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Paulina Staus,1 Maja von Cube,1,* Derek Hazard,1,* Sam Doerken,1 Ksenia Ershova,2 James Balmford1 ,† Martin Wolkewitz1 1Institute of Medical Biometry and Statistics, Division Methods in Clinical Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; 2Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA*These authors contributed equally to this work†James Balmford passed away in March 2020Correspondence: Paulina Staus, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany, Email staus@imbi.uni-freiburg.dePurpose: 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.Graphical Abstract: Keywords: selection bias, hospital infection, intensive care units, proportional hazards models, risk assessment, cohort studies

Martin Wolkewitz,1 Jerome Lambert,1 Maja von Cube,1 Lars Bugiera,1 Marlon Grodd,1 Derek Hazard,1 Nicole White,2 Adrian Barnett,2 Klaus Kaier1 1Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; 2School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, AustraliaCorrespondence: Klaus KaierInstitute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanyEmail kaier@imbi.uni-freiburg.deAbstract: 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.Keywords: competing risk bias, immortal-time bias, competing events, time-dependent bias, time-varying exposure, time-to-event analysis

OBJECTIVE: To determine if patient ratings of their ability to chew are dependent on the method of complete denture fabrication. METHOD AND MATERIALS: Twenty edentulous patients participated in a randomized within-subject crossover trial. Each participant received 2 sets of new complete dentures. One pair (Gerber prosthesis) was manufactured based on tracings (to determine centric relation) and facebow transfer; semianatomic teeth with a lingualized and balanced occlusal pattern were used. The other set of complete dentures was made using a simplified procedure without facebow transfer; jaw relations were recorded with wax occlusion rims, and anatomic teeth were set with a canine and premolar guidance (Gysi prosthesis). The 2 dentures were delivered in randomized order, and each was worn for 3 months. Three months after insertion, patients' ratings of each new prosthesis were obtained on visual analog scales for general satisfaction, comfort, ability to speak, stability, esthetics, ease of cleaning, and ability to chew. RESULTS: Patients rated their general satisfaction, stability, and esthetic appearance significantly better for the Gysi prostheses ( P < .05). No significant differences between the 2 denture treatment methods were detected for ability to speak, comfort, chewing ability, and the ease of cleaning the dentures. CONCLUSION: A comprehensive method for the fabrication of complete dentures using lingualized teeth does not appear to positively influence patient ratings of denture satisfaction when compared to a simple procedure with anatomic teeth. [ABSTRACT FROM AUTHOR]

Background: Sampling from a large cohort in order to derive a subsample that would be sufficient for statistical analysis is a frequently used method for handling large data sets in epidemiological studies with limited resources for exposure measurement. For clinical studies however, when interest is in the influence of a potential risk factor, cohort studies are often the first choice with all individuals entering the analysis.Objectives: Our aim is to close the gap between epidemiological and clinical studies with respect to design and power considerations. Schoenfeld's formula for the number of events required for a Cox' proportional hazards model is fundamental. Our objective is to compare the power of analyzing the full cohort and the power of a nested case-control and a case-cohort design.Methods: We compare formulas for power for sampling designs and cohort studies. In our data example we simultaneously apply a nested case-control design with a varying number of controls matched to each case, a case cohort design with varying subcohort size, a random subsample and a full cohort analysis. For each design we calculate the standard error for estimated regression coefficients and the mean number of distinct persons, for whom covariate information is required.Results: The formula for the power of a nested case-control design and the power of a case-cohort design is directly connected to the power of a cohort study using the well known Schoenfeld formula. The loss in precision of parameter estimates is relatively small compared to the saving in resources.Conclusions: Nested case-control and case-cohort studies, but not random subsamples yield an attractive alternative for analyzing clinical studies in the situation of a low event rate. Power calculations can be conducted straightforwardly to quantify the loss of power compared to the savings in the num-ber of patients using a sampling design instead of analyzing the full cohort. [ABSTRACT FROM AUTHOR]