A log rank test for clustered data with informative within‐cluster group size

Gregg, Mary; Datta, Somnath; Lorenz, D.

doi:10.1002/sim.7899

Cited by 9 publications

(11 citation statements)

References 21 publications

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“…Combined with the prognostic information of the samples, Kaplan–Meier (KM) survival analysis [ 38 ] was carried out. The log-rank test [ 39 ] was used to calculate P values. A P value < 0.05 indicated a significant correlation.…”

Section: Methodsmentioning

confidence: 99%

GPC3 affects the prognosis of lung adenocarcinoma and lung squamous cell carcinoma

Ning

Jiang

et al. 2021

BMC Pulm Med

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Background Glypican 3 (GPC3) is a heparin sulphate proteoglycan whose expression is associated with several malignancies. However, its expression in non-small-cell lung carcinoma (NSCLC) is limited and ambiguous. This study aimed to comprehensively evaluate the expression of GPC3 in NSCLC and develop a risk-score model for predicting the prognosis of NSCLC. Methods The gene expression profiles of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) were downloaded from the UCSC Xena database. Using the limma package, the differentially expressed genes (DEGs) between different comparison groups were analysed and the differential expression of GPC3 was calculated. A functional enrichment analysis was conducted for GPC3-associated genes using the DAVID tool. For the GPC3-associated genes shared by the four comparison groups, a protein–protein interaction network was built using the Cytoscape software. After conducting a survival analysis and a Cox regression analysis, the genes found to be significantly correlated with prognosis were selected to construct a risk-score model. Besides, the gene and protein levels of GPC3 were examined by quantitative reverse transcriptase-PCR (qRT-PCR) and immunohistochemistry (IHC) in LUSC tissues and paracancer tissues. Results The differential expression of GPC3 was significant (adjusted P < 0.05) in the NSCLC vs. normal, LUAD vs. normal, LUSC versus normal, and LUAD versus. LUSC comparison groups. GPC3 directly interacted with SERPINA1, MFI2, and FOXM1. Moreover, GPC3 expression was significantly correlated with pathologic N, pathologic T, gender, and tumour stage in LUAD samples. Finally, the risk-score model (involving MFI2, FOXM1, and GPC3) for LUAD and that (involving SERPINA1 and FOXM1) for LUSC were established separately. The qRT-PCR result showed that GPC3 expression was much higher in the LUSC tissues than that in the normal group. The IHC results further showed that GPC3 is highly expressed in LUSC tissues, but low in paracancer tissues. Conclusion The three-gene risk-score model for LUAD and the two-gene risk-score model for LUSC might be valuable in improving the prognosis of these carcinomas.

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Section: Methodsmentioning

confidence: 99%

GPC3 affects the prognosis of lung adenocarcinoma and lung squamous cell carcinoma

Ning

Jiang

et al. 2021

BMC Pulm Med

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“…Kaplan-Meier survival analysis was employed to screen graft loss-related DFGs based on their expression levels in renal allograft patients [ 27 ]. The log-rank test was used to determine the p value using the R package ‘survminer’ [ 28 ]. A p -value of < 0.05 was considered as statistically significant.…”

Section: Methodsmentioning

confidence: 99%

A ferroptosis-related gene signature for graft loss prediction following renal allograft

et al. 2021

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Allogeneic kidney transplantation (renal allograft) is the most effective treatment for advanced kidney disease. Previous studies have indicated that ferroptosis participates in the progression of acute kidney injury and renal transplant failure. However, few studies have evaluated the prognostic value of ferroptosis on renal transplantation outcomes. In this study, a total of 32 differentially expressed ferroptosis-related genes (DFGs) were identified, which were mainly enriched in infection-related pathways. Next, a ferroptosis-related gene signature, including GA-binding protein transcription factor subunit beta 1 (GABPB1), cyclin-dependent kinase inhibitor 1A (CDKN1A), Toll-like receptor 4 (TLR4), C-X-C motif chemokine ligand 2 (CXCL2), caveolin 1 (CAV1), and ribonucleotide reductase subunit M2 (RRM2), was constructed to predict graft loss following renal allograft. Moreover, receiver operating characteristic (ROC) curves (area under the ROC curve [AUC] > 0.8) demonstrated the accuracy of the gene signature and univariate Cox analysis suggested that the gene signature could play an independent role in graft loss (p < 0.05). Furthermore, the nomogram and calibration plots also indicated the good prognostic capability of the gene signature. Finally, immune-related and cytokine signaling pathways were mostly enriched in renal allograft patients with poor outcomes. Considered together, a ferroptosis-related gene signature and nomogram based on DFGs were created to predict the 1-, 2and 3-year graft loss probability of renal allograft patients.The gene signature could serve as a valuable biomarker for predicting graft loss, contributing to improving the outcome of allogeneic kidney transplantation.

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“…2 Thus, we can test H 0 using a Wald-type test by comparing the standardized form z=true(p^−p0true)/true(v^/Mtrue) to appropriate percentiles of the standard normal distribution, where truev^ is some estimate of the variance of truep^. While a number of tests of marginal parameters for clustered data with ICS have been established using the asymptotic normality of cluster-weighted estimators, 3–7 none has been evaluated under competing variance estimation techniques. As such, we propose four methods of estimation for truev^ including two novel methods that have not previously been considered in the cluster-weighted context.…”

Section: Methodsmentioning

confidence: 99%

“…18 This group reweighting has been implemented in the derivation of clustered data analogues to the rank sum and log rank tests that are resistant to the biasing effects of IWCGS. 7,17 In this section, we develop a modification of our proposed tests under reweighting appropriate for IWCGS. For brevity, we present this adjustment only in the context of estimating a marginal proportion, but extensions to the goodness of fit and independence tests follow in much the same fashion.…”

Section: Reweighting For Informative Within-cluster Group Sizementioning

confidence: 99%

“…These tests apply a marginalization principal 2 shown to result in estimators that are unbiased in the presence of ICS and free from any assumptions related to the clustering mechanism, and include clustered data analogues of the well-known rank sum, 3 signed rank, 4 correlation, 5,6 and log rank tests. 7 This collection of tests notably excludes tests of categorical responses, analogous to well-known chi-square tests of proportions. Further, while the aforementioned tests for clustered data under ICS were developed using a common motivating principal, there are substantial differences in the variance estimation techniques implemented in these tests.…”

Section: Introductionmentioning

confidence: 99%

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Variance estimation in tests of clustered categorical data with informative cluster size

Gregg

Datta

Lorenz

2020

Stat Methods Med Res

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In the analysis of clustered data, inverse cluster size weighting has been shown to be resistant to the potentially biasing effects of informative cluster size, where the number of observations within a cluster is associated with the outcome variable of interest. The method of inverse cluster size reweighting has been implemented to establish clustered data analogues of common tests for independent data, but the method has yet to be extended to tests of categorical data. Many variance estimators have been implemented across established cluster-weighted tests, but potential effects of differing methods on test performance has not previously been explored. Here, we develop cluster-weighted estimators of marginal proportions that remain unbiased under informativeness, and derive analogues of three popular tests for clustered categorical data, the one-sample proportion, goodness of fit, and independence chi square tests. We construct these tests using several variance estimators and show substantial differences in the performance of cluster-weighted tests based on variance estimation technique, with variance estimators constructed under the null hypothesis maintaining size closest to nominal. We illustrate the proposed tests through an application to a data set of functional measures from patients with spinal cord injuries participating in a rehabilitation program.

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A log rank test for clustered data with informative within‐cluster group size

Cited by 9 publications

References 21 publications

GPC3 affects the prognosis of lung adenocarcinoma and lung squamous cell carcinoma

GPC3 affects the prognosis of lung adenocarcinoma and lung squamous cell carcinoma

A ferroptosis-related gene signature for graft loss prediction following renal allograft

Variance estimation in tests of clustered categorical data with informative cluster size

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