Dynamic prediction of recurrent events data by landmarking with application to a follow-up study of patients after kidney transplant

© 2016, © The Author(s) 2016. This paper extends dynamic prediction by landmarking to recurrent event data. The motivating data comprised post-kidney transplantation records of repeated infections and repeated measurements of multiple markers. At each landmark time point t s , a Cox proportional hazards model with a frailty term was fitted using data of individuals who were at risk at landmark s. This model included the time-updated marker values at t s as time-fixed covariates. Based on a stacked data set that merged all landmark data sets, we considered supermodels that allow parameters to depend on the landmarks in a smooth fashion. We described and evaluated four ways to parameterize the supermodels for recurrent event data. With both the study data and simulated data sets, we compared supermodels that were fitted on stacked data sets that consisted of either overlapping or non-overlapping landmark periods. We observed that for recurrent event data, the supermodels may yield biased estimates when overlapping landmark periods are used for stacking. Using the best supermodel amongst the ones considered, we dynamically estimated the probability to remain infection free between t s and a prediction horizon t hor , conditional on the information available at t s

Dynamic prediction of recurrent events data by landmarking with application to a follow-up study of patients after kidney transplant

© 2016, © The Author(s) 2016. This paper extends dynamic prediction by landmarking to recurrent event data. The motivating data comprised post-kidney transplantation records of repeated infections and repeated measurements of multiple markers. At each landmark time point t s , a Cox proportional hazards model with a frailty term was fitted using data of individuals who were at risk at landmark s. This model included the time-updated marker values at t s as time-fixed covariates. Based on a stacked data set that merged all landmark data sets, we considered supermodels that allow parameters to depend on the landmarks in a smooth fashion. We described and evaluated four ways to parameterize the supermodels for recurrent event data. With both the study data and simulated data sets, we compared supermodels that were fitted on stacked data sets that consisted of either overlapping or non-overlapping landmark periods. We observed that for recurrent event data, the supermodels may yield biased estimates when overlapping landmark periods are used for stacking. Using the best supermodel amongst the ones considered, we dynamically estimated the probability to remain infection free between t s and a prediction horizon t hor , conditional on the information available at t s

Simulated maximum likelihood estimation in joint models for multiple longitudinal markers and recurrent events of multiple types, in the presence of a terminal event

In medical studies we are often confronted with complex longitudinal data. During the follow-up period, which can be ended prematurely by a terminal event (e.g. death), a subject can experience recurrent events of multiple types. In addition, we collect repeated measurements from multiple markers. An adverse health status, represented by ‘bad’ marker values and an abnormal number of recurrent events, is often associated with the risk of experiencing the terminal event. In this situation, the missingness of the data is not at random and, to avoid bias, it is necessary to model all data simultaneously using a joint model. The correlations between the repeated observations of a marker or an event type within an individual are captured by normally distributed random effects. Because the joint likelihood contains an analytically intractable integral, Bayesian approaches or quadrature approximation techniques are necessary to evaluate the likelihood. However, when the number of recurrent event types and markers is large, the dimensionality of the integral is high and these methods are too computationally expensive. As an alternative, we propose a simulated maximum-likelihood approach based on quasi-Monte Carlo integration to evaluate the likelihood of joint models with multiple recurrent event types and markers

Simulated maximum likelihood estimation in joint models for multiple longitudinal markers and recurrent events of multiple types, in the presence of a terminal event

In medical studies we are often confronted with complex longitudinal data. During the follow-up period, which can be ended prematurely by a terminal event (e.g. death), a subject can experience recurrent events of multiple types. In addition, we collect repeated measurements from multiple markers. An adverse health status, represented by ‘bad’ marker values and an abnormal number of recurrent events, is often associated with the risk of experiencing the terminal event. In this situation, the missingness of the data is not at random and, to avoid bias, it is necessary to model all data simultaneously using a joint model. The correlations between the repeated observations of a marker or an event type within an individual are captured by normally distributed random effects. Because the joint likelihood contains an analytically intractable integral, Bayesian approaches or quadrature approximation techniques are necessary to evaluate the likelihood. However, when the number of recurrent event types and markers is large, the dimensionality of the integral is high and these methods are too computationally expensive. As an alternative, we propose a simulated maximum-likelihood approach based on quasi-Monte Carlo integration to evaluate the likelihood of joint models with multiple recurrent event types and markers

Simulated maximum likelihood estimation in joint models for multiple longitudinal markers and recurrent events of multiple types, in the presence of a terminal event

In medical studies we are often confronted with complex longitudinal data. During the follow-up period, which can be ended prematurely by a terminal event (e.g. death), a subject can experience recurrent events of multiple types. In addition, we collect repeated measurements from multiple markers. An adverse health status, represented by ‘bad’ marker values and an abnormal number of recurrent events, is often associated with the risk of experiencing the terminal event. In this situation, the missingness of the data is not at random and, to avoid bias, it is necessary to model all data simultaneously using a joint model. The correlations between the repeated observations of a marker or an event type within an individual are captured by normally distributed random effects. Because the joint likelihood contains an analytically intractable integral, Bayesian approaches or quadrature approximation techniques are necessary to evaluate the likelihood. However, when the number of recurrent event types and markers is large, the dimensionality of the integral is high and these methods are too computationally expensive. As an alternative, we propose a simulated maximum-likelihood approach based on quasi-Monte Carlo integration to evaluate the likelihood of joint models with multiple recurrent event types and markers

Urinary granzyme A mRNA is a biomarker to diagnose subclinical and acute cellular rejection in kidney transplant recipients

The distinction between T-cell-mediated rejection (TCMR) and other causes of kidney transplant dysfunction such as tubular necrosis requires biopsy. Subclinical rejection (SCR), an established risk factor for chronic allograft dysfunction, can only be diagnosed by protocol biopsy. A specific non-invasive biomarker to monitor immunological graft status would facilitate diagnosis and treatment of common transplantation-related complications. To identify possible markers, we measured urinary mRNA levels of several cytolytic proteins by quantitative PCR. Our cohort of 70 renal transplant recipients had biopsy proven type I and type II TCMR, acute tubular necrosis, SCR, calcineurin inhibitortoxicity, cytomegalovirus infection, and stable graft function with normal histology. Granzyme A (GzmA) mRNA was significantly higher in subclinical and acute cellular rejection compared to patients with stable grafts or those with tubular necrosis with 80% sensitivity and up to 100% specificity. Granzyme B and perforin mRNA levels could significantly discriminate acute rejection from stable or tubular necrosis, but were not significantly elevated during SCR. Importantly, only GzmA mRNA remained below detection limits from grafts that were stable and most with tubular necrosis. Hence, the presented data indicate that urinary GzmA mRNA levels may entail a diagnostic non-invasive biomarker to distinguish patients with subclinical and acute cellular rejection from those with tubular necrosis or stable grafts

Divergent chemokine receptor expression and the consequence for human IgG4 B cell responses

IgG4 antibodies are unique to humans. IgG4 is associated with tolerance during immunotherapy in allergy, but also with pathology, as in pemphigus vulgaris and IgG4‐related disease. Its induction is largely restricted to non‐microbial antigens, and requires repeated or prolonged antigenic stimulation, for reasons poorly understood. An important aspect in generating high‐affinity IgG antibodies is chemokine receptor‐mediated migration of B cells into appropriate niches, such as germinal centers. Here, we show that compared to IgG1 B cells, circulating IgG4 B cells express lower levels of CXCR3, CXCR4, CXCR5, CCR6, and CXCR7, chemokine receptors involved in germinal center reactions and generation of long‐lived plasma cells. This phenotype was recapitulated by in vitro priming of naive B cells with an IgG4‐inducing combination of TFH/TH2 cytokines. Consistent with these observations, we found a low abundance of IgG4 B cells in secondary lymphoid tissues in vivo, and the IgG4 antibody response is substantially more short‐lived compared to other IgG subclasses in patient groups undergoing CD20+ B cell depletion therapy with rituximab. These results prompt the hypothesis that factors needed to form IgG4 B cells restrain at the same time the induction of a robust migratory phenotype that could support a long‐lived IgG4 antibody response