receptor antagonists

This observational study explored the association between proton pump inhibitor (PPI) and histamine-2 receptor antagonist (H2RA) use and the risk of chronic kidney disease (CKD). Using the National Health Insurance Service-National Sample Cohort (NHIS-NSC) and six-hospital electronic health record (EHR) databases, CKD incidence was analyzed among PPI and H2RA users. Propensity score matching was used to balance baseline characteristics, with 1,869 subjects each in the PPI and H2RA groups from the NHIS-NSC, and 5,967 in EHR databases. CKD incidence was similar for both groups (5.72/1000 person-years vs. 7.57/1000 person-years; HR = 0.68; 95% CI, 0.35-1.30). A meta-analysis of the EHR databases showed no significant increased CKD risk associated with PPI use (HR = 1.03, 95% CI: 0.87-1.23). These results suggest PPI use may not increase CKD risk compared to H2RA use, but the potential role of PPI-induced CKD needs further research. Clinicians should consider this when prescribing long-term PPI therapy.

Evaluation of neuroleptic malignant syndrome induced by antipsychotic drugs using spontaneous reporting system

Adverse events (AEs) of antipsychotic drugs include neuroleptic malignant syndrome (NMS), which presents complex clinical symptoms, resulting in a fatal outcome. In this study, the association between antipsychotic drugs and NMS was comprehensively evaluated by cluster and association analyses using the Japanese Adverse Drug Event Report (JADER) database. The analyses were performed using 20 typical antipsychotics (TAPs) alongside 9 atypical antipsychotics (AAPs). The Standardised MedDRA Queries (SMQ) database was used to analyze NMS (SMQ code: 20000044). Reporting odds ratios (RORs) were used for AE signal detection. The relationship between antipsychotic drugs and AEs for NMS was investigated by performing hierarchical cluster analysis using Ward's method. Between April 2004 and September 2021, the total number of JADER reports was 705,294. RORs (95 % confidence interval) of NMS for haloperidol, chlorpromazine, risperidone, and aripiprazole were 12.1 (11.1-13.3), 6.3 (5.7-7.0), 6.2 (5.8-6.6), and 4.7 (4.4-5.1), respectively. Three clusters were formed, with characteristics as follows: Cluster 1 consisted of only TAPs, such as bromperidol and fluphenazine, whilst having a high reporting rate of hypotension, tachycardia, dyskinesia, and dystonia. Cluster 2 consisted of all AAPs alongside several TAPs, such as haloperidol and chlorpromazine, with higher reporting rates of disturbance of consciousness, extrapyramidal disorders (excluding dyskinesia and dystonia), and serotonin syndrome. Cluster 3 consisted of only perphenazine, whilst having a higher reporting rate of coma, leukocytosis, and Parkinsonism. The results of this study may therefore aid in the management of NMS using antipsychotic drugs.

Impact of Trimetazidine on the Incident Heart Failure After Coronary Artery Revascularization

ABSTRACT

Abnormal myocardial metabolism is a common pathophysiological process underlying ischemic heart disease and heart failure (HF). Trimetazidine is an antianginal agent with a unique mechanism of action that regulates myocardial energy metabolism and might have a beneficial effect in preventing HF in patients undergoing myocardial revascularization. We aimed to evaluate the potential benefit of trimetazidine in preventing incident hospitalization for HF after myocardial revascularization. Using the common data model, we identified patients without prior HF undergoing myocardial revascularization from 8 hospital databases in Korea. To compare clinical outcomes using trimetazidine, database-level hazard ratios (HRs) were estimated using large-scale propensity score matching for each database and pooled using a random-effects model. The primary outcome was incident hospitalization for HF. The secondary outcome of interest was major adverse cardiac events (MACEs). After propensity score matching, 6724 and 11,211 patients were allocated to trimetazidine new-users and nonusers, respectively. There was no significant difference in the incidence of hospitalization for HF between the 2 groups (HR: 1.08, 95% confidence interval [CI], 0.88-1.31; P = 0.46). The risk of MACE also did not differ between the 2 groups (HR: 1.07, 95% CI, 0.98-1.16; P = 0.15). In conclusion, the use of trimetazidine did not reduce the risk of hospitalization for HF or MACE in patients undergoing myocardial revascularization. Therefore, the role of trimetazidine in contemporary clinical practice cannot be expanded beyond its current role as an add-on treatment for symptomatic angina.

An application of the Causal Roadmap in two safety monitoring case studies: Causal inference and outcome prediction using electronic health record data

Background

Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to assess and ensure that analyses meet their intended goals.

Methods

The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence.

Results

In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative - a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping.

Conclusion

These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis.

Impact of symptomatic menopausal transition on the occurrence of depression, anxiety, and sleep disorders: A real-world multi-site study

BACKGROUND

The menopause transition is a vulnerable period that can be associated with changes in mood and cognition. The present study aimed to investigate whether a symptomatic menopausal transition increases the risks of depression, anxiety, and sleep disorders.

METHODS

This population-based, retrospective cohort study analysed data from five electronic health record databases in South Korea. Women aged 45-64 years with and without symptomatic menopausal transition were matched 1:1 using propensity-score matching. Subgroup analyses were conducted according to age and use of hormone replacement therapy (HRT). A primary analysis of 5-year follow-up data was conducted, and an intention-to-treat analysis was performed to identify different risk windows over 5 or 10 years. The primary outcome was first-time diagnosis of depression, anxiety, and sleep disorder. We used Cox proportional hazard models and a meta-analysis to calculate the summary hazard ratio (HR) estimates across the databases.

RESULTS

Propensity-score matching resulted in a sample of 17,098 women. Summary HRs for depression (2.10; 95% confidence interval [CI] 1.63-2.71), anxiety (1.64; 95% CI 1.01-2.66), and sleep disorders (1.47; 95% CI 1.16-1.88) were higher in the symptomatic menopausal transition group. In the subgroup analysis, the use of HRT was associated with an increased risk of depression (2.21; 95% CI 1.07-4.55) and sleep disorders (2.51; 95% CI 1.25-5.04) when compared with non-use of HRT.

CONCLUSIONS

Our findings suggest that women with symptomatic menopausal transition exhibit an increased risk of developing depression, anxiety, and sleep disorders. Therefore, women experiencing a symptomatic menopausal transition should be monitored closely so that interventions can be applied early.

Ranitidine Use and Incident Cancer in a Multinational Cohort

Importance

Ranitidine, the most widely used histamine-2 receptor antagonist (H2RA), was withdrawn because of N-nitrosodimethylamine impurity in 2020. Given the worldwide exposure to this drug, the potential risk of cancer development associated with the intake of known carcinogens is an important epidemiological concern.

Objective

To examine the comparative risk of cancer associated with the use of ranitidine vs other H2RAs.

Design, Setting, and Participants

This new-user active comparator international network cohort study was conducted using 3 health claims and 9 electronic health record databases from the US, the United Kingdom, Germany, Spain, France, South Korea, and Taiwan. Large-scale propensity score (PS) matching was used to minimize confounding of the observed covariates with negative control outcomes. Empirical calibration was performed to account for unobserved confounding. All databases were mapped to a common data model. Database-specific estimates were combined using random-effects meta-analysis. Participants included individuals aged at least 20 years with no history of cancer who used H2RAs for more than 30 days from January 1986 to December 2020, with a 1-year washout period. Data were analyzed from April to September 2021.

Exposure

The main exposure was use of ranitidine vs other H2RAs (famotidine, lafutidine, nizatidine, and roxatidine).

Main Outcomes and Measures

The primary outcome was incidence of any cancer, except nonmelanoma skin cancer. Secondary outcomes included all cancer except thyroid cancer, 16 cancer subtypes, and all-cause mortality.

Results

Among 1 183 999 individuals in 11 databases, 909 168 individuals (mean age, 56.1 years; 507 316 [55.8%] women) were identified as new users of ranitidine, and 274 831 individuals (mean age, 58.0 years; 145 935 [53.1%] women) were identified as new users of other H2RAs. Crude incidence rates of cancer were 14.30 events per 1000 person-years (PYs) in ranitidine users and 15.03 events per 1000 PYs among other H2RA users. After PS matching, cancer risk was similar in ranitidine compared with other H2RA users (incidence, 15.92 events per 1000 PYs vs 15.65 events per 1000 PYs; calibrated meta-analytic hazard ratio, 1.04; 95% CI, 0.97-1.12). No significant associations were found between ranitidine use and any secondary outcomes after calibration.

Conclusions and Relevance

In this cohort study, ranitidine use was not associated with an increased risk of cancer compared with the use of other H2RAs. Further research is needed on the long-term association of ranitidine with cancer development.

Retrospective Case-Control Study of REGEN-COV (Casirivimab and Imdevimab) Therapy for Patients with COVID-19 and Cancer Using the United States MarketScan® Database

INTRODUCTION

Patients with cancer may be at a higher risk of experiencing severe complications from coronavirus disease 2019 (COVID-19) than are patients without cancer. This study evaluated the efficacy of REGEN-COV, a combination of the monoclonal antibodies casirivimab and imdevimab, for treating COVID-19 in patients with cancer in the USA.

METHODS

Using the MarketScanⓇ database, de-identified data of patients with a COVID-19 diagnosis between November 1, 2020, and November 30, 2021, were analyzed. In the preliminary study, patients with COVID-19 were divided into two groups: those with and without cancer within 1 year prior to a COVID-19 diagnosis. In the main study, patients with COVID-19 with cancer were divided into two groups: those with and without REGEN-COV treatment. Patient outcomes, such as COVID-19-derived hospitalization, hospitalization duration, and medical costs, were assessed between these two groups by propensity score matching.

RESULTS

Within the first 30 days of a COVID-19 diagnosis, the group treated with REGEN-COV had fewer hospitalizations (3.2% vs. 13.3%; p < 0.001), fewer mean hospitalization days (0.2 vs. 1.1 days; p < 0.001), and a lower mean-associated medical payment (2,709 vs. 8,120 USD; p < 0.001) than the group not treated with REGEN-COV. Patients with specific cancer types, including non-Hodgkin's lymphoma, leukemia, and lung cancer, had higher hospitalization rates than those with other cancer types.

CONCLUSION

Patients with cancer treated with REGEN-COV experienced a decreased risk for hospitalization, hospitalization duration, and total COVID-19-related costs. Patients with cancer were at a higher risk of being hospitalized for COVID-19 than were those without cancer. The use of neutralizing antibody therapy may reduce the risk of severe COVID-19 infection for patients with cancer with an otherwise high risk. Future replication studies should be conducted using other databases that include Medicaid users and other insured persons for comparison and validation.

Padé approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes

OBJECTIVE

We developed and evaluated a novel one-shot distributed algorithm for evidence synthesis in distributed research networks with rare outcomes.

MATERIALS AND METHODS

Fed-Padé, motivated by a classic mathematical tool, Padé approximants, reconstructs the multi-site data likelihood via Padé approximant whose key parameters can be computed distributively. Thanks to the simplicity of [2,2] Padé approximant, Fed-Padé requests an extremely simple task and low communication cost for data partners. Specifically, each data partner only needs to compute and share the log-likelihood and its first 4 gradients evaluated at an initial estimator. We evaluated the performance of our algorithm with extensive simulation studies and four observational healthcare databases.

RESULTS

Our simulation studies revealed that a [2,2]-Padé approximant can well reconstruct the multi-site likelihood so that Fed-Padé produces nearly identical estimates to the pooled analysis. Across all simulation scenarios considered, the median of relative bias and rate of instability of our Fed-Padé are both <0.1%, whereas meta-analysis estimates have bias up to 50% and instability up to 75%. Furthermore, the confidence intervals derived from the Fed-Padé algorithm showed better coverage of the truth than confidence intervals based on the meta-analysis. In real data analysis, the Fed-Padé has a relative bias of <1% for all three comparisons for risks of acute liver injury and decreased libido, whereas the meta-analysis estimates have a substantially higher bias (around 10%).

CONCLUSION

The Fed-Padé algorithm is nearly lossless, stable, communication-efficient, and easy to implement for models with rare outcomes. It provides an extremely suitable and convenient approach for synthesizing evidence in distributed research networks with rare outcomes.

Impact of Concomitant Use of Proton Pump Inhibitors and Clopidogrel on Recurrent Stroke and Myocardial Infarction

BACKGROUND/AIMS

Conflicting results have been reported regarding the interaction between proton pump inhibitors (PPIs) and clopidogrel. We investigated whether concomitant PPI use influenced the risk of recurrence in patients with stroke and myocardial infarction (MI).

METHODS

This study used two databases for two different designs, the Korean National Health Insurance Service (NHIS) database for a self-controlled case series design, and the national sample cohort of the NHIS data base converted to the Observational Medical Outcomes Partnership-Common Data Model version for a cohort study based on large-scale propensity score matching.

RESULTS

In the PPI co-prescription group, recurrent hospitalization with stroke occurred in 17.6% of the 8201 patients with history of stroke, and recurrent MI occurred in 17.1% of the 1216 patients with history of MI within1 year. According to the self-controlled case series, the overall relative risk (RR) of recurrent stroke was 2.09 (95% confidence interval (CI); 1.83-2.38); the RR showed an increasing trend parallel to the time from the beginning of PPI co-prescription. In the cohort study, there was a higher incidence of recurrent stroke in the PPI co-prescription group (Hazard ratio (HR): 1.34, 95% CI: 1.01-1.76, p = 0.04). The overall RR of recurrent MI was 1.47 (95% CI; 1.02-2.11) in the self-controlled case series; however, there was no statistically significant difference in recurrent MI in the cohort study (HR:1.42, 95% CI:0.79-2.49, p = 0.23). The impact of individual PPIs on stroke and MI showed different patterns.

CONCLUSIONS

A PPI co-prescription >4 weeks with clopidogrel was associated with hospitalization of recurrent stroke within 1 year of initial diagnosis; however, its association with recurrent MI remains inconclusive. The influence of individual PPIs should be clarified in the future.

Shrinkage with shrunken shoulders: Gibbs sampling shrinkage model posteriors with guaranteed convergence rates

Use of continuous shrinkage priors - with a "spike" near zero and heavy-tails towards infinity - is an increasingly popular approach to induce sparsity in parameter estimates. When the parameters are only weakly identified by the likelihood, however, the posterior may end up with tails as heavy as the prior, jeopardizing robustness of inference. A natural solution is to "shrink the shoulders" of a shrinkage prior by lightening up its tails beyond a reasonable parameter range, yielding a regularized version of the prior. We develop a regularization approach which, unlike previous proposals, preserves computationally attractive structures of original shrinkage priors. We study theoretical properties of the Gibbs sampler on resulting posterior distributions, with emphasis on convergence rates of the Pólya-Gamma Gibbs sampler for sparse logistic regression. Our analysis shows that the proposed regularization leads to geometric ergodicity under a broad range of global-local shrinkage priors. Essentially, the only requirement is for the prior π local ( ⋅ ) on the local scale λ to satisfy π local ( 0 ) < ∞ . If π local ( ⋅ ) further satisfies lim λ → 0 π local ( λ ) / λ a < ∞ for a > 0 , as in the case of Bayesian bridge priors, we show the sampler to be uniformly ergodic.

Evaluating the Performance of High-Dimensional Propensity Scores Compared with Standard Propensity Scores for Comparing Antihypertensive Therapies in the CPRD GOLD Database

INTRODUCTION

Propensity score (PS) matching is widely used in medical record studies to create balanced treatment groups, but relies on prior knowledge of confounding factors. High-dimensional PS (hdPS) is a semi-automated algorithm that selects variables with the highest potential for confounding from medical databases. The objective of this study was to evaluate performance of hdPS and PS when used to compare antihypertensive therapies in the UK clinical practice research datalink (CPRD) GOLD database.

METHODS

Patients initiating antihypertensive treatment with either monotherapy or bitherapy were extracted from the CPRD GOLD database. Simulated datasets were generated using plasmode simulations with a marginal hazard ratio (HRm) of 1.29 for bitherapy versus monotherapy for reaching blood pressure control at 3 months. Either 16 or 36 known covariates were forced into the PS and hdPS models, and 200 additional variables were automatically selected for hdPS. Sensitivity analyses were conducted to assess the impact of removing known confounders from the database on hdPS performance.

RESULTS

With 36 known covariates, the estimated HRm (RMSE) was 1.31 (0.05) for hdPS and 1.30 (0.04) for PS matching; the crude HR was 0.68 (0.61). Using 16 known covariates, the estimated HRm (RMSE) was 1.23 (0.10) and 1.09 (0.20) for hdPS and PS, respectively. Performance of hdPS was not compromised when known confounders were removed from the database.

RESULTS ON REAL DATA

With 49 investigator-selected covariates, the HR was 1.18 (95% CI 1.10; 1.26) for PS and 1.33 (95% CI 1.22; 1.46) for hdPS. Both methods yielded the same conclusion, suggesting superiority of bitherapy over monotherapy for time to blood pressure control.

CONCLUSION

HdPS can identify proxies for missing confounders, thereby having an advantage over PS in case of unobserved covariates. Both PS and hdPS showed superiority of bitherapy over monotherapy for reaching blood pressure control.

DR-VIDAL - Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data

Determining causal effects of interventions onto outcomes from real-world, observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is challenging due to underlying bias. Causal deep learning has improved over traditional techniques for estimating individualized treatment effects (ITE). We present the Doubly Robust Variational Information-theoretic Deep Adversarial Learning (DR-VIDAL), a novel generative framework that combines two joint models of treatment and outcome, ensuring an unbiased ITE estimation even when one of the two is misspecified. DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii) an information-theoretic generative adversarial network (Info-GAN) to generate counterfactuals; (iii) a doubly robust block incorporating treatment propensities for outcome predictions. On synthetic and real-world datasets (Infant Health and Development Program, Twin Birth Registry, and National Supported Work Program), DR-VIDAL achieves better performance than other non-generative and generative methods. In conclusion, DR-VIDAL uniquely fuses causal assumptions, VAE, Info-GAN, and doubly robustness into a comprehensive, per- formant framework. Code is available at: https://github.com/Shantanu48114860/DR-VIDAL-AMIA-22 under MIT license.

Cardiovascular outcomes among patients with castration-resistant prostate cancer: A comparative safety study using US administrative claims data

BACKGROUND

Cardiovascular conditions are the most prevalent comorbidity among patients with prostate cancer, regardless of treatment. Additionally, cardiovascular risk has been shown to increase following exposure to certain treatments for advanced prostate cancer. There is conflicting evidence on risk of overall and specific cardiovascular outcomes among men treated for metastatic castrate resistant prostate cancer (CRPC). We, therefore, sought to compare incidence of serious cardiovascular events among CRPC patients treated with abiraterone acetate plus predniso(lo)ne (AAP) and enzalutamide (ENZ), the two most widely used CRPC therapies.

METHODS

Using US administrative claims data, we selected CRPC patients newly exposed to either treatment after August 31, 2012, with prior androgen deprivation therapy (ADT). We assessed incidence of hospitalization for heart failure (HHF), ischemic stroke, and acute myocardial infarction (AMI) during the period 30-days after AAP or ENZ initiation to discontinuation, outcome occurrence, death, or disenrollment. We matched treatment groups on propensity-scores (PSs) to control for observed confounding to estimate the average treatment effect among the treated (AAP) using conditional Cox proportional hazards models. To account for residual bias, we calibrated our estimates against a distribution of effect estimates from 124 negative-control outcomes.

RESULTS

The HHF analysis included 2322 (45.1%) AAP initiators and 2827 (54.9%) ENZ initiators. In this analysis, the median follow-up times among AAP and ENZ initiators (after PS matching) were 144 and 122 days, respectively. The empirically calibrated hazard ratio (HR) estimate for HHF was 2.56 (95% confidence interval [CI]: 1.32, 4.94). Corresponding HRs for AMI and ischemic stroke were 1.94 (95% CI: 0.90, 4.18) and 1.25 (95% CI: 0.54, 2.85), respectively.

CONCLUSIONS

Our study sought to quantify risk of HHF, AMI and ischemic stroke among CRPC patients initiating AAP relative to ENZ within a national administrative claims database. Increased risk for HHF among AAP compared to ENZ users was observed. The difference in myocardial infarction did not attain statistical significance after controlling for residual bias, and no differences were noted in ischemic stroke between the two treatments. These findings confirm labeled warnings and precautions for AAP for HHF and contribute to the comparative real-world evidence on AAP relative to ENZ.

High-dimensional propensity scores for empirical covariate selection in secondary database studies: Planning, implementation, and reporting

Real-world evidence used for regulatory, payer, and clinical decision-making requires principled epidemiology in design and analysis, applying methods to minimize confounding given the lack of randomization. One technique to deal with potential confounding is propensity score (PS) analysis, which allows for the adjustment for measured preexposure covariates. Since its first publication in 2009, the high-dimensional propensity score (hdPS) method has emerged as an approach that extends traditional PS covariate selection to include large numbers of covariates that may reduce confounding bias in the analysis of healthcare databases. hdPS is an automated, data-driven analytic approach for covariate selection that empirically identifies preexposure variables and proxies to include in the PS model. This article provides an overview of the hdPS approach and recommendations on the planning, implementation, and reporting of hdPS used for causal treatment-effect estimations in longitudinal healthcare databases. We supply a checklist with key considerations as a supportive decision tool to aid investigators in the implementation and transparent reporting of hdPS techniques, and to aid decision-makers unfamiliar with hdPS in the understanding and interpretation of studies employing this approach. This article is endorsed by the International Society for Pharmacoepidemiology.

Real-World Trends in the Evaluation of Medical Products

There is a compelling need to evaluate the real-world health effects of medical products outside of tightly controlled preapproval clinical trials. This is done through pharmacoepidemiology, which is the study of the health effects of medical products (including drugs, biologicals, and medical devices and diagnostics) in populations, often using nonrandomized designs. Recent developments in pharmacoepidemiology span changes in the focus of research questions, research designs, data used, and statistical analysis methods. Developments in these areas are thought to improve the value of the evidence produced by such studies, and are prompting greater use of real-world evidence to inform clinical, regulatory, and reimbursement decisions.

Risk of COVID-19 Diagnosis and Hospitalisation in Patients with Osteoarthritis or Back Pain Treated with Ibuprofen Compared to Other NSAIDs or Paracetamol: A Network Cohort Study

OBJECTIVE

We aimed to investigate whether ibuprofen use, compared with other non-selective non-steroidal anti-inflammatory drugs (ns-NSAIDs), cyclooxygenase-2 inhibitors (COX-2i) or paracetamol, increases the risk of coronavirus disease 2019 (COVID-19) diagnosis or hospitalisation.

DESIGN

A prevalent user and active comparator cohort study.

SETTING

Two US claims databases (Open Claims and PharMetrics Plus) mapped to the Observational Medical Outcomes Partnership Common Data Model.

PARTICIPANTS

Insured patients with a history of osteoarthritis or back pain and receiving ibuprofen, other ns-NSAIDs, COX-2i or paracetamol between 1 November, 2019 and 31 January, 2020 (study enrolment window 1) or between 1 February, 2020 and 31 October, 2020 (study enrolment window 2).

MAIN OUTCOME MEASURES

Large-scale propensity score matching and empirical calibration were used to minimise confounding. Incidence and hazard ratios of COVID-19 diagnosis and hospitalisation according to drug/s use were estimated and pooled in the same study period across data sources using a fixed-effects meta-analysis. Index treatment episode was the primary risk evaluation window, censored at the time of discontinuation.

RESULTS

A total of 633,562 and 1,063,960 participants were included in periods 1 and 2, respectively, for the ibuprofen versus ns-NSAIDs comparison, 311,669 and 524,470 for ibuprofen versus COX-2i, and 492,002 and 878,598 for ibuprofen versus paracetamol. Meta-analyses of empirically calibrated hazard ratios revealed no significantly differential risk of COVID-19 outcomes in users of ibuprofen versus any of the other studied analgesic classes: hazard ratios were 1.13 (0.96-1.33) for the ibuprofen-ns-NSAIDs comparison, 1.03 (0.83-1.28) for the ibuprofen-COX-2i comparison and 1.13 (0.74-1.73) for ibuprofen-paracetamol comparison on COVID-19 diagnosis in the February 2020-October 2020 window. Similar hazard ratios were found on COVID-19 hospitalisation and across both study periods.

CONCLUSIONS

In patients with osteoarthritis or back pain, we found no differential risks of incident COVID-19 diagnosis or COVID-19 hospitalisation for ibuprofen users compared with other ns-NSAIDs, COX-2i or paracetamol. Our findings support regulatory recommendations that NSAIDs, including ibuprofen, should be prescribed as indicated in the same way as before the COVID-19 pandemic, especially for those who rely on ibuprofen or NSAIDs to manage chronic arthritis or musculoskeletal pain symptoms.

External control arm analysis: an evaluation of propensity score approaches, G-computation, and doubly debiased machine learning

BACKGROUND

An external control arm is a cohort of control patients that are collected from data external to a single-arm trial. To provide an unbiased estimation of efficacy, the clinical profiles of patients from single and external arms should be aligned, typically using propensity score approaches. There are alternative approaches to infer efficacy based on comparisons between outcomes of single-arm patients and machine-learning predictions of control patient outcomes. These methods include G-computation and Doubly Debiased Machine Learning (DDML) and their evaluation for External Control Arms (ECA) analysis is insufficient.

METHODS

We consider both numerical simulations and a trial replication procedure to evaluate the different statistical approaches: propensity score matching, Inverse Probability of Treatment Weighting (IPTW), G-computation, and DDML. The replication study relies on five type 2 diabetes randomized clinical trials granted by the Yale University Open Data Access (YODA) project. From the pool of five trials, observational experiments are artificially built by replacing a control arm from one trial by an arm originating from another trial and containing similarly-treated patients.

RESULTS

Among the different statistical approaches, numerical simulations show that DDML has the smallest bias followed by G-computation. In terms of mean squared error, G-computation usually minimizes mean squared error. Compared to other methods, DDML has varying Mean Squared Error performances that improves with increasing sample sizes. For hypothesis testing, all methods control type I error and DDML is the most conservative. G-computation is the best method in terms of statistical power, and DDML has comparable power at [Formula: see text] but inferior ones for smaller sample sizes. The replication procedure also indicates that G-computation minimizes mean squared error whereas DDML has intermediate performances in between G-computation and propensity score approaches. The confidence intervals of G-computation are the narrowest whereas confidence intervals obtained with DDML are the widest for small sample sizes, which confirms its conservative nature.

CONCLUSIONS

For external control arm analyses, methods based on outcome prediction models can reduce estimation error and increase statistical power compared to propensity score approaches.

Indirect covariate balance and residual confounding: An applied comparison of propensity score matching and cardinality matching

PURPOSE

Propensity score matching (PSM) is subject to limitations associated with limited degrees of freedom and covariate overlap. Cardinality matching (CM), an optimization algorithm, overcomes these limitations by matching directly on the marginal distribution of covariates. This study compared the performance of PSM and CM.

METHODS

Comparative cohort study of new users of angiotensin-converting enzyme inhibitor (ACEI) and β-blocker monotherapy identified from a large U.S. administrative claims database. One-to-one matching was conducted through PSM using nearest-neighbor matching (caliper = 0.15) and CM permitting a maximum standardized mean difference (SMD) of 0, 0.01, 0.05, and 0.10 between comparison groups. Matching covariates included 37 patient demographic and clinical characteristics. Observed covariates included patient demographics, and all observed prior conditions, drug exposures, and procedures. Residual confounding was assessed based on the expected absolute systematic error of negative control outcome experiments. PSM and CM were compared in terms of post-match patient retention, matching and observed covariate balance, and residual confounding within a 10%, 1%, 0.25% and 0.125% sample group.

RESULTS

The eligible study population included 182 235 (ACEI: 129363; β-blocker: 56872) patients. CM achieved superior patient retention and matching covariate balance in all analyses. After PSM, 1.6% and 28.2% of matching covariates were imbalanced in the 10% and 0.125% sample groups, respectively. No significant difference in observed covariate balance was observed between matching techniques. CM permitting a maximum SMD <0.05 was associated with improved residual bias as compared to PSM.

CONCLUSION

We recommend CM with more stringent balance criteria as an alternative to PSM when matching on a set of clinically relevant covariates.

Comparative Effectiveness and Safety Between Apixaban, Dabigatran, Edoxaban, and Rivaroxaban Among Patients With Atrial Fibrillation : A Multinational Population-Based Cohort Study

BACKGROUND

Current guidelines recommend using direct oral anticoagulants (DOACs) over warfarin in patients with atrial fibrillation (AF), but head-to-head trial data do not exist to guide the choice of DOAC.

OBJECTIVE

To do a large-scale comparison between all DOACs (apixaban, dabigatran, edoxaban, and rivaroxaban) in routine clinical practice.

DESIGN

Multinational population-based cohort study.

SETTING

Five standardized electronic health care databases, which covered 221 million people in France, Germany, the United Kingdom, and the United States.

PARTICIPANTS

Patients who were newly diagnosed with AF from 2010 through 2019 and received a new DOAC prescription.

MEASUREMENTS

Database-specific hazard ratios (HRs) of ischemic stroke or systemic embolism, intracranial hemorrhage (ICH), gastrointestinal bleeding (GIB), and all-cause mortality between DOACs were estimated using a Cox regression model stratified by propensity score and pooled using a random-effects model.

RESULTS

A total of 527 226 new DOAC users met the inclusion criteria (apixaban, n = 281 320; dabigatran, n = 61 008; edoxaban, n = 12 722; and rivaroxaban, n = 172 176). Apixaban use was associated with lower risk for GIB than use of dabigatran (HR, 0.81 [95% CI, 0.70 to 0.94]), edoxaban (HR, 0.77 [CI, 0.66 to 0.91]), or rivaroxaban (HR, 0.72 [CI, 0.66 to 0.79]). No substantial differences were observed for other outcomes or DOAC-DOAC comparisons. The results were consistent for patients aged 80 years or older. Consistent associations between lower GIB risk and apixaban versus rivaroxaban were observed among patients receiving the standard dose (HR, 0.72 [CI, 0.64 to 0.82]), those receiving a reduced dose (HR, 0.68 [CI, 0.61 to 0.77]), and those with chronic kidney disease (HR, 0.68 [CI, 0.59 to 0.77]).

LIMITATION

Residual confounding is possible.

CONCLUSION

Among patients with AF, apixaban use was associated with lower risk for GIB and similar rates of ischemic stroke or systemic embolism, ICH, and all-cause mortality compared with dabigatran, edoxaban, and rivaroxaban. This finding was consistent for patients aged 80 years or older and those with chronic kidney disease, who are often underrepresented in clinical trials.

PRIMARY FUNDING SOURCE

None.

Comparative risk of thrombosis with thrombocytopenia syndrome or thromboembolic events associated with different covid-19 vaccines: international network cohort study from five European countries and the US

OBJECTIVE

To quantify the comparative risk of thrombosis with thrombocytopenia syndrome or thromboembolic events associated with use of adenovirus based covid-19 vaccines versus mRNA based covid-19 vaccines.

DESIGN

International network cohort study.

SETTING

Routinely collected health data from contributing datasets in France, Germany, the Netherlands, Spain, the UK, and the US.

PARTICIPANTS

Adults (age ≥18 years) registered at any contributing database and who received at least one dose of a covid-19 vaccine (ChAdOx1-S (Oxford-AstraZeneca), BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), or Ad26.COV2.S (Janssen/Johnson & Johnson)), from December 2020 to mid-2021.

MAIN OUTCOME MEASURES

Thrombosis with thrombocytopenia syndrome or venous or arterial thromboembolic events within the 28 days after covid-19 vaccination. Incidence rate ratios were estimated after propensity scores matching and were calibrated using negative control outcomes. Estimates specific to the database were pooled by use of random effects meta-analyses.

RESULTS

Overall, 1 332 719 of 3 829 822 first dose ChAdOx1-S recipients were matched to 2 124 339 of 2 149 679 BNT162b2 recipients from Germany and the UK. Additionally, 762 517 of 772 678 people receiving Ad26.COV2.S were matched to 2 851 976 of 7 606 693 receiving BNT162b2 in Germany, Spain, and the US. All 628 164 Ad26.COV2.S recipients from the US were matched to 2 230 157 of 3 923 371 mRNA-1273 recipients. A total of 862 thrombocytopenia events were observed in the matched first dose ChAdOx1-S recipients from Germany and the UK, and 520 events after a first dose of BNT162b2. Comparing ChAdOx1-S with a first dose of BNT162b2 revealed an increased risk of thrombocytopenia (pooled calibrated incidence rate ratio 1.33 (95% confidence interval 1.18 to 1.50) and calibrated incidence rate difference of 1.18 (0.57 to 1.8) per 1000 person years). Additionally, a pooled calibrated incidence rate ratio of 2.26 (0.93 to 5.52) for venous thrombosis with thrombocytopenia syndrome was seen with Ad26.COV2.S compared with BNT162b2.

CONCLUSIONS

In this multinational study, a pooled 30% increased risk of thrombocytopenia after a first dose of the ChAdOx1-S vaccine was observed, as was a trend towards an increased risk of venous thrombosis with thrombocytopenia syndrome after Ad26.COV2.S compared with BNT162b2. Although rare, the observed risks after adenovirus based vaccines should be considered when planning further immunisation campaigns and future vaccine development.

Long-term methylphenidate use for children and adolescents with attention deficit hyperactivity disorder and risk for depression, conduct disorder, and psychotic disorder: a nationwide longitudinal cohort study in South Korea

BACKGROUND

Methylphenidate (MPH) is the most frequently prescribed medication for the treatment of attention deficit hyperactivity disorder (ADHD). However, the safety of its long-term use remain unclear. In particular, real-world evidence of long-term MPH treatment regarding the risk of depression, conduct disorders, and psychotic disorders in children and adolescents is needed. This study aimed to compare the risks of depression, conduct disorder, and psychotic disorder between long- and short-term MPH treatments in children and adolescents.

METHODS

This population-based cohort study used a nationwide claims database of all patients with ADHD in South Korea. Patients aged less than 18 years who were prescribed MPH were included in the study. Long- and short-term MPH were defined as > 1 year, and < 1 year, respectively. Overall, the risk of developing depressive disorder, conduct disorder and oppositional defiant disorder (ODD), and psychotic disorder were investigated. A 1:2 propensity score matching was used to balance the cohorts, and the Cox proportional hazards model was used to evaluate the safety of MPH.

RESULTS

We identified 1309 long-term and 2199 short-term MPH users. Long-term MPH use was associated with a significantly lower risk of depressive (hazard ratio [HR], 0.70 [95% confidence interval [CI] 0.55-0.88]) and conduct disorders and ODD (HR, 0.52 [95% CI 0.38-0.73]) than short-term MPH use. Psychotic disorder was not significantly associated with long-term MPH use (hazard ratio [HR], 0.83 [95% confidence interval [CI] 0.52-1.32]).

CONCLUSIONS

Our findings suggest that long-term MPH use may be associated with a decreased risk of depression, conduct disorders and ODD. Moreover, the long-term use of MPH does not increase the risk of psychotic disorders. Long-term MPH administration may be considered as a favourable treatment strategy for children and adolescents with ADHD regarding depressive, conduct, and psychotic disorders.

International cohort study indicates no association between alpha-1 blockers and susceptibility to COVID-19 in benign prostatic hyperplasia patients

Purpose: Alpha-1 blockers, often used to treat benign prostatic hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storm release. The proposed treatment based on this hypothesis currently lacks support from reliable real-world evidence, however. We leverage an international network of large-scale healthcare databases to generate comprehensive evidence in a transparent and reproducible manner.

Methods: In this international cohort study, we deployed electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We assessed association between alpha-1 blocker use and risks of three COVID-19 outcomes—diagnosis, hospitalization, and hospitalization requiring intensive services—using a prevalent-user active-comparator design. We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We pooled database-specific estimates through random effects meta-analysis.

Results: Our study overall included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH medications. We observed no significant difference in their risks for any of the COVID-19 outcomes, with our meta-analytic HR estimates being 1.02 (95% CI: 0.92–1.13) for diagnosis, 1.00 (95% CI: 0.89–1.13) for hospitalization, and 1.15 (95% CI: 0.71–1.88) for hospitalization requiring intensive services.

Conclusion: We found no evidence of the hypothesized reduction in risks of the COVID-19 outcomes from the prevalent-use of alpha-1 blockers—further research is needed to identify effective therapies for this novel disease.

Adjusting for indirectly measured confounding using large-scale propensity score

Confounding remains one of the major challenges to causal inference with observational data. This problem is paramount in medicine, where we would like to answer causal questions from large observational datasets like electronic health records (EHRs) and administrative claims. Modern medical data typically contain tens of thousands of covariates. Such a large set carries hope that many of the confounders are directly measured, and further hope that others are indirectly measured through their correlation with measured covariates. How can we exploit these large sets of covariates for causal inference? To help answer this question, this paper examines the performance of the large-scale propensity score (LSPS) approach on causal analysis of medical data. We demonstrate that LSPS may adjust for indirectly measured confounders by including tens of thousands of covariates that may be correlated with them. We present conditions under which LSPS removes bias due to indirectly measured confounders, and we show that LSPS may avoid bias when inadvertently adjusting for variables (like colliders) that otherwise can induce bias. We demonstrate the performance of LSPS with both simulated medical data and real medical data.

COVID-19 vaccination effectiveness rates by week and sources of bias: a retrospective cohort study

OBJECTIVE

To examine COVID-19 vaccine effectiveness over six 7-day intervals after the first dose and assess underlying bias in observational data.

DESIGN AND SETTING

Retrospective cohort study using Columbia University Irving Medical Center data linked to state and city immunisation registries.

OUTCOMES AND MEASURES

We used large-scale propensity score matching with up to 54 987 covariates, fitted Cox proportional hazards models and constructed Kaplan-Meier plots for two main outcomes (COVID-19 infection and COVID-19-associated hospitalisation). We conducted manual chart review of cases in week 1 in both groups along with a set of secondary analyses for other index date, outcome and population choices.

RESULTS

The study included 179 666 patients. We observed increasing effectiveness after the first dose of mRNA vaccines with week 6 effectiveness approximating 84% (95% CI 72% to 91%) for COVID-19 infection and 86% (95% CI 69% to 95%) for COVID-19-associated hospitalisation. When analysing unexpectedly high effectiveness in week 1, chart review revealed that vaccinated patients are less likely to seek care after vaccination and are more likely to be diagnosed with COVID-19 during the encounters for other conditions. Secondary analyses highlighted potential outcome misclassification for International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis, the influence of excluding patients with prior COVID-19 infection and anchoring in the unexposed group. Long-term vaccine effectiveness in fully vaccinated patients matched the results of the randomised trials.

CONCLUSIONS

For vaccine effectiveness studies, observational data need to be scrutinised to ensure compared groups exhibit similar health-seeking behaviour and are equally likely to be captured in the data. While we found that studies may be capable of accurately estimating long-term effectiveness despite bias in early weeks, the early week results should be reported in every study so that we may gain a better understanding of the biases. Given the difference in temporal trends of vaccine exposure and patients' baseline characteristics, indirect comparison of vaccines may produce biased results.

Decreasing Incidence of Gastric Cancer with Increasing Time after Helicobacter pylori Treatment: A Nationwide Population-Based Cohort Study

Background: Treatment of Helicobacter pylori (HP) has been shown to reduce the risk of gastric cancer (GC) development. However, previous studies have focused on patients at high risk of GC. This study aimed to assess the effect of HP treatment on the incidence of GC in the general population. Materials and Methods: Medical records were obtained from the Common Data Model-converted sample Cohort of the National Health Insurance Service of Korea (NHIS-CDM). The target cohort included those who had been prescribed HP treatment and the comparator cohort included those who had not. The association between HP treatment and the risk of GC development was assessed using the Cox proportional hazard model. The incidences of GC according to the period after HP treatment in different age groups were analyzed using proportional trend tests. Results: After large-scale 1:4 propensity score matching, 2735 and 5328 individuals were included in the target and comparator cohorts, respectively. During the median follow-up of 6.5 years, the GC incidence was lower in the HP treatment cohort than in the comparator cohort, but this was statistically insignificant (hazard ratio [HR]: 0.76; 95% confidence interval [CI]: 0.50−1.13; p-value = 0.19). This trend was also observed among the older age (≥65 years, HR: 0.87; 95% CI: 0.44−1.68; p-value = 0.69) and male cohorts (HR: 0.82; 95% CI: 0.51−1.27; p-value = 0.38). Among 58,684 individuals who were treated for HP from the whole NHIS-CDM cohort, the incidence of GC consistently decreased over time and showed a marked decrease with increasing age (p for trend < 0.05). Conclusions: In all age groups of the general population, HP treatment could be recommended to reduce the risk of GC.

Long-term proton pump inhibitor use and risk of osteoporosis and hip fractures: A nationwide population-based and multicenter cohort study using a common data model

BACKGROUND AND AIM

Association between protonpump inhibitors (PPIs) and osteoporosis, hip fractures has not been fully elucidated. We aimed to evaluate the relationship between PPIs use and the risk of osteoporosis and hip fractures in the databases converted to a common data model (CDM) and to compare the results across the databases.

METHODS

This was a population-based, propensity-matched, retrospective cohort study that included patients aged ≥ 50 years who were prescribed with PPIs for over 180 days. We compared the incidence of osteoporosis and hip fractures between new PPI user and new user of other drugs using the Cox proportional hazards model and performed meta-analysis in the electronic health record (EHR) databases.

RESULTS

In the Korean National Health Insurance Service (NHIS)-CDM database, long-term PPI users had greater risk of osteoporosis [PPIs vs non-PPIs groups, 28.42/1000 person-years vs 19.29/1000 person-years; hazard ratio (HR), 1.62; 95% confidence interval (CI), 1.22-2.15; P = 0.001]. The meta-analytic results of six EHR databases also showed similar result (pooled HR, 1.57; 95% CI, 1.28-1.92). In the analysis of hip fracture, PPI use was not significantly associated with a hip fracture in the NHIS-CDM database (PPI vs non-PPI groups, 3.09/1000 person-years vs 2.26/1000 person-years; HR, 1.45; 95% CI, 0.74-2.80; P = 0.27). However, in the meta-analysis of four EHR databases, the risk of hip fractures was higher in PPI users (pooled HR, 1.82; 95% CI, 1.04-3.19).

CONCLUSIONS

Long-term PPI was significantly associated with osteoporosis; however, the results of hip fractures were inconsistent. Further study based on better data quality may be needed.

Hip Fracture Risk After Treatment with Tramadol or Codeine: An Observational Study

INTRODUCTION

Hip fractures among older people are a major public health issue, which can impact quality of life and increase mortality within the year after they occur. A recent observational study found an increased risk of hip fracture in subjects who were new users of tramadol compared with codeine. These drugs have somewhat different indications. Tramadol is indicated for moderate to severe pain and can be used for an extended period; codeine is indicated for mild to moderate pain and cough suppression.

OBJECTIVE

In this observational study, we compared the risk of hip fracture in new users of tramadol or codeine, using multiple databases and analytical methods.

METHODS

Using data from the Clinical Practice Research Datalink and three US claims databases, we compared the risk of hip fracture after exposure to tramadol or codeine in subjects aged 50-89 years. To ensure comparability, large-scale propensity scores were used to adjust for confounding.

RESULTS

We observed a calibrated hazard ratio of 1.10 (95% calibrated confidence interval 0.99-1.21) in the Clinical Practice Research Datalink database, and a pooled estimate across the US databases yielded a calibrated hazard ratio of 1.06 (95% calibrated confidence interval 0.97-1.16).

CONCLUSIONS

Our results did not demonstrate a statistically significant difference between subjects treated for pain with tramadol compared with codeine for the outcome of hip fracture risk.

Vaccine Safety Surveillance Using Routinely Collected Healthcare Data-An Empirical Evaluation of Epidemiological Designs

Background: Routinely collected healthcare data such as administrative claims and electronic health records (EHR) can complement clinical trials and spontaneous reports to detect previously unknown risks of vaccines, but uncertainty remains about the behavior of alternative epidemiologic designs to detect and declare a true risk early. Methods: Using three claims and one EHR database, we evaluate several variants of the case-control, comparative cohort, historical comparator, and self-controlled designs against historical vaccinations using real negative control outcomes (outcomes with no evidence to suggest that they could be caused by the vaccines) and simulated positive control outcomes. Results: Most methods show large type 1 error, often identifying false positive signals. The cohort method appears either positively or negatively biased, depending on the choice of comparator index date. Empirical calibration using effect-size estimates for negative control outcomes can bring type 1 error closer to nominal, often at the cost of increasing type 2 error. After calibration, the self-controlled case series (SCCS) design most rapidly detects small true effect sizes, while the historical comparator performs well for strong effects. Conclusion: When applying any method for vaccine safety surveillance we recommend considering the potential for systematic error, especially due to confounding, which for many designs appears to be substantial. Adjusting for age and sex alone is likely not sufficient to address differences between vaccinated and unvaccinated, and for the cohort method the choice of index date is important for the comparability of the groups. Analysis of negative control outcomes allows both quantification of the systematic error and, if desired, subsequent empirical calibration to restore type 1 error to its nominal value. In order to detect weaker signals, one may have to accept a higher type 1 error.

Heart Failure and Stroke Risks in Users of Liothyronine With or Without Levothyroxine Compared with Levothyroxine Alone: A Propensity Score-Matched Analysis

Background: Combination therapy with liothyronine (LT3) and levothyroxine (LT4) is used in patients with persistent symptoms, despite being administered an adequate dose of LT4. LT3 may also be used in some thyroid cancer patients preparing for radioactive iodine therapy. However, there is a controversy regarding the safety of LT3 use, and there has been no definite evidence of long-term safety of LT3 therapy in Asian populations. The aim of this study was to examine the long-term safety of LT3 therapy using the Common Data Model (CDM). Methods: We conducted a retrospective multicenter study across four hospital databases encoded in the Observational Medical Outcomes Partnership (OMOP) CDM. LT3 users were defined as those who received an LT3 prescription for at least 90 days (with or without LT4), and their safety outcomes were compared with those in LT4-only users after 1:4 propensity score matching. Safety outcomes included the incidences of osteoporosis, cardiovascular disease, cancer, anxiety disorder, and mood disorder. Results: We identified 1434 LT3 users and 3908 LT4-only users. There was a statistically significant difference in the incidence rate of safety outcomes between LT3 users and LT4-only users. The risks of heart failure (incidence rate ratio [IRR] = 1.664, 95% confidence interval [95% CI] 1.002-2.764, p = 0.049) and stroke (IRR = 1.757, CI 1.073-2.877, p = 0.025) were higher in LT3 users than in LT4-only users. When subgroup analysis was performed according to the presence/absence of thyroid cancer history and duration of thyroid hormone replacement, the risk of heart failure was higher in LT3 users with a history of thyroid cancer and those who underwent ≥52 weeks of LT3 therapy. In addition, the risk of stroke was higher in LT3 users without thyroid cancer history and those who underwent ≥52 weeks of LT3 therapy. Conclusions: The use of LT3 was associated with increased incidence of heart failure and stroke in patients with a longer duration of LT3 use and history of thyroid cancer. Therefore, clinicians should consider the risk of heart failure and stroke in thyroid cancer patients with long-term use of LT3. These findings require confirmation in other populations.

Synthetic Negative Controls: Using Simulation to Screen Large-scale Propensity Score Analyses

The propensity score has become a standard tool to control for large numbers of variables in healthcare database studies. However, little has been written on the challenge of comparing large-scale propensity score analyses that use different methods for confounder selection and adjustment. In these settings, balance diagnostics are useful but do not inform researchers on which variables balance should be assessed or quantify the impact of residual covariate imbalance on bias. Here, we propose a framework to supplement balance diagnostics when comparing large-scale propensity score analyses. Instead of focusing on results from any single analysis, we suggest conducting and reporting results for many analytic choices and using both balance diagnostics and synthetically generated control studies to screen analyses that show signals of bias caused by measured confounding. To generate synthetic datasets, the framework does not require simulating the outcome-generating process. In healthcare database studies, outcome events are often rare, making it difficult to identify and model all predictors of the outcome to simulate a confounding structure closely resembling the given study. Therefore, the framework uses a model for treatment assignment to divide the comparator population into pseudo-treatment groups where covariate differences resemble those in the study cohort. The partially simulated datasets have a confounding structure approximating the study population under the null (synthetic negative control studies). The framework is used to screen analyses that likely violate partial exchangeability due to lack of control for measured confounding. We illustrate the framework using simulations and an empirical example.

Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: an overview of the current literature

Controlling for large numbers of variables that collectively serve as 'proxies' for unmeasured factors can often improve confounding control in pharmacoepidemiologic studies utilizing administrative healthcare databases. There is a growing body of evidence showing that data-driven machine learning algorithms for high-dimensional proxy confounder adjustment can supplement investigator-specified variables to improve confounding control compared to adjustment based on investigator-specified variables alone. Consequently, there has been a recent focus on the development of data-driven methods for high-dimensional proxy confounder adjustment. In this paper, we discuss the considerations underpinning three areas for data-driven high-dimensional proxy confounder adjustment: 1) feature generation-transforming raw data into covariates (or features) to be used for proxy adjustment; 2) covariate prioritization, selection and adjustment; and 3) diagnostic assessment. We survey current approaches and recent advancements within each area, including the most widely used approach to proxy confounder adjustment in healthcare database studies (the high-dimensional propensity score or hdPS). We also discuss limitations of the hdPS and outline recent advancements that incorporate the principles of proxy adjustment with machine learning extensions to improve performance. We further discuss challenges and avenues of future development within each area. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE). This article is protected by copyright. All rights reserved.

Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies

INTRODUCTION

Therapeutic options for type 2 diabetes mellitus (T2DM) have expanded over the last decade with the emergence of cardioprotective novel agents, but without such data for older drugs, leaving a critical gap in our understanding of the relative effects of T2DM agents on cardiovascular risk.

METHODS AND ANALYSIS

The large-scale evidence generations across a network of databases for T2DM (LEGEND-T2DM) initiative is a series of systematic, large-scale, multinational, real-world comparative cardiovascular effectiveness and safety studies of all four major second-line anti-hyperglycaemic agents, including sodium-glucose co-transporter-2 inhibitor, glucagon-like peptide-1 receptor agonist, dipeptidyl peptidase-4 inhibitor and sulfonylureas. LEGEND-T2DM will leverage the Observational Health Data Sciences and Informatics (OHDSI) community that provides access to a global network of administrative claims and electronic health record data sources, representing 190 million patients in the USA and about 50 million internationally. LEGEND-T2DM will identify all adult, patients with T2DM who newly initiate a traditionally second-line T2DM agent. Using an active comparator, new-user cohort design, LEGEND-T2DM will execute all pairwise class-versus-class and drug-versus-drug comparisons in each data source, producing extensive study diagnostics that assess reliability and generalisability through cohort balance and equipoise to examine the relative risk of cardiovascular and safety outcomes. The primary cardiovascular outcomes include a composite of major adverse cardiovascular events and a series of safety outcomes. The study will pursue data-driven, large-scale propensity adjustment for measured confounding, a large set of negative control outcome experiments to address unmeasured and systematic bias.

ETHICS AND DISSEMINATION

The study ensures data safety through a federated analytic approach and follows research best practices, including prespecification and full disclosure of results. LEGEND-T2DM is dedicated to open science and transparency and will publicly share all analytic code from reproducible cohort definitions through turn-key software, enabling other research groups to leverage our methods, data and results to verify and extend our findings.

Red blood cell transfusions post diagnosis of necrotizing enterocolitis and the deterioration of necrotizing enterocolitis in full-term and near-term infants: a propensity score adjustment retrospective cohort study

Background

Necrotizing enterocolitis (NEC) is one of serious gastrointestinal inflammatory diseases in newborn infants, with a high morbidity and mortality. Red blood cell transfusion (RBCT) plays a controversial and doubtful role in the treatment of NEC. In present study, we aim to analyze the association between RBCT and the deterioration of NEC.

Methods

This was a retrospective cohort study of near-term and full-term infants with a confirmed diagnosis of Bell’s stage II NEC between Jan 1, 2010 and Jan 31, 2020. The maternal and infant baseline characteristics, treatment information and laboratory test for each case were collected. The eligible subjects were divided into two groups based on receiving RBCT post NEC diagnosis or not. The propensity score was used to eliminate potential bias and baseline differences. A multivariate logistic regression model was used to adjust the propensity score and calculate the odds ratio (OR) and 95% confidential interval (CI) of RBCT for the deterioration of NEC.

Results

A total of 242 infants were included in this study, 60 infants had a history of RBCT post NEC diagnosis, and 40 infants deteriorated from Bell’s stage II to stage III. By adjusting the propensity score, RBCT post NEC diagnosis was associated with an increased risk for NEC deteriorating from stage II to III (adjusted OR 6.06, 95%CI 2.94–12.50, P = 0.000).

Conclusions

NEC infants who required RBCT post NEC diagnosis were more likely to deteriorate from stage II to III in full-term and near-term infants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03276-4.

Incidence and Survival Outcomes of Colorectal Cancer in Long-Term Metformin Users with Diabetes: A Population-Based Cohort Study Using a Common Data Model

Background and aims: Previous studies have reported that metformin use in patients with diabetes mellitus may reduce the risk of colorectal cancer (CRC) incidence and prognosis; however, the evidence is not definite. This population-based cohort study aimed to investigate whether metformin reduces the risk of CRC incidence and prognosis in patients with diabetes mellitus using a common data model of the Korean National Health Insurance Service database from 2002 to 2013. Methods: Patients who used metformin for at least 6 months were defined as metformin users. The primary outcome was CRC incidence, and the secondary outcomes were the all-cause and CRC-specific mortality. Cox proportional hazard model was performed and large-scaled propensity score matching was used to control for potential confounding factors. Results: During the follow-up period of 81,738 person-years, the incidence rates (per 1000 person-years) of CRC were 5.18 and 8.12 in metformin users and non-users, respectively (p = 0.001). In the propensity score matched cohort, the risk of CRC incidence in metformin users was significantly lower than in non-users (hazard ratio (HR), 0.58; 95% CI (confidence interval), 0.47–0.71). In the sensitivity analysis, the lag period extending to 1 year showed similar results (HR: 0.63, 95% CI: 0.51–0.79). The all-cause mortality was significantly lower in metformin users than in non-users (HR: 0.71, 95% CI: 0.64–0.78); CRC-related mortality was also lower among metformin users. However, there was no significant difference (HR: 0.55, 95% CI: 0.26–1.08). Conclusions: Metformin use was associated with a reduced risk of CRC incidence and improved overall survival.