Generalizations of Current Status Data With Applications

In estimation of a survival function, current status data arises when the only information available on individuals is their survival status at a single monitoring time. Here we briefly review extensions of this form of data structure in two directions: (i) doubly censored current status data, where there is incomplete information on the origin of the failure time random variable, and (ii) current status information on more complicated stochastic processes. Simple examples of these data forms are presented for motivation.

Smooth Hazard Function Estimation for Interval Censored Data with Time Varying Covariates

In this paper we propose methods for smooth hazard estimation of a time variable where that variable is interval censored. These methods allow one to model the transformed hazard in terms of either smooth (smoothing splines) or linear functions of time and other relevant time varying predictor variables. We illustrate the use of this method on a dataset of hemophiliacs where the outcome, time to seroconversion for HIV, is interval censored and left-truncated.

Inference on Survival Data with Covariate Measurement Error - An Imputation-based Approach

We propose a new method for fitting proportional hazards models with error-prone covariates. Regression coefficients are estimated by solving an estimating equation that is the average of the partial likelihood scores based on imputed true covariates. For the purpose of imputation, a linear spline model is assumed on the baseline hazard. We discuss consistency and asymptotic normality of the resulting estimators, and propose a stochastic approximation scheme to obtain the estimates. The algorithm is easy to implement, and reduces to the ordinary Cox partial likelihood approach when the measurement error has a degenerative distribution. Simulations indicate high efficiency and robustness. We consider the special case where error-prone replicates are available on the unobserved true covariates. As expected, increasing the number of replicate for the unobserved covariates increases efficiency and reduces bias. We illustrate the practical utility of the proposed method with an Eastern Cooperative Oncology Group clinical trial where a genetic marker, c-myc expression level, is subject to measurement error.

A BAYESIAN SHRINKAGE MODEL FOR INCOMPLETE LONGITUDINAL BINARY DATA WITH APPLICATION TO THE BREAST CANCER PREVENTION TRIAL

We consider inference in randomized studies, in which repeatedly measured outcomes may be informatively missing due to drop out. In this setting, it is well known that full data estimands are not identified unless unverified assumptions are imposed. We assume a non-future dependence model for the drop-out mechanism and posit an exponential tilt model that links non-identifiable and identifiable distributions. This model is indexed by non-identified parameters, which are assumed to have an informative prior distribution, elicited from subject-matter experts. Under this model, full data estimands are shown to be expressed as functionals of the distribution of the observed data. To avoid the curse of dimensionality, we model the distribution of the observed data using a Bayesian shrinkage model. In a simulation study, we compare our approach to a fully parametric and a fully saturated model for the distribution of the observed data. Our methodology is motivated and applied to data from the Breast Cancer Prevention Trial.

Analyzing Bivariate Survival Data with Interval Sampling and Application to Cancer Epidemiology

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To analyze this type of bivariate failure time data, it is important to recognize the presence of bias arising due to interval sampling. In this paper, nonparametric and semiparametric methods are developed to analyze the bivariate survival data with interval sampling under stationary and semi-stationary conditions. Numerical studies demonstrate the proposed estimating approaches perform well with practical sample sizes in different simulated models. We apply the proposed methods to SEER ovarian cancer registry data for illustration of the methods and theory.

A non-spatial bias favouring fixated stimuli revealed in patients with spatial neglect

The cardinal feature of spatial neglect is severely impaired exploration of the contralesional space, a failure resulting in unawareness of many contralesional stimuli. This deficit is exacerbated by a reflexive attentional bias toward ipsilesional items. Here we show that, in addition to these spatially lateralized failures, neglect patients also exhibit a severe bias favouring stimuli presented at fixation. We tested neglect patients and matched healthy and right-hemisphere damaged patients without neglect in a task requiring saccade execution to targets in the left or right hemifield. Targets were presented alone or simultaneously with a distracter that appeared in the same hemifield, in the opposite hemifield, or at fixation. We found two fundamental biases in saccade initiation of neglect patients: irrelevant distracters presented in the preserved hemifield tended to capture gaze reflexively, resulting in a large number of saccades erroneously directed toward the distracter. Additionally, distracters presented at fixation severely disrupted saccade initiation irrespective of saccade direction, leading to disproportionately increased latencies of left and right saccades. This latency increase was specific to oculomotor responses of neglect patients and was not observed when a manual response was required. These results show that, in addition to their failure to inhibit reflexive glances toward ipsilesional items neglect patients exhibit a strong oculomotor bias favouring fixated stimuli. We conclude that impaired initiation of saccades in any direction contributes to the deficits of spatial exploration that characterize spatial neglect.

From Statistical Data to Spatial Knowledge - informing decision making in Vietnam

Vietnam has developed rapidly over the past 15 years. However, progress was not uniformly distributed across the country. Availability, adequate visualization and analysis of spatially explicit data on socio-economic and environmental aspects can support both research and policy towards sustainable development. Applying appropriate mapping techniques allows gleaning important information from tabular socio-economic data. Spatial analysis of socio-economic phenomena can yield insights into locally-specifi c patterns and processes that cannot be generated by non-spatial applications. This paper presents techniques and applications that develop and analyze spatially highly disaggregated socioeconomic datasets. A number of examples show how such information can support informed decisionmaking and research in Vietnam.

Interaction between rheumatoid arthritis and pregnancy: correlation of molecular data with clinical disease activity measures

OBJECTIVE: The factors that induce remission of RA during pregnancy and the relapse occurring after delivery remain an enigma. In a previous study, we investigated gene-expression profiles of peripheral blood mononuclear cells (PBMC) in patients with RA and healthy women in late pregnancy and postpartum. Profiles of samples from both groups were similar in late pregnancy with elevated monocyte and decreased lymphocyte signatures. Postpartum, in RA PBMC the high level of monocyte transcripts persisted. Further increase was observed in adhesion, migration and signalling processes related to monocytes but also in lymphocytes despite similar clinical activity due to intensified drug treatment. This prompted us to investigate correlations between clinical parameters of disease activity and gene profiles. METHODS: Transcriptome data were correlated with RADAI, CRP, monocyte and lymphocyte counts. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations, monocytes and lymphocytes signatures were used as reference information. RESULTS: Comparative analysis of PBMC expression profiles from RA patients during and after pregnancy with RADAI and CRP revealed a correlation of these disease activity parameters predominantly with monocyte transcripts. Genes related to cellular programs of adhesion, migration and response to infections were upregulated. Comparing clinically active and not-active RA patients postpartum revealed a cluster of 19 genes that could also identify active disease during pregnancy. CONCLUSION: The data suggest that an increase of the RADAI and an elevation of CRP is a consequence of molecular activation of monocytes. Furthermore, they indicate that molecular activation of T lymphocytes may remain clinically unrecognized postpartum. It is conceivable that a set of 19 genes may qualify as molecular disease activity marker.