Logrank-type tests for comparing survival curves with interval-censored data

Kim, Jinheum; Kang, Dae Ryong; Nam, Chung Mo

doi:10.1016/j.csda.2005.06.014

Cited by 8 publications

(13 citation statements)

References 11 publications

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“…Of course, to apply it, one has to determine Ŝ 0 . Recently Kim et al [36] suggested a modification to the procedure that does not require Ŝ 0 by assuming that T i follows the uniform distribution over the observed interval.…”

Section: Comparison Of Survival Functionsmentioning

confidence: 99%

Interval censoring

Zhang

Sun

2009

Stat Methods Med Res

147

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Interval-censored failure time data occur in many medical investigations as well as other studies such as demographical and sociological studies. They include the usual right-censored failure time data as a special case but provide much more complex structure and less relevant information than the right-censored data. This article reviews some basic concepts, issues and the corresponding statistical approaches related to the analysis of interval-censored data as well as recent advances. In particular, we discuss estimation of a survival function, comparison of several treatments and regression analysis as well as competing risks analysis and truncation in the presence of interval censoring. A well-known example of interval-censored data is described and analysed to illustrate some of the statistical procedures discussed.

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Section: Comparison Of Survival Functionsmentioning

confidence: 99%

Interval censoring

Zhang

Sun

2009

Stat Methods Med Res

147

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“…Many studies have been conducted on the comparison of several survival functions with intervalcensored data. Among these, those using rank-based nonparametric tests include Self and Grossman (1986), Fay (1996), Sun (1996), Pan (2000), Zhao and Sun (2004) and Kim et al (2006). Zhao and Sun (2004) proposed a nonparametric test which is a generalization of the most commonly used log-rank test for right-censored data.…”

Section: Introductionmentioning

confidence: 99%

“…Their test has advantages over the test proposed in Sun (1996) since it applies to both discrete and continuous failure time data and reduces to the log-rank test for right-censored data. Unlike the test proposed by Zhao and Sun (2004), Kim et al (2006) proposed log-rank-type tests that do not require the use of an EM algorithm because they introduced uniform weights that would depend only on the size of the risk set at each observed time instead of the weights involving estimated survivals.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned in Sun (2006), when we have large amounts of sample data or continuous failure time data, estimating the marginal survivals is very time-consuming and nonuniqueness of the estimators could occur. In this paper, in order to develop a test which is easily implemented and is also applicable to both discrete failure time and continuous failure time data, we extend the test by Kim et al (2006) to doubly interval-censored data.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 2, we propose a new test procedure based on ideas similar to those used in Kim et al (2006). In Section 3, a simulation study is carried out to investigate the finite-sample performance of the proposed test and compare it with Sun's test (Sun, 2006) and the proposed test is illustrated with the AIDS cohort study data discussed in Kim et al (1993).…”

Section: Introductionmentioning

confidence: 99%

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A Generalized Log‐Rank‐Type Test for Comparing Survivals with Doubly Interval‐Censored Data

Kim

Nam

2009

Biometrical J

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In doubly interval-censored data, the survival time of interest is defined as the elapsed time between an initial event and a subsequent event, but the occurrences of both events cannot be observed exactly. Instead, only right- or interval-censored observations on the occurrence times are available. Our purpose is to develop a generalized log-rank-type test for comparing survival functions of several groups. For the same problem, Sun (The Statistical Analysis of Interval-censored Failure Time Data, Springer, New York) suggested a nonparametric test based on the estimated marginal survival functions of the two related events. We consider a new method using uniform weights, which depend only on the size of the risk set at each observed time. The proposed method does not require the estimation of marginal survival functions and furthermore can reduce to the log-rank test for right-censored data. Results from a simulation study show that our test performs well in terms of size and power. We analyze the AIDS cohort study data taken from Kim et al. (Biometrics 49, 13-22).

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