Censoring Issues in Survival Analysis

Leung, Kwan‐Moon; Elashoff, Robert M.; Afifi, Abdelmonem A.

doi:10.1146/annurev.publhealth.18.1.83

Cited by 267 publications

(198 citation statements)

References 39 publications

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“…Patients who died causes other than GIST, or who were alive when the study was ended (2001), were rightcensored. 26 The model assumptions were tested and found valid for all patients. 27 All analyses were adjusted for sex and age.…”

Section: Survival Analysismentioning

confidence: 99%

Expression profiling of GIST: CD133 is associated with KIT exon 11 mutations, gastric location and poor prognosis

Arne¹,

Kristiansson²,

Nerman³

et al. 2011

Intl Journal of Cancer

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In gastrointestinal stromal tumors (GISTs), KIT exon 11 deletions are associated with poor prognosis. The aim of this study was to determine the gene expression profiles of GISTs carrying KIT exon 11 deletions and to identify genes associated with poor prognosis. Expression profiling was performed on nine tumors with KIT exon 11 deletions and 7 without KIT exon 11 mutations using oligonucleotide microarrays. In addition, gene expression profiles for 35 GISTs were analyzed by metaanalysis. Expression of CD133 (prominin-1) protein was examined by tissue microarray (TMA) analysis of 204 GISTs from a population-based study in western Sweden. Survival analysis was performed on patients subjected to R0 resection (n 5 180) using the Cox proportional hazards model. Gene expression profiling, meta-analysis, and qPCR showed up regulation of CD133 in GISTs carrying KIT exon 11 deletions. Immunohistochemical analysis on TMA confirmed CD133 expression in 28% of all tumors. CD133 positivity was more frequent in gastric GISTs (48%) than in small intestinal GISTs (4%). CD133 positivity was also more frequent in GISTs with KIT exon 11 mutations (41%) than in tumors with mutations in KIT exon 9, plateletderived growth factor receptor a (PDGFRA), or wild-type tumors (0-17%). Univariate survival analysis showed a significant correlation between the presence of CD133 protein and shorter overall survival (hazard ratio 5 2.23, p 5 0.027). Multivariate analysis showed that CD133 provided additional information on patient survival compared to age, sex, National Institutes of Health (NIH) risk group and mutational status. CD133 is expressed in a subset of predominantly gastric GISTs with KIT exon 11 mutations and poor prognosis.

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Section: Survival Analysismentioning

confidence: 99%

Expression profiling of GIST: CD133 is associated with KIT exon 11 mutations, gastric location and poor prognosis

Arne¹,

Kristiansson²,

Nerman³

et al. 2011

Intl Journal of Cancer

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“…As demonstrated in Figure 1, the former would underestimate and the latter would overestimate the median and point estimates. Although this issue has been discussed extensively in the statistical literature from the theoretical perspective [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] and for randomized phase III studies comparing two or more therapies, 18,19 there are few empirical data to demonstrate the magnitude of the bias for various amounts of informative censoring in the phase II setting. In this article, we describe situations in which informative censoring may occur, evaluate the impact on the estimated quantities via simulation, and recommend guidelines for design, analysis, and end point definition in the context of phase II clinical trials.…”

Section: Introductionmentioning

confidence: 99%

Impact of Informative Censoring on the Kaplan-Meier Estimate of Progression-Free Survival in Phase II Clinical Trials

Campigotto

Weller

2014

JCO

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Informative censoring in a progression-free survival (PFS) analysis arises when patients are censored for initiation of an effective anticancer treatment before the protocol-defined progression, and these patients are at a different risk for treatment failure than those who continue on therapy. This may cause bias in the estimated PFS when using the Kaplan-Meier method for analysis. Although there are several articles that discuss this issue from a theoretical perspective or in randomized phase III studies, there are little data to demonstrate the magnitude of the bias on the estimated quantities from a phase II trial. This article describes the issues by using two oncology phase II trials as examples, evaluates the impact of the bias using simulations, and provides recommendations. The two trials were selected because they demonstrate two different reasons for censoring. Simulations show that the magnitude of the bias depends primarily on the proportion of patients who are informatively censored and secondarily on the hazard ratio between the group of patients who remain on study and the group of patients who are informatively censored. Recommendations include using an alternative end point, which includes inadequate response and initial signs of clinical progression as treatment failure, and a competing risk analysis for studies in which competing events preclude or modify the probability of observing the primary event of interest. If informative censoring cannot be avoided, then all patients should be observed until progression, and sensitivity analyses should be used as appropriate.

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“…A two-sided P value of Ͻ0.05 was considered statistically significant. The log-rank test was used to estimate the equality of survival functions (24). To adjust for potential bias, a propensity score that reflected the probability that a patient would receive ASB therapy was generated.…”

Section: Methodsmentioning

confidence: 99%

Antistaphylococcal β-Lactams versus Vancomycin for Treatment of Infective Endocarditis Due to Methicillin-Susceptible Coagulase-Negative Staphylococci: a Prospective Cohort Study from the International Collaboration on Endocarditis

Carugati¹,

Petti²,

Arnold³

et al. 2016

Antimicrob Agents Chemother

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Of the 7,587 patients in the two databases, there were 280 patients with methicillin-susceptible CoNS IE. Detailed treatment and outcome data were available for 180 patients. Eighty-eight patients received ASB, while 36 were treated with VAN. In-hospital mortality (19.3% versus 11.1%; P ‫؍‬ 0.27), 6-month mortality (31.6% versus 25.9%; P ‫؍‬ 0.58), and survival time after discharge (P ‫؍‬ 0.26) did not significantly differ between the two cohorts. Cox regression analysis did not show any significant association between ASB use and the survival time (hazard ratio, 1.7; P ‫؍‬ 0.22); this result was not affected by adjustment for confounders. This study provides no evidence for a difference in outcome with the use of VAN versus ASB for methicillin-susceptible CoNS IE.

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Censoring Issues in Survival Analysis

Cited by 267 publications

References 39 publications

Expression profiling of GIST: CD133 is associated with KIT exon 11 mutations, gastric location and poor prognosis

Expression profiling of GIST: CD133 is associated with KIT exon 11 mutations, gastric location and poor prognosis

Impact of Informative Censoring on the Kaplan-Meier Estimate of Progression-Free Survival in Phase II Clinical Trials

Antistaphylococcal β-Lactams versus Vancomycin for Treatment of Infective Endocarditis Due to Methicillin-Susceptible Coagulase-Negative Staphylococci: a Prospective Cohort Study from the International Collaboration on Endocarditis

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