Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19.

Safe and effective vaccines are crucial for the control of Covid-19 and to protect individuals at higher risk of severe disease. The test-negative design is a popular option for evaluating the effectiveness of Covid-19 vaccines. However, the findings could be biased by several factors, including imperfect sensitivity and/or specificity of the test used for diagnosing the SARS-Cov-2 infection. We propose a simple Bayesian modeling approach for estimating vaccine effectiveness that is robust even when the diagnostic test is imperfect. We use simulation studies to demonstrate the robustness of our method to misclassification bias and illustrate the utility of our approach using real-world examples.

Evaluation of diagnostic test procedures for SARS-CoV-2 using latent class models.

Polymerase chain reaction (PCR) and antigen tests have been used extensively for screening during the severe acute respiratory syndrome coronavirus 2 pandemics. However, the real-world sensitivity and specificity of the two testing procedures in the field have not yet been estimated without assuming that the PCR constitutes a gold standard test. We use latent class models to estimate the in situ performance of both tests using data from the Danish national registries. We find that the specificity of both tests is very high (>99.7%), while the sensitivities are 95.7% (95% confidence interval [CI]: 92.8%-98.4%) and 53.8% (95% CI: 49.8%-57.9%) for the PCR and antigen tests, respectively. These findings have implications for the use of confirmatory PCR tests following a positive antigen test result: we estimate that serial testing is counterproductive at higher prevalence levels.

Capturing Spike Variability in Noisy Izhikevich Neurons Using Point Process Generalized Linear Models.

To understand neural activity, two broad categories of models exist: statistical and dynamical. While statistical models possess rigorous methods for parameter estimation and goodness-of-fit assessment, dynamical models provide mechanistic insight. In general, these two categories of models are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input current. We then fit these spike train data with a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured.

Oscillating systems with cointegrated phase processes.

We present cointegration analysis as a method to infer the network structure of a linearly phase coupled oscillating system. By defining a class of oscillating systems with interacting phases, we derive a data generating process where we can specify the coupling structure of a network that resembles biological processes. In particular we study a network of Winfree oscillators, for which we present a statistical analysis of various simulated networks, where we conclude on the coupling structure: the direction of feedback in the phase processes and proportional coupling strength between individual components of the system. We show that we can correctly classify the network structure for such a system by cointegration analysis, for various types of coupling, including uni-/bi-directional and all-to-all coupling. Finally, we analyze a set of EEG recordings and discuss the current applicability of cointegration analysis in the field of neuroscience.

Serum amyloid A does not predict non-survival in hospitalised adult horses with acute colitis.

BACKGROUND: Predicting non-survival in horses with acute colitis improves early decision making. Therefore, this study aimed to determine the prognostic value of serum amyloid A (SAA) and other clinicopathological and clinical variables in adult horses with acute colitis. METHODS: Clinical variables, SAA and other blood biomarkers, including plasma L-lactate (lactate), were assessed in 176 horses with acute colitis. A multivariate model for the prediction of non-survival was constructed. Icelandic horses were analysed separately. RESULTS: Admission SAA was similar in survivors (median 548 mg/L; range 0-5453 mg/L) and non-survivors (396 mg/L; 0-5294) (p = 0.43). A model for non-survival included year of admission, lactate, heart rate, age and colic duration of more than 24 hours. Icelandic horses had a relative risk of 2.9 (95% confidence interval = 2.2-3.8) for acute colitis compared to other breeds. Lactate in Icelandic horses was higher than that in other breeds in both survivors (4.0 mmol/L, range 1.0-12.7 vs. 2.0, 0.7-12.5) and non-survivors (10.0, 1.5-26 vs. 5.4, 0.8-22) (p < 0.001). LIMITATIONS: The prognostic value of repeated measurements of SAA could not be assessed in this study, as 71% of the non-surviving horses died within a day of admission. CONCLUSION: Admission SAA did not predict non-survival. Breed needs consideration when lactate is evaluated as a predictor for non-survival in horses with colitis.

Differentiating Strongylus vulgaris-associated, non-strangulating intestinal infarctions from idiopathic peritonitis and acute colitis in horses.

BACKGROUND: Horses with non-strangulating intestinal infarction (NSII) are often misdiagnosed with idiopathic peritonitis or acute colitis. Early diagnosis is essential to ensure early surgical intervention and improve survival. METHODS: Clinical and laboratory data from horses admitted to the University of Copenhagen Large Animal Teaching Hospital with NSII, idiopathic peritonitis or acute colitis between 2009 and 2018 were used for univariate comparisons and a multivariable logistic regression model for prediction of NSII. RESULTS: Two hundred and thirty-one horses were included. A multivariable model for the prediction of NSII included gastric reflux (more than 5 L) (odds ratio [OR] 8.7; 95% confidence interval [CI] 2.1-36.2), abnormal findings palpated per rectum (intestinal dilatations/impactions [OR 4.43; 95% CI 1.43-13.38], colon displacements [OR 23.16; 95% CI 5.26-101.97] or intestinal mass [OR 179.7; 95% CI 23.5-1375.5]), white blood cell count (OR 1.2; 95% CI 1.1-1.4), packed cell volume (OR 0.9; 95% CI 0.8-0.9), age (OR 0.9; 95% CI 0.8-1.0) and heart rate (OR 1.1; 95% CI 1.0-1.1). The model had a low false positive rate (5%), but a high false negative rate (50%). LIMITATIONS: Due to the retrospective nature of the study, sample collection was inconsistent, resulting in missing values. CONCLUSION: The model had some capability in predicting NSII. However, the high risk of false negatives means that exploratory laparotomy should be considered in horses with peritonitis of unknown aetiology in areas where Strongylus vulgaris is prevalent and occurrence of idiopathic peritonitis is low.