Interval Censoring

2016 ◽  
pp. 375-396
Keyword(s):  
Interval Censoring

Estimating time to onset of swine influenza symptoms after initial novel A(H1N1v) viral infection

2010 ◽  
Vol 139 (9) ◽  
pp. 1418-1424 ◽  
Author(s):  
B. D. M. TOM ◽  
A. J. VAN HOEK ◽  
R. PEBODY ◽  
J. McMENAMIN ◽  
C. ROBERTSON ◽  
...  
Keyword(s):  
Incubation Time ◽  
Time Distribution ◽  
Age Groups ◽  
Swine Influenza ◽  
Interval Censoring ◽  
Limited Data ◽  
History Of ◽  
Influenza Case ◽  
Time To Onset

SUMMARYCharacterization of the incubation time from infection to onset is important for understanding the natural history of infectious diseases. Attempts to estimate the incubation time distribution for novel A(H1N1v) have been, up to now, based on limited data or peculiar samples. We characterized this distribution for a generic group of symptomatic cases using laboratory-confirmed swine influenza case-information. Estimates of the incubation distribution for the pandemic influenza were derived through parametric time-to-event analyses of data on onset of symptoms and exposure dates, accounting for interval censoring. We estimated a mean of about 1·6–1·7 days with a standard deviation of 2 days for the incubation time distribution in those who became symptomatic after infection with the A(H1N1v) virus strain. Separate analyses for the <15 years and ⩾15 years age groups showed a significant (P<0·02) difference with a longer mean incubation time in the older age group.

scholarly journals A Re-Analysis of a Randomized Clinical Trial for Gastric Cancer Using Interval Censoring

1997 ◽  
Vol 27 (6) ◽  
pp. 445-446 ◽  
Author(s):  
J. Sakamoto ◽  
S. Teramukai ◽  
H. Nakazato ◽  
Y. Ohashi
Keyword(s):  
Gastric Cancer ◽  
Clinical Trial ◽  
Randomized Clinical Trial ◽  
Interval Censoring

Receiver operating characteristic curve estimation for time to event with semicompeting risks and interval censoring

2016 ◽  
Vol 25 (6) ◽  
pp. 2750-2766 ◽  
Author(s):  
Hélène Jacqmin-Gadda ◽  
Paul Blanche ◽  
Emilie Chary ◽  
Célia Touraine ◽  
Jean-François Dartigues
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Censored Data ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Disease Onset ◽  
Interval Censoring ◽  
Inverse Probability ◽  
Operating Characteristic Curve ◽  
Semicompeting Risks

Semicompeting risks and interval censoring are frequent in medical studies, for instance when a disease may be diagnosed only at times of visit and disease onset is in competition with death. To evaluate the ability of markers to predict disease onset in this context, estimators of discrimination measures must account for these two issues. In recent years, methods for estimating the time-dependent receiver operating characteristic curve and the associated area under the ROC curve have been extended to account for right censored data and competing risks. In this paper, we show how an approximation allows to use the inverse probability of censoring weighting estimator for semicompeting events with interval censored data. Then, using an illness-death model, we propose two model-based estimators allowing to rigorously handle these issues. The first estimator is fully model based whereas the second one only uses the model to impute missing observations due to censoring. A simulation study shows that the bias for inverse probability of censoring weighting remains modest and may be less than the one of the two parametric estimators when the model is misspecified. We finally recommend the nonparametric inverse probability of censoring weighting estimator as main analysis and the imputation estimator based on the illness-death model as sensitivity analysis.

scholarly journals A Progressive Interval Type-I Censored Life Test Plan for Rayleigh Distribution

2019 ◽  
Vol 48 (3) ◽  
pp. 76-86
Author(s):  
Arun Kaushik
Keyword(s):  
Rayleigh Distribution ◽  
Interval Censoring ◽  
Maximum Likelihood Estimates ◽  
Type I ◽  
Test Plan ◽  
Type I Censoring ◽  
Progressive Type ◽  
Interval Type ◽  
Censoring Scheme ◽  
Two Parameter

In this paper, we have considered the problem of optimal inspection times for the progressive interval type-I censoring scheme where uncertainty in the process is governed by the two-parameter Rayleigh distribution. Here, we also introduced some optimality criterion and determined the optimum inspection times, accordingly. The effect of the number of inspections and choice of optimally spaced inspection times based on the asymptotic relative efficiencies of the maximum likelihood estimates of the parameters are also investigated. Further, we have discussed the optimal progressive type-I interval censoring plan when the inspection times and the expected proportions of total failures in the experiment are under control.

scholarly journals Semi-Markov models for interval censored transient cognitive states with back transitions and a competing risk

2016 ◽  
Vol 25 (6) ◽  
pp. 2909-2924 ◽  
Author(s):  
Shaoceng Wei ◽  
Richard J Kryscio
Keyword(s):  
Markov Models ◽  
Waiting Times ◽  
Likelihood Estimation ◽  
Interval Censoring ◽  
Competing Risk ◽  
Quasi Monte Carlo ◽  
Cognitive States ◽  
Interval Censored ◽  
Semi Markov Process ◽  
Nun Study

Continuous-time multi-state stochastic processes are useful for modeling the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient cognitive states and death as a competing risk. Each subject's cognition is assessed periodically resulting in interval censoring for the cognitive states while death without dementia is not interval censored. Since back transitions among the transient states are possible, Markov chains are often applied to this type of panel data. In this manuscript, we apply a semi-Markov process in which we assume that the waiting times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and in which we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for likelihood estimation. We apply our model to a real dataset, the Nun Study, a cohort of 461 participants.

Interval Censoring

2015 ◽  
pp. 1-9
Author(s):  
Thomas A. Gerds ◽  
Carolina Meier-Hirmer
Keyword(s):  
Interval Censoring
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