Nonparametric estimation for competing risks survival data subject to left truncation and interval censoring

Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death—that is, competing risks—and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Nonparametric Estimation of Survival Functions under Dependent Competing Risks

Nonparametric Functional Estimation and Related Topics ◽

10.1007/978-94-011-3222-0_33 ◽

1991 ◽

pp. 431-441

Author(s):

M. Bhaskara Rao ◽

G. Jogesh Babu ◽

C. Radhakrishna Rao

Keyword(s):

Competing Risks ◽

Nonparametric Estimation ◽

Survival Functions ◽

Dependent Competing Risks

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Flexible parametric model for survival data subject to dependent censoring

Biometrical Journal ◽

10.1002/bimj.201800375 ◽

2019 ◽

Vol 62 (1) ◽

pp. 136-156 ◽

Cited By ~ 1

Author(s):

Negera Wakgari Deresa ◽

Ingrid Van Keilegom

Keyword(s):

Survival Data ◽

Parametric Model ◽

Dependent Censoring ◽

Flexible Parametric Model ◽

Data Subject

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

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x19893639 ◽

2019 ◽

Vol 19 (4) ◽

pp. 931-949 ◽

Cited By ~ 3

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).

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