Flexible parametric approach to classical measurement error variance estimation without auxiliary data.

Measurement error in the continuous covariates of a model generally yields bias in the estimators. It is a frequent problem in practice, and many correction procedures have been developed for different classes of models. However, in most cases, some information about the measurement error distribution is required. When neither validation nor auxiliary data (e.g., replicated measurements) are available, this specification turns out to be tricky. In this article, we develop a flexible likelihood-based procedure to estimate the variance of classical additive error of Gaussian distribution, without additional information, when the covariate has compact support. The performance of this estimator is investigated both in an asymptotic way and through finite sample simulations. The usefulness of the obtained estimator when using the simulation extrapolation (SIMEX) algorithm, a widely used correction method, is then analyzed in the Cox proportional hazards model through other simulations. Finally, the whole procedure is illustrated on real data.

One-year COVID-19 outcomes on the oncology care patient pathway: Results of a French descriptive, cross-sectional comprehensive study (ONCOCARE-COV).

BACKGROUND: The COVID-19 pandemic led to a widely documented disruption in cancer care pathway. Since a resurgence of the pandemic was expected after the first lockdown in France, the global impact on the cancer care pathway over the year 2020 was investigated. AIMS: This study aimed to describe the changes in the oncology care pathway for cancer screening, diagnosis, assessment, diagnosis annoucement procedure and treatment over a one-year period. MATERIALS & METHODS: The ONCOCARE-COV study was a comprehensive, retrospective, descriptive, and cross-sectional study comparing the years 2019 and 2020. All key indicators along the cancer care pathway assessing the oncological activity over four periods were described. This study was set in a high-volume, public, single tertiary care center divided in two complementary sites (Reims University Hospital and Godinot Cancer Institute, Reims, France) which was located in a high COVID-19 incidence area during both peaks of the outbreak. RESULTS: A total of 26,566 patient's files were active during the year 2020. Breast screening (-19.5%), announcement dedicated consultations (-9.2%), Intravenous and Hyperthermic Intraoperative Intraperitoneal Chemotherapy (HIPECs) (-25%), and oncogeriatric evaluations (-14.8%) were heavily disrupted in regard to 2020 activity. We identified a clear second outbreak wave impact on medical announcement procedures (October, -14.4%), radiotherapy sessions (October, -16%), number of new health record discussed in multidisciplinary tumor board meeting (November, -14.6%) and HIPECs (November, -100%). Moreover, 2020 cancer care activity stagnated compared to 2019. DISCUSSION: The oncological care pathway was heavily disrupted during the first and second peaks of the COVID-19 outbreak. Between lockdowns, we observed a remarkable but non-compensatory recovery as well as a lesser impact from the pandemic resurgence. However, in absence of an increase in activity, a backlog persisted. CONCLUSION: Public health efforts are needed to deal with the consequences of the COVID-19 pandemic on the oncology care pathway.

Integrative respiratory follow-up of severe COVID-19 reveals common functional and lung imaging sequelae.

BACKGROUND: COVID-19 pandemic resulted in an unprecedented number of hospitalizations in general wards and intensive care units (ICU). Severe and critical COVID-19 patients suffer from extensive pneumonia; therefore, long-term respiratory sequelae may be expected. RESEARCH QUESTION: We conducted a cohort study to determine respiratory sequelae in patients with severe and critical COVID-19. We aimed at evaluating the proportion of patients with persisting respiratory symptoms and/or abnormalities in pulmonary function tests (PFT) or in lung imaging. STUDY DESIGN: and methods: This is a single center cohort study including COVID-19 survivors who underwent a three-month follow-up with clinical evaluation, PFT and lung high-resolution computed tomography (HRCT). All clinical, functional, and radiological data were centrally reviewed. Multiple linear regression analysis was performed to identify factors associated with residual lesions on HRCT. RESULTS: Full clinical evaluation, PFT and lung HRCT were available for central review in 126, 122 and 107 patients, respectively. At follow-up, 25% of patients complained from dyspnea and 35% from fatigue, lung diffusion capacity (DLCO) was decreased in 45%, 17% had HRCT abnormalities affecting more than 5% of their lung parenchyma while signs of fibrosis were found in 21%. In multiple linear regression model, number of days in ICU were related to the extent of persisting lesions on HRCT, while intubation was associated with signs of fibrosis at follow-up (P = 0.0005, Fisher's exact test). In contrast, the severity of lung imaging or PFT changes were not predictive of fatigue and dyspnea. INTERPRETATION: Although most hospitalized COVID-19 patients recover, a substantial proportion complains from persisting dyspnea and fatigue. Impairment of DLCO and signs suggestive of fibrosis are common but are not strictly related to long-lasting symptoms.

Bacterial contamination in the environment of hospitalised children with cystic fibrosis.

Pathogenic bacterial colonisation in Cystic Fibrosis patients is associated with a poor prognosis; thus, protective measures need to be taken to prevent their transmission. We studied the extent of contamination in the environment of hospitalised children with cystic fibrosis (CF) associated with specific activities. We assessed the levels of bacterial contamination in 432 air and surface samples collected from various locations in our CF centre over a three-month period: the bedrooms, corridor, communal showers, school, leisure centre and the respiratory functional explorations (RFE) unit. Staphylococcus aureus and Pseudomonas aeruginosa strains found in bedrooms and the RFE were compared with those found in patient expectorations using pulsed field gel electrophoresis. In all sampling locations, there were high levels of airborne contamination just after the presence of patients or nursing staff. In the bedrooms, the amount of S. aureus or P. aeruginosa in the air, at wake-up and after physiotherapy, were significantly higher than that after the bedroom had been cleaned. For P. aeruginosa, 33% of isolates were multiresistant to antibiotics; 50% of the colonised patients had the same P. aeruginosa strain in their sputum as in air taken from their bedroom. P. aeruginosa was detected in 23% of samples taken from the surfaces in the showers after patient washing. Very low levels of pathogenic bacteria were found in samples from the other locations. Overall, activities with the highest risk of contamination in the CF ward are physiotherapy and washing in the communal shower room. We therefore recommend to open windows after physiotherapy and to implement a strong decontamination after showers.

Inference in a survival cure model with mismeasured covariates using a simulation-extrapolation approach.

In many situations in survival analysis, it may happen that a fraction of individuals will never experience the event of interest: they are considered to be cured. The promotion time cure model takes this into account. We consider the case where one or more explanatory variables in the model are subject to measurement error, which should be taken into account to avoid biased estimators. A general approach is the simulation-extrapolation algorithm, a method based on simulations which allows one to estimate the effect of measurement error on the bias of the estimators and to reduce this bias. We extend this approach to the promotion time cure model. We explain how the algorithm works, and we show that the proposed estimator is approximately consistent and asymptotically normally distributed, and that it performs well in finite samples. Finally, we analyse a database in cardiology: among the explanatory variables of interest is the ejection fraction, which is known to be measured with error.