Literature DB >> 17688493

Testing goodness of fit of a uniform truncation model.

Micha Mandel1, Rebecca A Betensky.   

Abstract

Several goodness-of-fit tests of a lifetime distribution have been suggested in the literature; many take into account censoring and/or truncation of event times. In some contexts, a goodness-of-fit test for the truncation distribution is of interest. In particular, better estimates of the lifetime distribution can be obtained when knowledge of the truncation law is exploited. In cross-sectional sampling, for example, there are theoretical justifications for the assumption of a uniform truncation distribution, and several studies have used it to improve the efficiency of their survival estimates. The duality of lifetime and truncation in the absence of censoring enables methods for testing goodness of fit of the lifetime distribution to be used for testing goodness of fit of the truncation distribution. However, under random censoring, this duality does not hold and different tests are required. In this article, we introduce several goodness-of-fit tests for the truncation distribution and investigate their performance in the presence of censored event times using simulation. We demonstrate the use of our tests on two data sets.

Mesh:

Year:  2007        PMID: 17688493     DOI: 10.1111/j.1541-0420.2006.00710.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  7 in total

1.  Semiparametric likelihood inference for left-truncated and right-censored data.

Authors:  Chiung-Yu Huang; Jing Ning; Jing Qin
Journal:  Biostatistics       Date:  2015-03-21       Impact factor: 5.899

2.  Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data.

Authors:  Chyong-Mei Chen; Pao-Sheng Shen
Journal:  Lifetime Data Anal       Date:  2017-02-06       Impact factor: 1.588

3.  Assumptions regarding right censoring in the presence of left truncation.

Authors:  Jing Qian; Rebecca A Betensky
Journal:  Stat Probab Lett       Date:  2014-04-01       Impact factor: 0.870

Review 4.  Nonparametric and semiparametric regression estimation for length-biased survival data.

Authors:  Yu Shen; Jing Ning; Jing Qin
Journal:  Lifetime Data Anal       Date:  2016-04-16       Impact factor: 1.588

5.  Nonparametric estimation in the illness-death model using prevalent data.

Authors:  Bella Vakulenko-Lagun; Micha Mandel; Yair Goldberg
Journal:  Lifetime Data Anal       Date:  2016-06-28       Impact factor: 1.588

6.  A pairwise likelihood augmented Cox estimator for left-truncated data.

Authors:  Fan Wu; Sehee Kim; Jing Qin; Rajiv Saran; Yi Li
Journal:  Biometrics       Date:  2017-08-29       Impact factor: 2.571

7.  Semiparametric Accelerated Failure Time Model for Length-biased Data with Application to Dementia Study.

Authors:  Jing Ning; Jing Qin; Yu Shen
Journal:  Stat Sin       Date:  2014-01-01       Impact factor: 1.261

  7 in total

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