Current status data with two competing risks and time-dependent missing failure types.

In competing risks data, in practice, there may be lack of information or uncertainty about the true failure type, termed as 'missing failure type', for some subjects. We consider a general pattern of missing failure type in which we observe, if not the true failure type, a set of possible failure types containing the true one. In this work, we focus on both parametric and non-parametric estimation based on current status data with two competing risks and the above-mentioned missing failure type.

Current status data with two competing risks and missing failure types: a parametric approach.

Missing cause of failure is a common problem in competing risks data. Here we consider a general missing pattern in which one observes a set of possible causes containing the true cause. In this work, we focus on the parametric analysis of current status data with two competing risks and the above-mentioned missing pattern.

Optimal Timing for Cancer Screening and Adaptive Surveillance Using Mathematical Modeling.

Cancer screening and early detection efforts have been partially successful in reducing incidence and mortality, but many improvements are needed. Although current medical practice is informed by epidemiologic studies and experts, the decisions for guidelines are ultimately . We propose here that quantitative optimization of protocols can potentially increase screening success and reduce overdiagnosis.

A validation sampling approach for consistent estimation of adverse drug reaction risk with misclassified right-censored survival data.

Patient electronic health records, viewed as continuous-time right-censored survival data, can be used to estimate adverse drug reaction risk. Temporal outcome misclassification may occur as a result of errors in follow-up. These errors can be due to a failure to observe the incidence time of the adverse event of interest (due to misdiagnosis or nonreporting, etc) or an actual misdiagnosis of a competing adverse event.

Sample size of the reference sample in a case-augmented study.

The case-augmented study, in which a case sample is augmented with a reference (random) sample from the source population with only covariates information known, is becoming popular in different areas of applied science such as pharmacovigilance, ecology, and econometrics. In general, the case sample is available from some source (for example, hospital database, case registry, etc.); however, the reference sample is required to be drawn from the corresponding source population.

Effect of reporting bias in the analysis of spontaneous reporting data.

It is well-known that a spontaneous reporting system suffers from significant under-reporting of adverse drug reactions from the source population. The existing methods do not adjust for such under-reporting for the calculation of measures of association between a drug and the adverse drug reaction under study. Often there is direct and/or indirect information on the reporting probabilities.

Time-series analyses of air pollution and mortality in the United States: a subsampling approach.

Background: Hierarchical Bayesian methods have been used in previous papers to estimate national mean effects of air pollutants on daily deaths in time-series analyses.

Objectives: We obtained maximum likelihood estimates of the common national effects of the criteria pollutants on mortality based on time-series data from ≤ 108 metropolitan areas in the United States.

Methods: We used a subsampling bootstrap procedure to obtain the maximum likelihood estimates and confidence bounds for common national effects of the criteria pollutants, as measured by the percentage increase in daily mortality associated with a unit increase in daily 24-hr mean pollutant concentration on the previous day, while controlling for weather and temporal trends.

Number and size distribution of colorectal adenomas under the multistage clonal expansion model of cancer.

Colorectal cancer (CRC) is believed to arise from mutant stem cells in colonic crypts that undergo a well-characterized progression involving benign adenoma, the precursor to invasive carcinoma. Although a number of (epi)genetic events have been identified as drivers of this process, little is known about the dynamics involved in the stage-wise progression from the first appearance of an adenoma to its ultimate conversion to malignant cancer. By the time adenomas become endoscopically detectable (i.

Analysis of spontaneous adverse drug reaction (ADR) reports using supplementary information.

Assessment of safety of newly marketed drugs is an important public health issue. Once the drug is in the market, clinicians and/or health professionals are responsible for recognizing and reporting suspected side effects known as adverse drug reaction (ADR). Such reports are collected in a so-called spontaneous reporting (SR) system.

Parametric estimation of quality adjusted lifetime (QAL) distribution in progressive illness--death model.

In this work, we consider the parametric estimation of quality adjusted lifetime (QAL) distribution in progressive illness-death models. The main idea of this paper is to derive the theoretical distribution of QAL for the progressive illness-death models, under parametric models for the sojourn time distributions in different states, and then replace the model parameters by their estimates obtained by standard techniques of survival analysis. The method of estimation of the model parameters is also described.

Estimation of competing risks with general missing pattern in failure types.

In competing risks data, missing failure types (causes) is a very common phenomenon. In this work, we consider a general missing pattern in which, if a failure type is not observed, one observes a set of possible types containing the true type, along with the failure time. We first consider maximum likelihood estimation with missing-at-random assumption via the expectation maximization (EM) algorithm.

On the relationship between time-series studies, dynamic population studies, and estimating loss of life due to short-term exposure to environmental risks.

There is a growing concern that short-term exposure to combustion-related air pollution is associated with increased risk of death. This finding is based largely on time-series studies that estimate associations between daily variations in ambient air pollution concentrations and in the number of nonaccidental deaths within a community. Because these results are not based on cohort or dynamic population designs, where individuals are followed in time, it has been suggested that estimates of effect from these time-series studies cannot be used to determine the amount of life lost because of short-term exposures.

Exploring heterogeneity in tumour data using Markov chain Monte Carlo.

We describe a Bayesian approach to incorporate between-individual heterogeneity associated with parameters of complicated biological models. We emphasize the use of the Markov chain Monte Carlo (MCMC) method in this context and demonstrate the implementation and use of MCMC by analysis of simulated overdispersed Poisson counts and by analysis of an experimental data set on preneoplastic liver lesions (their number and sizes) in the presence of heterogeneity. These examples show that MCMC-based estimates, derived from the posterior distribution with uniform priors, may agree well with maximum likelihood estimates (if available).

Choice of stratification in Poisson process analysis of recurrent event data with environmental covariates.

The Poisson process approach for studying the association between environmental covariates and recurrent events depends on the stratification of study period into intervals within which the baseline intensities are assumed constant. In this work we investigate the problem of bias and variance due to misspecification of this stratification. We suggest a cross-validation approach to choosing a stratification model to balance the trade-off between bias and variance.