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Analysis for Time-To-Event Data Under Censoring and Truncation

by Hongsheng Dai

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A focused introduction to censored and truncated survival data

$93.51 USD
$93.51 USD $93.51 USD

Paperback New English

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AlbakiReads Editorial

A focused introduction to censored and truncated survival data

This concise statistics text examines survival-analysis data in which event times are only partly observed or shaped by study-entry and observation rules. It centers on estimating univariate and bivariate survival functions under truncation, while distinguishing censoring from forms of truncation tied to selection in study design. The discussion includes left truncation associated with delayed entry and right truncation, where observation is limited to events occurring before a specified point. Across these cases, the book frames selection bias as a central analytical concern when estimating time-to-event outcomes and the effects of risk factors.

Good fit for readers who enjoy

  • Students and researchers studying survival analysis
  • Statisticians working with time-to-event data
  • Researchers confronting censoring, delayed entry, or selection bias

Themes

Survival analysisCensoringTruncationSelection biasUnivariate and bivariate survival functions

Based on publisher information and book metadata.

Book overview

Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design.

Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors.

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