Interim monitoring using the adaptively weighted log‐rank test in clinical trials for survival outcomes

Yang, Song

doi:10.1002/sim.7958

Cited by 6 publications

(7 citation statements)

References 25 publications

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“…Often, its empirical size is greater than the nominal level, especially under small sample size (see the Supplementary Material). In a recent work of Yang, 17 which deals with interim monitoring using adaptive weighted log-rank test, the author suggests using the re-sampling method of Lin et al. 18 instead of the original asymptotic approach, 18 for improving the type I error rate.…”

Section: Simulation Studymentioning

confidence: 99%

K-sample omnibus non-proportional hazards tests based on right-censored data

Gorfine

Schlesinger

Hsu

2020

Stat Methods Med Res

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This work presents novel and powerful tests for comparing nonproportional hazard functions, based on sample-space partitions. Right censoring introduces two major difficulties which make the existing sample-space partition tests for uncensored data non-applicable: (i) the actual event times of censored observations are unknown; and (ii) the standard permutation procedure is invalid in case the censoring distributions of the groups are unequal. We overcome these two obstacles, introduce invariant tests, and prove their consistency. Extensive simulations reveal that under non-proportional alternatives, the proposed tests are often of higher power compared with existing popular tests for non-proportional hazards. Efficient implementation of our tests is available in the R package KONPsurv, which can be freely downloaded from https://github.com/matan-schles/KONPsurv.

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Section: Simulation Studymentioning

confidence: 99%

K-sample omnibus non-proportional hazards tests based on right-censored data

Gorfine

Schlesinger

Hsu

2020

Stat Methods Med Res

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“…Let us look at the interim data from SPRINT at the last 3 looks, collected at roughly the information time of 0.30, 0.41, and 0.54. If we use a spending function that takes the values .00024, .00139, and .00535 at those 3 looks, the values used in the trial, then it was shown that the adaptively weighted log‐rank test would cross the boundary at the last 2 looks, agreeing with the log‐rank test and the test used in the trial …”

Section: Examplesmentioning

confidence: 74%

“…It was noted in the literature that, for some cases with moderate sample size, the approach of interpolation with tabular values for the adaptively weighted log‐rank test may result in somewhat inflated size. In Yang a resampling method was used to calculate the critical value, which results in better control of the type 1 error rate while still maintaining overall improvement of the log‐rank test. Interim testing procedures were also developed for group sequential monitoring of clinical trials using the adaptively weighted log‐rank test.…”

Section: Testing the Treatment Effectmentioning

confidence: 99%

“…Denote the adaptively weighted log‐rank test by LRAD, with critical values obtained from the resampling method, and denote the Wald tests based on estimators of the average hazard ratios in and by A H R , A H R F , respectively. Denote the Wald tests based on nonparametric and model‐based estimators of R S P R τ in by R S P R N and R S P R M , respectively.…”

Section: Simulation Studiesmentioning

confidence: 99%

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Improving testing and description of treatment effect in clinical trials with survival outcomes

Yang

2018

Statistics in Medicine

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Cox model inference and the log-rank test have been the cornerstones for design and analysis of clinical trials with survival outcomes. In this article, we summarize some recently developed methods for analyzing survival data when the hazards may possibly be nonproportional and also propose some new estimators for summary measures of the treatment effect. These methods utilize the short-term and long-term hazard ratio model proposed in Yang and Prentice (2005), which contains the Cox model and also accommodates various nonproportional hazards scenarios. Without the proportional hazards assumption, these methods often improve the log-rank test and inference procedures based on the Cox model, as well as nonparametric procedures currently available in the literature. The proposed methods have sound theoretical justifications and can be computed quickly. R codes for implementing them are available. Detailed illustrations with 3 clinical trials are provided.

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“… 21 The Kaplan–Meier method was adopted for survival analysis, and the Log rank test was used to test the significance of differences between groups. 22 , 23 The factors affecting the prognosis of RC were measured by univariate and multivariate Cox regression models. 24 Multivariate analysis was conducted with all factors of P < 0.05 in the univariate.…”

Section: Methodsmentioning

confidence: 99%

Combination of Changes in CEA and CA199 Concentration After Neoadjuvant Chemoradiotherapy Could Predict the Prognosis of Stage II/III Rectal Cancer Patients Receiving Neoadjuvant Chemoradiotherapy Followed by Total Mesorectal Excision

Zhao¹,

Zhao²,

Jia³

et al. 2022

CMAR

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Background Previous studies have shown that the levels of serum tumor markers CEA and CA19-9 were related to chemoradiotherapy. Therefore, it has been assumed that dynamic monitoring of these markers could predict the prognosis of stage II/III rectal cancer (RC). Therefore, this study proposed to evaluate the prognostic value of changes in serum tumor biomarkers for stage II/III RC patients undergoing neoadjuvant chemoradiotherapy (NCRT) followed by total mesorectal excision (TME). Methods A total of 217 patients with stage II/III RC receiving NCRT followed by TME were retrospectively analyzed. Serum CEA and CA199 levels were measured within one week before NCRT and one week before TME. The optimal cut-off points of ∆CEA% and ∆CA199% for prognosis prediction were calculated by receiver operating characteristics (ROC) analysis. Independent prognostic predictors were identified by univariate and multivariate Cox regression analyses. To avoid the efficiency of ∆CEA% and ∆CA199% on serum tumor biomarker change (STBC) score, two models including and excluding ∆CEA% and ∆CA199% were established separately in multivariate analysis. Results The optimal cut-off point for ∆CEA% and ∆CA199% were −30.29% and 20.30%, respectively. Univariate analysis showed that ∆CEA%, ∆CA199%, STBC score, ypT staging and yN staging could predict OS. ypT staging and STBC score could predict DFS. In multivariate analysis, only ∆CA199% (HR = 0.468, 95% CI: 0.220–0.994, p = 0.048), ypT staging (HR = 0.420, 95% CI: 0.182–0.970, p = 0.042), and STBC score (HR = 0.204, 95% CI: 0.078–0.532, p = 0.001) were independently related to OS; and STBC score (HR = 0.412, 95% CI: 0.216–0.785, p=0.007) and ypT staging (HR = 0.421, 95% CI: 0.224–0.792, p = 0.007) were independently related to DFS. Conclusion We established a combined STBC score to predict the prognosis of stage II/III RC patients receiving NCRT followed by TME. The predictive value of the combined score was stronger than a single marker alone and even stronger than several pathological indicators.

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Interim monitoring using the adaptively weighted log‐rank test in clinical trials for survival outcomes

Cited by 6 publications

References 25 publications

K-sample omnibus non-proportional hazards tests based on right-censored data

K-sample omnibus non-proportional hazards tests based on right-censored data

Improving testing and description of treatment effect in clinical trials with survival outcomes

Combination of Changes in CEA and CA199 Concentration After Neoadjuvant Chemoradiotherapy Could Predict the Prognosis of Stage II/III Rectal Cancer Patients Receiving Neoadjuvant Chemoradiotherapy Followed by Total Mesorectal Excision

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