Interval-Censoring in Biomedical and Biopharmaceutical Clinical Trials

Ding-Geng Chen; Karl E. Peace; Lili Yu; Y. L. Lio; Yibin Wang

Interval-Censoring in Biomedical and Biopharmaceutical Clinical Trials

Ding-Geng Chen, Karl E. Peace, Lili Yu, Y. L. Lio, Yibin Wang

Biostatistics, Epidemiology, & Environmental Health Sciences

Research output: Contribution to book or proceeding › Chapter

Abstract

<div class="line" id="line-30"> In some biomedical clinical trials, especially cancer clinical trials, time-to-event data are generated from cancer scan within a speci&filig;c interval (such as 1 or more months) resulting in interval-censored data. The common practice in analyzing data from this type of trial is to approximate the interval-censored data using the midpoint or right endpoint (i.e. the &filig;rst observed time) of the interval so that well-known statistical methods developed for right-censored data may be used for the requisite analyses. This could introduce bias and lead to erroneous conclusions. In this paper we conduct a simulation study to investigate the bias in parameter estimation and inference. Appropriate statistical models for interval-censored data are proposed with application to breast cancer clinical trial data.</div>

Original language	American English
Title of host publication	Proceedings of the International Conference on Bioinformatics and Computational Biology
State	Published - Jul 2010

Keywords

Cox model
bias
interval-censoring
time-to-event data

DC Disciplines

Biostatistics
Public Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@inbook{d5a5da7f8e2a476aa65af07d5add12d2,

title = "Interval-Censoring in Biomedical and Biopharmaceutical Clinical Trials",

abstract = " In some biomedical clinical trials, especially cancer clinical trials, time-to-event data are generated from cancer scan within a speci&filig;c interval (such as 1 or more months) resulting in interval-censored data. The common practice in analyzing data from this type of trial is to approximate the interval-censored data using the midpoint or right endpoint (i.e. the &filig;rst observed time) of the interval so that well-known statistical methods developed for right-censored data may be used for the requisite analyses. This could introduce bias and lead to erroneous conclusions. In this paper we conduct a simulation study to investigate the bias in parameter estimation and inference. Appropriate statistical models for interval-censored data are proposed with application to breast cancer clinical trial data.",

keywords = "Cox model, bias, interval-censoring, time-to-event data",

author = "Ding-Geng Chen and Peace, {Karl E.} and Lili Yu and Lio, {Y. L.} and Yibin Wang",

year = "2010",

month = jul,

language = "American English",

booktitle = "Proceedings of the International Conference on Bioinformatics and Computational Biology",

}

TY - CHAP

T1 - Interval-Censoring in Biomedical and Biopharmaceutical Clinical Trials

AU - Chen, Ding-Geng

AU - Peace, Karl E.

AU - Yu, Lili

AU - Lio, Y. L.

AU - Wang, Yibin

PY - 2010/7

Y1 - 2010/7

N2 - In some biomedical clinical trials, especially cancer clinical trials, time-to-event data are generated from cancer scan within a speci&filig;c interval (such as 1 or more months) resulting in interval-censored data. The common practice in analyzing data from this type of trial is to approximate the interval-censored data using the midpoint or right endpoint (i.e. the &filig;rst observed time) of the interval so that well-known statistical methods developed for right-censored data may be used for the requisite analyses. This could introduce bias and lead to erroneous conclusions. In this paper we conduct a simulation study to investigate the bias in parameter estimation and inference. Appropriate statistical models for interval-censored data are proposed with application to breast cancer clinical trial data.

AB - In some biomedical clinical trials, especially cancer clinical trials, time-to-event data are generated from cancer scan within a speci&filig;c interval (such as 1 or more months) resulting in interval-censored data. The common practice in analyzing data from this type of trial is to approximate the interval-censored data using the midpoint or right endpoint (i.e. the &filig;rst observed time) of the interval so that well-known statistical methods developed for right-censored data may be used for the requisite analyses. This could introduce bias and lead to erroneous conclusions. In this paper we conduct a simulation study to investigate the bias in parameter estimation and inference. Appropriate statistical models for interval-censored data are proposed with application to breast cancer clinical trial data.

KW - Cox model

KW - bias

KW - interval-censoring

KW - time-to-event data

UR - https://www.researchgate.net/publication/221051760

M3 - Chapter

BT - Proceedings of the International Conference on Bioinformatics and Computational Biology

ER -

Interval-Censoring in Biomedical and Biopharmaceutical Clinical Trials

Abstract

Keywords

DC Disciplines

UN SDGs

Other files and links

Fingerprint

Cite this