No abstract

In this article, we propose a new approach to the problem of dynamic prediction of survival data in the presence of competing risks as an extension of the landmark model for ordinary survival data. The key feature of our method is the introduction of dynamic pseudo-observations constructed from the prediction probabilities at different landmark prediction times. They specifically address the issue of estimating covariate effects directly on the cumulative incidence scale in competing risks. A flexible generalized linear model based on these dynamic pseudo-observations and a generalized estimation equations approach to estimate the baseline and covariate effects will result in the desired dynamic predictions and robust standard errors. Our approach has a number of attractive features. It focuses directly on the prediction probabilities of interest, avoiding in this way complex modeling of cause-specific hazards or subdistribution hazards. As a result, it is robust against departures from these omnibus models. From a computational point of view an advantage of our approach is that it can be fitted with existing statistical software and that a variety of link functions and regression models can be considered, once the dynamic pseudo-observations have been estimated. We illustrate our approach on a real data set of chronic myeloid leukemia patients after bone marrow transplantation.

We propose an extension of the landmark model for ordinary survival data as a new approach to the problem of dynamic prediction in competing risks with time-dependent covariates. We fix a set of landmark time points tLM within the follow-up interval. For each of these landmark time points tLM , we create a landmark data set by selecting individuals at risk at tLM ; we fix the value of the time-dependent covariate in each landmark data set at tLM . We assume Cox proportional hazard models for the cause-specific hazards and consider smoothing the (possibly) time-dependent effect of the covariate for the different landmark data sets. Fitting this model is possible within the standard statistical software. We illustrate the features of the landmark modelling on a real data set on bone marrow transplantation.

Background In older adults pneumococcal disease is strongly associated with respiratory viral infections, but the impact of viruses on Streptococcus pneumoniae carriage prevalence and load remains poorly understood. Here, we investigated the effects of influenza-like illness (ILI) on pneumococcal carriage in community-dwelling older adults. Methods We investigated the presence of pneumococcal DNA in saliva samples collected in the 2014/2015 influenza season from 232 individuals aged ≥60 years at ILI-onset, followed by sampling 2-3 weeks and 7-9 weeks after the first sample. We also sampled 194 age-matched controls twice 2-3 weeks apart. Pneumococcal DNA was detected with quantitative-PCRs targeting piaB and lytA genes in raw and in culture-enriched saliva. Bacterial and pneumococcal abundances were determined in raw saliva with 16S and piaB quantification. Results The prevalence of pneumococcus-positive samples was highest at onset of ILI (18% or 42/232) and lowest among controls (13% or 26/194, and 11% or 22/194, at the first and second sampling moment, respectively), though these differences were not significant. Pneumococcal carriage was associated with exposure to young children (OR:2.71, 95%CI 1.51-5.02, p<0.001), and among asymptomatic controls with presence of rhinovirus infection (OR:4.23; 95%CI 1.16-14.22, p<0.05). When compared with carriers among controls, pneumococcal absolute abundances were significantly higher at onset of ILI (p<0.01), and remained elevated beyond recovery from ILI (p<0.05). Finally, pneumococcal abundances were highest in carriage events newly-detected after ILI-onset (estimated geometric mean 1.21E -5, 95%CI 2.48E -7-2.41E -5, compared with pre-existing carriage). Conclusions ILI exacerbates pneumococcal colonization of the airways in older adults, and this effect persists beyond recovery from ILI.

We study an alternative approach for estimation in the competing risks framework, called vertical modeling. It is motivated by a decomposition of the joint distribution of time and cause of failure. The two elements of this decomposition are (1) the time of failure and (2) the cause of failure condition on time of failure. Both elements of the model are based on observable quantities, namely the total hazard and the relative cause-specific hazards. The model can be implemented using the standard software. The relative cause-specific hazards are flexibly estimated using multinomial logistic regression and smoothing splines. We show estimates of cumulative incidences from vertical modeling to be more efficient statistically than those obtained from the standard nonparametric model. We illustrate our methods using data of 8966 leukemia patients from the European Group for Blood and Marrow Transplantation.

Background People aged 60 years or older are at high risk for respiratory infections, one of the leading causes of mortality worldwide. Vaccination is the main way to protect against these infections; however, vaccination is less effective in older adults than in younger adults due to ageing of the immune system, so innovative strategies that improve vaccine responses could provide a major public health benefit. The gut microbiota regulates host immune homoeostasis and response against pathogens, but human studies showing the effects of the gut microbiota on respiratory infections in older adults are sparse. We aimed to investigate the composition of the microbiota in relation to respiratory infections and local and systemic immune markers in older adults during an influenza season.Methods In this observational study, participants were selected from an influenza-like illness (ILI) prospective surveillance cohort in which community-dwelling adults aged 60 years and older in the Netherlands were recruited through their general practitioner or the Civil Registry. Inclusion criteria have been described elsewhere. Participants completed questionnaires and self-reported symptoms. To measure microbiota composition, faecal samples were collected from participants registering an ILI event, with a follow-up (recovery) sample collected 7-9 weeks after the ILI event, and from asymptomatic participants not reporting any event throughout the season. We tested associations between microbiota profiles and a set of health-related variables, patient characteristics, and local and systemic immune markers. We cultured identified bacterial biomarkers for ILI with CaCo-2 cells in an in vitro intestinal epithelial model and measured the induced immune response. This study is registered with http://www.trialregister.nl, NL4666.

We propose vertical modelling as a natural approach to the problem of analysis of competing risks data when failure types are missing for some individuals. Under a natural missing-at-random assumption for these missing failure types, we use the observed data likelihood to estimate its parameters and show that the all-cause hazard and the relative hazards appearing in vertical modelling are indeed key quantities of this likelihood. This fact has practical implications in that it suggests vertical modelling as a simple and attractive method of analysis in competing risks with missing causes of failure; all individuals are used in estimating the all-cause hazard and only those with non-missing cause of failure for relative hazards. The relative hazards also appear in a multiple imputation approach to the same problem proposed by Lu and Tsiatis and in the EM algorithm. We compare the vertical modelling approach with the method of Goetghebeur and Ryan for a breast cancer data set, highlighting the different aspects they contribute to the data analysis.

Breakthrough infections of measles and mumps have raised concerns about the duration of vaccine-induced immunity, that might be improved by a third dose of measles–mumps–rubella vaccine (MMR3). Here we compared (IgG) antibody levels to measles, mumps and rubella in blood samples of 9-year-old children and young adults (18-25 years) following MMR2 and MMR3, respectively. We found that, in addition to antibody boosting for all three vaccine components, MMR3 resulted in lower antibody decay rates than MMR2, which was most prominent for mumps and rubella. This study suggests that MMR3 provides long-lasting seroprotection against measles, mumps and rubella.