Changes in opioid prescribing during the COVID-19 pandemic in England: an interrupted time-series analysis in the OpenSAFELY-TTP cohort.

Background: The COVID-19 pandemic disrupted health-care delivery, including difficulty accessing in-person care, which could have increased the need for strong pharmacological pain relief. Due to the risks associated with overprescribing of opioids, especially to vulnerable populations, we aimed to quantify changes to measures during the COVID-19 pandemic, overall, and by key subgroups.

Methods: For this interrupted time-series analysis study conducted in England, with National Health Service England approval, we used routine clinical data from more than 20 million general practice adult patients in OpenSAFELY-TPP, which is a a secure software platform for analysis of electronic health records.

Rotational thromboelastometry as a biomarker for mortality - The Maastricht Intensive Care COVID cohort.

Background: Patients with severe coronavirus disease 2019 (COVID-19) present with persisting hypercoagulability, hypofibrinolysis and prolonged clot initiation as measured with viscoelastic assays. The objective of this study was to investigate the trajectories of traditional assays of hemostasis, routine and tissue plasminogen activator (tPA) rotational thromboelastometry (ROTEM) in COVID-19 patients and to study their association with mortality.

Methods: Patients enrolled within the Maastricht Intensive Care COVID (MaastrICCht) cohort were included.

Comparative effectiveness of nirmatrelvir/ritonavir versus sotrovimab and molnupiravir for preventing severe COVID-19 outcomes in non-hospitalised high-risk patients during Omicron waves: observational cohort study using the OpenSAFELY platform.

Background: Timely evidence of the comparative effectiveness between COVID-19 therapies in real-world settings is needed to inform clinical care. This study aimed to compare the effectiveness of nirmatrelvir/ritonavir versus sotrovimab and molnupiravir in preventing severe COVID-19 outcomes in non-hospitalised high-risk COVID-19 adult patients during Omicron waves.

Methods: With the approval of NHS England, we conducted a real-world cohort study using the OpenSAFELY-TPP platform.

Comparative effectiveness of sotrovimab and molnupiravir for preventing severe COVID-19 outcomes in patients on kidney replacement therapy: observational study using the OpenSAFELY-UKRR and SRR databases.

Background: Due to limited inclusion of patients on kidney replacement therapy (KRT) in clinical trials, the effectiveness of coronavirus disease 2019 (COVID-19) therapies in this population remains unclear. We sought to address this by comparing the effectiveness of sotrovimab against molnupiravir, two commonly used treatments for non-hospitalised KRT patients with COVID-19 in the UK.

Methods: With the approval of National Health Service England, we used routine clinical data from 24 million patients in England within the OpenSAFELY-TPP platform linked to the UK Renal Registry (UKRR) to identify patients on KRT.

Factors associated with COVID-19 vaccine uptake in people with kidney disease: an OpenSAFELY cohort study.

Objective: To characterise factors associated with COVID-19 vaccine uptake among people with kidney disease in England.

Design: Retrospective cohort study using the OpenSAFELY-TPP platform, performed with the approval of NHS England.

Setting: Individual-level routine clinical data from 24 million people across GPs in England using TPP software.

Comparative effectiveness of sotrovimab and molnupiravir for prevention of severe covid-19 outcomes in patients in the community: observational cohort study with the OpenSAFELY platform.

Objective: To compare the effectiveness of sotrovimab (a neutralising monoclonal antibody) with molnupiravir (an antiviral) in preventing severe outcomes of covid-19 in adult patients infected with SARS-CoV-2 in the community and at high risk of severe outcomes from covid-19.

Design: Observational cohort study with the OpenSAFELY platform.

Setting: With the approval of NHS England, a real world cohort study was conducted with the OpenSAFELY-TPP platform (a secure, transparent, open source software platform for analysis of NHS electronic health records), and patient level electronic health record data were obtained from 24 million people registered with a general practice in England that uses TPP software.

Estimating incidence of venous thromboembolism in COVID-19: Methodological considerations.

Background: Coagulation abnormalities and coagulopathy are recognized as consequences of severe acute respiratory syndrome coronavirus 2 infection and the resulting coronavirus disease 2019 (COVID-19). Specifically, venous thromboembolism (VTE) has been reported as a frequent complication. By May 27, 2021, at least 93 original studies and 25 meta-analyses investigating VTE incidence in patients with COVID-19 had been published, showing large heterogeneity in reported VTE incidence ranging from 0% to 85%.

Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation.

Objective: Sensitivity analysis for random measurement error can be applied in the absence of validation data by means of regression calibration and simulation-extrapolation. These have not been compared for this purpose.

Study Design And Setting: A simulation study was conducted comparing the performance of regression calibration and simulation-extrapolation for linear and logistic regression.

Mecor: An R package for measurement error correction in linear regression models with a continuous outcome.

Measurement error in a covariate or the outcome of regression models is common, but is often ignored, even though measurement error can lead to substantial bias in the estimated covariate-outcome association. While several texts on measurement error correction methods are available, these methods remain seldomly applied. To improve the use of measurement error correction methodology, we developed mecor, an R package that implements measurement error correction methods for regression models with a continuous outcome.

Sampling Strategies for Internal Validation Samples for Exposure Measurement-Error Correction: A Study of Visceral Adipose Tissue Measures Replaced by Waist Circumference Measures.

Statistical correction for measurement error in epidemiologic studies is possible, provided that information about the measurement error model and its parameters are available. Such information is commonly obtained from a randomly sampled internal validation sample. It is however unknown whether randomly sampling the internal validation sample is the optimal sampling strategy.

Approaches to addressing missing values, measurement error, and confounding in epidemiologic studies.

Objectives: Epidemiologic studies often suffer from incomplete data, measurement error (or misclassification), and confounding. Each of these can cause bias and imprecision in estimates of exposure-outcome relations. We describe and compare statistical approaches that aim to control all three sources of bias simultaneously.

Quantitative Bias Analysis for a Misclassified Confounder: A Comparison Between Marginal Structural Models and Conditional Models for Point Treatments.

Observational data are increasingly used with the aim of estimating causal effects of treatments, through careful control for confounding. Marginal structural models estimated using inverse probability weighting (MSMs-IPW), like other methods to control for confounding, assume that confounding variables are measured without error. The average treatment effect in an MSM-IPW may however be biased when a confounding variable is error prone.

[Big, bigger, biggest; big data in medical research].

Big data is characterised not only by the size of data files, but also by the diversity of data sources and continuity in data collection. Technological developments make it possible to store and analyse ever larger and more complex data files. Unlike more conventional research data, big data are often collected without explicit research questions in mind, and analytical techniques are used to find patterns in the data or to generate hypotheses.