Cross-ratio estimation for bivariate failure times with left truncation

Hu, Tianle; Lin, Xihong; Nan, Bin

doi:10.1007/s10985-013-9263-7

Cited by 7 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following Hu, Lin and Nan (2014), the objective function (4) can be easily modified to accommodate left truncation. We leave the details to interested readers.…”

Section: Discussionmentioning

confidence: 99%

Proportional cross-ratio model

Nan

Lin

2018

Lifetime Data Anal

Self Cite

View full text Add to dashboard Cite

Cross-ratio is an important local measure of the strength of dependence among correlated failure times. If a covariate is available, it may be of scientific interest to understand how the cross-ratio varies with the covariate as well as time components. Motivated by the Tremin study, where the dependence between age at a marker event reflecting early lengthening of menstrual cycles and age at menopause may be affected by age at menarche, we propose a proportional cross-ratio model through a baseline cross-ratio function and a multiplicative covariate effect. Assuming a parametric model for the baseline cross-ratio, we generalize the pseudo-partial likelihood approach of Hu et al. (Biometrika 98:341-354, 2011) to the joint estimation of the baseline cross-ratio and the covariate effect. We show that the proposed parameter estimator is consistent and asymptotically normal. The performance of the proposed technique in finite samples is examined using simulation studies. In addition, the proposed method is applied to the Tremin study for the dependence between age at a marker event and age at menopause adjusting for age at menarche. The method is also applied to the Australian twin data for the estimation of zygosity effect on cross-ratio for age at appendicitis between twin pairs.

show abstract

“…Following Hu, Lin and Nan (2014), the objective function (4) can be easily modified to accommodate left truncation. We leave the details to interested readers.…”

Section: Discussionmentioning

confidence: 99%

Proportional cross-ratio model

Nan

Lin

2018

Lifetime Data Anal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Acquired Immune Deficiency Syndrome (AIDS) is a chronic disease, which is a result of Human Immunodeficiency Virus (HIV) infection. Transfusion related AIDS data are collected by the center for disease control (Hu et al, 2014;Moreira et al, 2021). The data consist of patients who were infected with HIV through blood or bloodproduct transfusion.…”

Section: Introductionmentioning

confidence: 99%

A Real Time Monitoring Approach for Bivariate Event Data

Zwetsloot¹,

Mahmood²,

Taiwo³

et al. 2021

Preprint

View full text Add to dashboard Cite

Early detection of changes in the frequency of events is an important task, in, for example, disease surveillance, monitoring of high-quality processes, reliability monitoring and public health. In this article, we focus on detecting changes in multivariate event data, by monitoring the time-between-events (TBE). Existing multivariate TBE charts are limited in the sense that, they only signal after an event occurred for each of the individual processes. This results in delays (i.e., long time to signal), especially if it is of interest to detect a change in one or a few of the processes. We propose a bivariate TBE (BTBE) chart which is able to signal in real time. We derive analytical expressions for the control limits and average time-to-signal performance, conduct a performance evaluation and compare our chart to an existing method. The findings showed that our method is a realistic approach to monitor bivariate time-betweenevent data, and has better detection ability than existing methods. A large benefit of our method is that it signals in real-time and that due to the analytical expressions no simulation is needed. The proposed method is implemented on a real-life dataset related to AIDS.

show abstract

“…, β p ) ⊤ and Λ 0 is a completely unspecified continuous baseline cumulative hazard rate function. Note that if one is interested in the cross-ratio function estimation, the method proposed in [14] is applicable for such censored and truncated data. We here focus on the estimation for the above hazard rate function for T , which is the main interest in this application [29].…”

Section: Introductionmentioning

confidence: 99%

A proportional hazards model for time-to-event data with epidemiological bias

Zhang

Dai

2016

Journal of Multivariate Analysis

View full text Add to dashboard Cite

In hepatitis C virus (HCV) epidemiological studies, the estimation of progression to cirrhosis and prognostic effects of associated risk factors is of particular importance when projecting national disease burden. However, the progression estimates obtained from conventional methods could be distorted due to a referral bias [11]. In recent years, several approaches have been developed to handle this epidemiological bias in analyzing time-to-event data. This paper proposes a new estimation approach for this problem under a semiparametric proportional hazards framework. The new method uses a martingale approach based on the mean rate function, rather than the traditional hazard rate function, and develops an iterative algorithm to estimate the Cox regression parameter and baseline hazard rate simultaneously. The consistency and asymptotic properties of the proposed estimators are derived theoretically and evaluated via simulation studies. The new * Corresponding author. Email address: hdaia@essex.ac.uk (Hongsheng Dai).

show abstract

Cross-ratio estimation for bivariate failure times with left truncation

Cited by 7 publications

References 27 publications

Proportional cross-ratio model

Proportional cross-ratio model

A Real Time Monitoring Approach for Bivariate Event Data

A proportional hazards model for time-to-event data with epidemiological bias

Contact Info

Product

Resources

About