Semiparametric left truncation and right censorship models with missing censoring indicators

2008 ◽  
Vol 78 (16) ◽  
pp. 2572-2577 ◽  
Author(s):  
Sundarraman Subramanian ◽  
Dipankar Bandyopadhyay
Keyword(s):  
Left Truncation ◽  
Right Censorship ◽  
Missing Censoring Indicators

The CLT under right censorship and reporting delays

2007 ◽  
Vol 137 (3) ◽  
pp. 1035-1042
Author(s):  
Kai Kopperschmidt ◽  
Winfried Stute
Keyword(s):  
Right Censorship ◽  
Reporting Delays

scholarly journals Decreased median survival of adults with sickle cell disease after adjusting for left truncation bias: a pooled analysis

Blood ◽  
2019 ◽  
Vol 133 (6) ◽  
pp. 615-617 ◽  
Author(s):  
Michael R. DeBaun ◽  
Djamila L. Ghafuri ◽  
Mark Rodeghier ◽  
Poulami Maitra ◽  
Shruti Chaturvedi ◽  
...  
Keyword(s):  
Sickle Cell Disease ◽  
Median Survival ◽  
Sickle Cell ◽  
Pooled Analysis ◽  
Left Truncation ◽  
Cell Disease

scholarly journals Nonidentifiability in the presence of factorization for truncated data

Biometrika ◽  
2019 ◽  
Vol 106 (3) ◽  
pp. 724-731
Author(s):  
B Vakulenko-Lagun ◽  
J Qian ◽  
S H Chiou ◽  
R A Betensky
Keyword(s):  
Conditional Distribution ◽  
Left Truncation ◽  
Time To Event ◽  
Right Censored Data ◽  
Truncated Data ◽  
The Common ◽  
Independence Results ◽  
Factorization Condition ◽  
Common Understanding ◽  
Standard Risk

Summary A time to event, $X$, is left-truncated by $T$ if $X$ can be observed only if $T<X$. This often results in oversampling of large values of $X$, and necessitates adjustment of estimation procedures to avoid bias. Simple risk-set adjustments can be made to standard risk-set-based estimators to accommodate left truncation when $T$ and $X$ are quasi-independent. We derive a weaker factorization condition for the conditional distribution of $T$ given $X$ in the observable region that permits risk-set adjustment for estimation of the distribution of $X$, but not of the distribution of $T$. Quasi-independence results when the analogous factorization condition for $X$ given $T$ holds also, in which case the distributions of $X$ and $T$ are easily estimated. While we can test for factorization, if the test does not reject, we cannot identify which factorization condition holds, or whether quasi-independence holds. Hence we require an unverifiable assumption in order to estimate the distribution of $X$ or $T$ based on truncated data. This contrasts with the common understanding that truncation is different from censoring in requiring no unverifiable assumptions for estimation. We illustrate these concepts through a simulation of left-truncated and right-censored data.

scholarly journals Multilevel mixed-effects parametric survival analysis: Estimation, simulation, and application

2019 ◽  
Vol 19 (4) ◽  
pp. 931-949 ◽  
Author(s):  
Michael J. Crowther
Keyword(s):  
Random Effects ◽  
Survival Data ◽  
Relative Survival ◽  
Survival Models ◽  
Left Truncation ◽  
Delayed Entry ◽  
Working Paper ◽  
Expected Mortality ◽  
Parametric Survival ◽  
Parametric Survival Analysis

In this article, I present the community-contributed stmixed command for fitting multilevel survival models. It serves as both an alternative to Stata’s official mestreg command and a complimentary command with substantial extensions. stmixed can fit multilevel survival models with any number of levels and random effects at each level, including flexible spline-based approaches (such as Royston–Parmar and the log-hazard equivalent) and user-defined hazard models. Simple or complex time-dependent effects can be included, as can expected mortality for a relative survival model. Left-truncation (delayed entry) is supported, and t-distributed random effects are provided as an alternative to Gaussian random effects. I illustrate the methods with a commonly used dataset of patients with kidney disease suffering recurrent infections and a simulated example illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert 2012, Stata Journal 12: 674–687; 2013, Statistics in Medicine 32: 4118–4134). stmixed is part of the merlin family (Crowther 2017, arXiv Working Paper No. arXiv:1710.02223; 2018, arXiv Working Paper No. arXiv:1806.01615).

A mixture model for time to AIDS data with left truncation and an uncertain origin

Biometrika ◽  
1989 ◽  
Vol 76 (4) ◽  
pp. 814-817 ◽  
Author(s):  
C. A. STRUTHERS ◽  
V. T. FAREWELL
Keyword(s):  
Mixture Model ◽  
Left Truncation

Comparing estimation approaches for the illness-death model under left truncation and right censoring

2015 ◽  
Vol 35 (9) ◽  
pp. 1533-1548 ◽  
Author(s):  
Bella Vakulenko-Lagun ◽  
Micha Mandel
Keyword(s):  
Right Censoring ◽  
Left Truncation
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