Guidance to reinforce the credibility of health care database studies and ensure their appropriate impact

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

PURPOSE

There is increasing recognition of the importance of transparency and reproducibility in scientific research. This study aimed to quantify the extent to which programming code is publicly shared in pharmacoepidemiology, and to develop a set of recommendations on this topic.

METHODS

We conducted a literature review identifying all studies published in Pharmacoepidemiology and Drug Safety (PDS) between 2017 and 2022. Data were extracted on the frequency and types of programming code shared, and other key open science practices (clinical codelist sharing, data sharing, study preregistration, and stated use of reporting guidelines and preprinting). We developed six recommendations for investigators who choose to share code and gathered feedback from members of the International Society for Pharmacoepidemiology (ISPE).

RESULTS

Programming code sharing by articles published in PDS ranged from 1.8% in 2017 to 9.5% in 2022. It was more prevalent among articles with a methodological focus, simulation studies, and papers which also shared record-level data.

CONCLUSION

Programming code sharing is rare but increasing in pharmacoepidemiology studies published in PDS. We recommend improved reporting of whether code is shared and how available code can be accessed. When sharing programming code, we recommend the use of permanent digital identifiers, appropriate licenses, and, where possible, adherence to good software practices around the provision of metadata and documentation, computational reproducibility, and data privacy.

Exploring prior diseases associated with incident late-onset Alzheimer's disease dementia

Studies have identified prior conditions associated with late-onset Alzheimer's disease dementia (LOAD), but all prior diseases have rarely been screened simultaneously in the literature. Our objective in the present study was to identify prior conditions associated with LOAD and construct pathways for them. We conducted a population-based matched case-control study based on data collected in the National Health Insurance Research database of Taiwan and the Catastrophic Illness Certificate database for the years 1997-2013. Prior diseases definitions were based on the first three digits of the codes listed in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). Inclusion criteria required that each ICD-code existed for at least 1 year and incurred at least 2 outpatient visits or inpatient diagnosis. The case group comprised 4,600 patients newly diagnosed with LOAD in 2007-2013. The LOAD patients were matched by sex and age to obtain 4,600 controls. Using stepwise multivariate logistic regression analysis, diseases were screened for 1, 2 …, 9 years prior to the first diagnosis of LOAD. Path analysis was used to construct pathways between prior diseases and LOAD. Our results revealed that the following conditions were positively associated with the incidence of LOAD: anxiety (ICD-code 300), functional digestive disorder (ICD code 564), psychopathology-specific symptoms (ICD-code 307), disorders of the vestibular system (ICD-code 386), concussion (ICD-code 850), disorders of the urethra and urinary tract (ICD-code 599), disorders of refraction and accommodation (ICD-code 367), and hearing loss (ICD-code 389). A number of the prior diseases have previously been described in the literature in a manner identical to that in the present study. Our study supports the assertion that mental, hearing, vestibular system, and functional digestive disorders may play an important role in the pathogenesis of LOAD.

Observational research on sodium glucose co-transporter-2 inhibitors: A real breakthrough?

Sodium glucose co-transporter-2 inhibitors have attracted the interest of the scientific community following the results from dedicated cardiovascular outcome trials, which demonstrated remarkable reduction in all-cause mortality and other cardiovascular (CV) endpoints with empagliflozin and canagliflozin. These impressive results raised further expectations on real world data from large observational cohort studies. They were designed to address the possible existence of a class effect, and the uncertainty on whether this benefit can be extended from secondary to primary CV prevention of patients with type 2 diabetes. In this review, we collated data from existing observational studies (including the celebrated CVD-REAL cohorts) and critically appraised results and methodological issues with the aim of providing clinical insight, including unsettled aspects, and proposing a research agenda for future investigations.

Lessons from meta-analyses of randomized clinical trials for analysis of distributed networks of observational databases

Networks of constellations of longitudinal observational databases, often electronic medical records or transactional insurance claims or both, are increasingly being used for studying the effects of medicinal products in real-world use. Such databases are frequently configured as distributed networks. That is, patient-level data are kept behind firewalls and not communicated outside of the data vendor other than in aggregate form. Instead, data are standardized across the network, and queries of the network are executed locally by data partners, and summary results provided to a central research partner(s) for amalgamation, aggregation, and summarization. Such networks can be huge covering years of data on upwards of 100 million patients. Examples of such networks include the FDA Sentinel Network, ASPEN, CNODES, and EU-ADR. As this is a new emerging field, we note in this paper the conceptual similarities and differences between the analysis of distributed networks and the now well-established field of meta-analysis of randomized clinical trials (RCTs). We recommend, wherever appropriate, to apply learnings from meta-analysis to help guide the development of distributed network analyses of longitudinal observational databases.