Bayesian Estimation of the Time‐Varying Sensitivity of a Diagnostic Test with Application to Mother‐to‐Child Transmission of HIV

Summary We present a Bayesian model to estimate the time‐varying sensitivity of a diagnostic assay when the assay is given repeatedly over time, disease status is changing, and the gold standard is only partially observed. The model relies on parametric assumptions for the distribution of the latent time of disease onset and the time‐varying sensitivity. Additionally, we illustrate the incorporation of historical data for constructing prior distributions. We apply the new methods to data collected in a study of mother‐to‐child transmission of HIV and include a covariate for sensitivity to assess whether two different assays have different sensitivity profiles.     

Dynamic clinical prediction models for discrete time‐to‐event data with competing risks—A case study on the OUTCOMEREA database

The development of clinical prediction models requires the selection of suitable predictor variables. Techniques to perform objective Bayesian variable selection in the linear model are well developed and have been extended to the generalized linear model setting as well as to the Cox proportional hazards model. Here, we consider discrete time‐to‐event data with competing risks and propose methodology to develop a clinical prediction model for the daily risk of acquiring a ventilator‐associated pneumonia (VAP) attributed to P. aeruginosa (PA) in intensive care units. The competing events for a PA VAP are extubation, death, and VAP due to other bacteria. Baseline variables are potentially important to predict the outcome at the start of ventilation, but may lose some of their predictive power after a certain time. Therefore, we use a landmark approach for dynamic Bayesian variable selection where the set of relevant predictors depends on the time already spent at risk. We finally determine the direct impact of a variable on each competing event through cause‐specific variable selection.     

Cox proportional hazards models with left truncation and time‐varying coefficient: Application of age at event as outcome in cohort studies

When analyzing time‐to‐event cohort data, two different ways of choosing a time scale have been discussed in the literature: time‐on‐study or age at onset of disease. One advantage of choosing the latter is interpretability of the hazard ratio as a function of age. To handle the analysis of age at onset in a principled manner, we present an analysis of the Cox Proportional Hazards model with time‐varying coefficient for left‐truncated and right‐censored data. In the analysis of Northern Manhattan Study (NOMAS) with age at onset of stroke as outcome, we demonstrate that well‐established risk factors may be important only around a certain age span and less established risk factors can have a strong effect in a certain age span.     

O‐Acyl isopeptide method: development of an O‐acyl isodipeptide unit for Boc SPPS and its application to the synthesis of Aβ1‐42 isopeptide

The O‐acyl isopeptide method was developed for the efficient preparation of difficult sequence‐containing peptide. Furthermore, development of the O‐acyl isodipeptide unit for Fmoc chemistry simplified its synthetic procedure by solid‐phase peptide synthesis. Here, we report a novel isodipeptide unit for Boc chemistry, and the unit was successfully applied to the synthesis of amyloid β peptide. Combination of Boc chemistry and the isodipeptide unit would be an effective method for the synthesis of many difficult peptides. Copyright © 2014 European Peptide Society and John Wiley & Sons, Ltd.