Presence Of Interval Censoring Research Articles

A proposal for estimating intensity functions in a three-state model in the presence of arbitrary censoring

Multi-state are useful tools to model the dynamics of recurring processes over time or some changing phenomenon over time. This paper presents a methodology to estimate time-dependent intensity functions in the presence of interval censoring and right-hand censoring when considering a three-state model like the disease-death one. The likelihood function is deduced mathematically, which incorporates information that has been collected longitudinally, as well as the different modes of censoring. This likelihood should be optimized numerically with the help of a Gauss quadrature since in that expression there is an integral which is related to censored units. A piecewise function-based method is explored through a simulation study to obtain an estimate of the intensities.

Incidence Estimation in Post-ICU Populations: Challenges and Possible Solutions When Using Claims Data.

New or worsening cognitive, physical and/or mental health impairments after acute care for critical illness are referred to as "post-intensive care syndrome" (PICS). Little is known about the incidence of its components, since it is challenging to recruit patients after intensive care unit (ICU) treatment for observational studies. Claims data are particularly suited to achieve incidence estimates in difficult-to-recruit groups. However, some limitations remain when using claims data for empirical research on the outcome of ICU treatment. The objective of this article is to describe three challenges and possible solutions for the estimation of the incidence of PICS based on claims data METHODOLOGICAL CHALLENGES: THE PRESENCE OF COMPETING RISK BY DEATH, INVESTIGATING A SYNDROME AND DEALING WITH INTERVAL CENSORING: First, in (post) ICU populations the assumption of independence between the event of interest (diagnosis of PICS component) and the competing event (death) is violated. Competing risk is an event whose occurrence precludes the event of interest to be observed, and in ICU populations, death is a frequent secondary event. Methods that estimate incidence in the presence of competing risks are well-established but have not been applied to the scenario described above. Second, PICS is a complex syndrome and represented by various ICD-10 (International Classification of Diseases, 10th Revision) disease codes. The operationalization of this syndrome (case identification) and the validation of cases are particularly challenging. Third, another major challenge is that the exact date of the event of interest is not available in claims data. It is only known that the event occurred within a certain interval. This feature is called interval censoring. Recently, methods have been developed that address informative censoring due to competing risks in the presence of interval censoring. We will discuss how these methods could be used to tackle the problem when estimating PICS components. Alternatively, it could be possible to assign an exact date for each diagnosis by combining the diagnosis with the exact date of prescriptions of the respective medicines and/or medical services. Estimating incidence in post-ICU populations entails various methodological issues when using claims data. Investigators need to be aware of the presence of competing risks. The application of internal validation criteria to operationalize the event of interest is crucial to achieve reliable incidence estimates. The problem of interval censoring can be solved either by statistical methods or by combining information from different sources.

Regression analysis of interval-censored failure time data with cured subgroup and mismeasured covariates

Failure time data occur in many areas and also in various forms and in particular, many authors have discussed regression analysis of failure time data in the presence of interval censoring, a cured subgroup or mismeasured covariates. However, it does not seem to exist an established procedure that can deal with all three issues together. Corresponding to this, we propose a sieve maximum likelihood estimation procedure that takes into account all three issues with the use of the SIMEX algorithm. The asymptotic properties of the proposed estimators are established, and an extensive simulation study is also conducted and suggests that the proposed method works well for practical situations.

Confidence Bands for the Survival Function Using a Weibull Regression Model in Presence of Arbitrary Censoring

Usually, the exact time at which an event occurs cannot be observed for several reasons; for instance, it is not possible to constantly monitor a characteristic of interest. This generates a phenomenon known as censoring that can be classified as having a left censor, right censor or interval censor. When one is working with survival data in the presence of arbitrary censoring, the survival time of interest is defined as the elapsed time between an initial event and the next event that is generally unknown. This problem has been widely studied in the statistic literature and some progress has been made, toward resolving and the formulation of a bivariate likelihood to estimate parameters in a parametric regression model offers positive development opportunities. In this paper, we construct a bivariate likelihood for the Weibull regression model in the presence of interval censoring. Finally, its performance is illustrated by means of a simulation study.

A Bayesian Analysis of Unobserved Heterogeneity for Unemployment Duration Data in the Presence of Interval Censoring

In this paper, we discuss Bayesian inference of unobserved heterogeneity for unemployment duration data in the presence of right and interval-censoring, and non-proportionality. We employ accelerated failure time models with three different distributional assumptions: log-logistic, log-normal, and Weibull models, and use members of an exponential family of distributions for considering unobserved heterogeneity. We adopt a Bayesian approach, using Markov Chain Monte Carlo via WinBUGS software, to analyze the data. The proposed approach is illustrated using the unemployment duration data set of Iran in 2009. A sensitivity analysis using different latent variable models of the exponential family is also considered. After checking convergence, using the Gelman-Rubin diagnostic test, we compared different distributional assumptions using the DIC3 criterion. Our findings reveal significant discrepancies in unemployment duration based on different covariates for the sample population of Iran in 2009.

Modeling the Longitudinal Transitions of Performance Status in Cancer Outpatients: Time to Discuss Palliative Care

ContextUnderstanding the longitudinal transitions of performance status among persons with cancer can assist providers in determining the appropriate time to initiate palliative care support. ObjectivesTo model longitudinal transitions of performance status in cancer outpatients, to determine the probabilities of improvement and deterioration in performance status over time, and to evaluate the factors associated with rates of transitions. MethodsThis population-based, retrospective, cohort study comprised adult outpatients diagnosed with any type of cancer and assessed for performance status throughout their observation period using the Palliative Performance Scale (PPS; scale 0–100; 0 indicates death). At every PPS assessment, patients were assigned to one of four states: stable state (PPS score 70–100), transitional state (PPS score 40–60), end-of-life state (PPS score 10–30), or dead. A Markov multistate model under the presence of interval censoring was used to examine the rate of state-to-state transitions. ResultsThere were 11,374 patients representing nearly 71,000 assessments. Patients with lung cancer in the transitional state had a 27.7% chance of being dead at the end of one month vs. 17.5% in patients with breast cancer. The average time spent in the transitional state was 6.6 weeks for patients diagnosed with gastrointestinal cancer vs. 8.8 weeks for patients with breast cancer. The rate at which one moves from the transitional state to death was higher for patients with lung cancer than those with breast cancer. ConclusionWe estimated the probability and direction of change in performance status in cancer outpatients. Entry into the transitional state may serve as an indicator for referral for palliative care support. Mean end-of-life sojourn times are too short to allow meaningful integration of palliative care.

Parametric Model to Analyse the Survival of Gastric Cancer in the Presence of Interval Censoring

The objective of the study was to assess the impact of prognostic factors on survival of patients with gastric cancer in the presence of interval censoring using parametric models. In a retrospective cohort study, 178 patients with gastric cancer were studied from February 2003 to January 2008. Gender, age at diagnosis, distant metastasis, tumor size, histology type, tumor grade, lymph node metastasis and pathologic stage were selected as prognostic and entered in the models. Weibull, exponential, log-logistic and log-normal analyses with interval censoring were performed as parametric models, and Akaike Information Criterion (AIC) was used to compare the efficiency of models. The risk of death for patients at an older age, with tumor size greater than 35 mm, distant metastasis and advanced stage of disease was statistically higher. Other clinical and demographic factors were not significant. According to AIC, the log logistic model is the most efficient of all the models in multivariable analysis. The results indicated that the early detection of a cancer at a young patient age and in primary stages is important to increase survival from gastric cancer. According to statistical criteria, a parametric model can also be a useful statistical model to find prognostic factors in the presence of interval censoring. Although it seems that all models in this analysis fit well, AIC supported the log logistic regression as the best option.

Multiple decrement modeling in the presence of interval censoring and masking

A self-consistent algorithm will be proposed to non-parametrically estimate the cause-specific cumulative incidence functions (CIFs) in an interval censored, multiple decrement context. More specifically, the censoring mechanism will be assumed to be a mixture of case 2 interval-censored data with the additional possibility of exact observations. The proposed algorithm is a generalization of the classical univariate algorithms of Efron and Turnbull. However, unlike any previous non-parametric models proposed in the literature to date, the algorithm will explicitly allow for the possibility of any combination of masked modes of failure, where failure is known only to occur due to a subset from the set of all possible causes. A simulation study is also conducted to demonstrate the consistency of the estimators of the CIFs produced by the proposed algorithm, as well as to explore the effect of masking. The paper concludes by applying the method to masked mortality data obtained for Pueblo County, CO, for three risks: death by cancer; cardiovascular failure; or other.

Interval censoring.

Interval-censored failure time data occur in many medical investigations as well as other studies such as demographical and sociological studies. They include the usual right-censored failure time data as a special case but provide much more complex structure and less relevant information than the right-censored data. This article reviews some basic concepts, issues and the corresponding statistical approaches related to the analysis of interval-censored data as well as recent advances. In particular, we discuss estimation of a survival function, comparison of several treatments and regression analysis as well as competing risks analysis and truncation in the presence of interval censoring. A well-known example of interval-censored data is described and analysed to illustrate some of the statistical procedures discussed.

Accelerated Failure Time Model for Arbitrarily Censored Data With Smoothed Error Distribution

This article develops a semiparametric procedure to estimate parameters of an accelerated failure time model. To express the density of the error distribution, we use the P-spline (B-splines with penalties) smoothing technique. To accommodate error densities with infinite support (and for other reasons) we replace the B-splines with their limits as the degree of the B-spline goes to infinity; namely, with normal densities. The spline coefficients as well as any number of regression parameters are quickly and accurately estimated via penalized maximum likelihood. The method directly provides predictive survival distributions for fixed values of covariates while allowing for left-, right-, and interval-censored data. The approach has been implemented as an R package and is applied here to the problem of predicting AIDS-free survival in the presence of interval censoring.