TITLE

A flexible semi-Markov model for interval-censored data and goodness-of-fit testing

AUTHOR(S)
Foucher, Y.; Giral, M.; Soulillou, J. P.; Daures, J. P.
PUB. DATE
April 2010
SOURCE
Statistical Methods in Medical Research;Apr2010, Vol. 19 Issue 2, p127
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Multi-state approaches are becoming increasingly popular to analyse the complex evolution of patients with chronic diseases. For example, the evolution of kidney transplant recipients can be broken down into several clinical states. With this application in mind, we present a flexible semi-Markov model. The distribution functions are fitted to the durations in states and the relevance of the generalised Weibull distribution is shown. The corresponding likelihood function allows for interval censoring, i.e. the times of transitions and the sequences of states are not available during the elapsed times between two visits. The explanatory variables are introduced through the Markov chain and through the probability density functions of durations. A goodness-of-fit test is also defined to examine the stationarity of the semi-Markov model.
ACCESSION #
49765391

 

Related Articles

  • Comparison of Weibull and normal distributions for concrete compressive strengths. Tumidajski, P. J.; Fiore, L.; Khodabocus, T.; Lachemi, M.; Pari, R. // Canadian Journal of Civil Engineering;Oct2006, Vol. 33 Issue 10, p1287 

    For concrete produced in a commercial ready mix operation, the compressive strengths were fitted to Weibull and normal distributions. It was found that the Weibull distribution successfully describes concrete compressive strength failure data. This information is useful in the theoretical...

  • Fitting of Statistical Distributions to Wind Speed Data in Malaysia. Zaharim, Azami; Razali, Ahmad Mahir; Abidin, Rozaimah Zainal; Sopian, Kamaruzzaman // European Journal of Scientific Research;Jan2009, Vol. 26 Issue 1, p6 

    This paper investigates the probability distribution of wind speed data recorded in Faculty of Engineering, University Kebangsaan Malaysia. The wind speed data represented in the form of frequency curves show the shape of a potential model. The two-parameter Weibull distribution and lognormal...

  • Homogeneity testing in a Weibuil mixture model. Mosler, Karl; Scheicher, Christoph // Statistical Papers;Apr2008, Vol. 49 Issue 2, p315 

    The mixed Weibull distribution provides a flexible model to analyze random durations in a possibly heterogeneous population. To test for homogeneity against unobserved heterogeneity in a Weibull mixture model, a dispersion score test and a goodness-of-fit test are investigated. The empirical...

  • Computation of the asymptotic null distribution of goodness-of-fit tests for multi-state models. Titman, Andrew C. // Lifetime Data Analysis;Dec2009, Vol. 15 Issue 4, p519 

    We develop an improved approximation to the asymptotic null distribution of the goodness-of-fit tests for panel observed multi-state Markov models (Aguirre-Hernandez and Farewell, Stat Med 21:1899–1911, 2002) and hidden Markov models (Titman and Sharples, Stat Med 27:2177–2195,...

  • Wind Energy Validation Using Available Wind Potential. Hapse, Manik M.; Thosar, A. G.; Shinde, Sanjay M.; Markad, Satish A. // International Journal on Communication;Mar2011, Vol. 2 Issue 1, p33 

    This paper analyzes the probability distribution of wind speed data recorded by Maharashtra Energy Development Agency (MEDA) wind farm at Ahmednagar (India). The main objective is to validate the wind energy probability by using probability distribution function (PDF) of available wind...

  • The Anderson-Darling test for normality. Nelson, Lloyd S. // Journal of Quality Technology;Jul98, Vol. 30 Issue 3, p298 

    Provides information on the test of Anderson-Darling for normality. When a sampled distribution is normal; Description of goodness-of-fit tests.

  • An Estimation of the Chronic Rejection of Kidney Transplant Using an Eternal Weibull Regression: A Historical Cohort Study. Golestan, Banafsheh; Hosseini-Moghaddam, Seyed Mohammadmehdi; Nafar, Mohsen; Rennolls, K.; Mohammad, Kazem // Archives of Iranian Medicine (AIM);Jul2009, Vol. 12 Issue 4, p341 

    Background: We estimated the chronic rejection of kidney transplant using an eternal Weibull regression. Methods: In this historical cohort study, we enrolled all patients with chronic renal failure who were admitted to Shahid Labbafinejad medical center (Tehran, Iran) from 1984 to 2003. Using...

  • Bayesian Estimation Based on Record Values from Exponentiated Weibull Distribution: an Markov Chain Monte Carlo Approach. El-Sagheer, Rashad Mohamed // World Applied Sciences Journal;2014, Vol. 32 Issue 8, p1513 

    In this paper,we consider the Bayes estimators of the unknown parameters of the exponentiated Weibull distribution (EWD) under the assumptions of gamma priors on both shape parameters. Point estimation and confidence intervals based on maximum likelihood and bootstrap methods are proposed. The...

  • COPADS III (Compendium of Distributions II): Cauchy, Cosine, Exponential, Hypergeometric, Logarithmic, Semicircular, Triangular, and Weibull. Chen, Kenneth F. Q.; Ling, Maurice H. T. // Python Papers Source Codes;2013, Vol. 5, p1 

    This manuscript illustrates the implementation and testing of eight statistical distributions, namely Cauchy, Cosine, Exponential, Hypergeometric, Logarithmic, Semicircular, Triangular, and Weibull distribution, where each distribution consists of three common functions - Probability Density...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics