Abstract: Methodological biases are common in observational studies evaluating treatment effectiveness. The objective of this study is to emulate a target trial in a competing risks setting using hospital-based observational data. We extend established methodology accounting for immortal time bias and time-fixed confounding biases to a setting where no survival information beyond hospital discharge is available: a condition common to coronavirus disease 2019 (COVID-19) research data. This exemplary study includes a cohort of 618 hospitalized patients with COVID-19. We describe methodological opportunities and challenges that cannot be overcome applying traditional statistical methods. We demonstrate the practical implementation of this trial emulation approach via clone-censor-weight techniques. We undertake a competing risk analysis, reporting the cause-specific cumulative hazards and cumulative incidence probabilities. Our analysis demonstrates that a target trial emulation framework can be extended to account for competing risks in COVID-19 hospital studies. In our analysis, we avoid immortal time bias, time-fixed confounding bias, and competing risks bias simultaneously. Choosing the length of the grace period is justified from a clinical perspective and has an important advantage in ensuring reliable results. This extended trial emulation with the competing risk analysis enables an unbiased estimation of treatment effects, along with the ability to interpret the effectiveness of treatment on all clinically important outcomes

Abstract: By definition, in-hospital patient data are restricted to the time between hospital admission and discharge (alive or dead). For hospitalised cases of COVID-19, a number of events during hospitalization are of interest regarding the influence of risk factors on the likelihood of experiencing these events. The same is true for predicting times from hospital admission of COVID-19 patients to intensive care or from start of ventilation (invasive or non-invasive) to extubation. This logical restriction of the data to the period of hospitalisation is associated with a substantial risk that inappropriate methods are used for analysis. Here, we briefly discuss the most common types of bias which can occur when analysing in-hospital COVID-19 data

Abstract: Purpose: When studying nosocomial infections, resource-efficient sampling designs such as nested case-control, case-cohort, and point prevalence studies are preferred. However, standard analyses of these study designs can introduce selection bias, especially when interested in absolute rates and risks. Moreover, nosocomial infection studies are often subject to competing risks. We aim to demonstrate in this tutorial how to address these challenges for all three study designs using simple weighting techniques. Patients and Methods: We discuss the study designs and explain how inverse probability weights (IPW) are applied to obtain unbiased hazard ratios (HR), odds ratios and cumulative incidences. We illustrate these methods in a multi-state framework using a dataset from a nosocomial infections study (n = 2286) in Moscow, Russia. Results: Including IPW in the analysis corrects the unweighted naïve analyses and enables the estimation of absolute risks. Resulting estimates are close to the full cohort estimates using substantially smaller numbers of patients. Conclusion: IPW is a powerful tool to account for the unequal selection of controls in case-cohort, nested case-control and point prevalence studies. Findings can be generalized to the full population and absolute risks can be estimated. When applied to a multi-state model, competing risks are also taken into account

Abstract: Objectives To analyse the adherence and impact of quality-of-care indicators (QCIs) in the management of Staphylococcus aureus bloodstream infection in a prospective and multicentre cohort. Methods Analysis of the prospective, multicentre international S. Aureus Collaboration cohort of S. Aureus bloodstream infection cases observed between January 2013 and April 2015. Multivariable analysis was performed to evaluate the impact of adherence to QCIs on 90-day mortality. Results A total of 1784 cases were included. Overall, 90-day mortality was 29.9% and mean follow-up period was 118 days. Adherence was 67% (n = 1180/1762) for follow-up blood cultures, 31% (n = 416/1342) for early focus control, 77.6% (n = 546/704) for performance of echocardiography, 75.5% (n = 1348/1784) for adequacy of targeted antimicrobial therapy, 88.6% (n = 851/960) for adequacy of treatment duration in non-complicated bloodstream infections and 61.2% (n = 366/598) in complicated bloodstream infections. Full bundle adherence was 18.4% (n = 328/1784). After controlling for immortal time bias and potential confounders, focus control (adjusted hazard ratio = 0.76; 95% CI, 0.59-0.99; p 0.038) and adequate targeted antimicrobial therapy (adjusted hazard ratio = 0.75; 95% CI, 0.61-0.91; p 0.004) were associated with low 90-day mortality. Discussion Adherence to QCIs in S. Aureus bloodstream infection did not reach expected rates. Apart from the benefits of application as a bundle, focus control and adequate targeted therapy were independently associated with low mortality