scholarly journals Subgroup detection and sample size calculation with proportional hazards regression for survival data

2017 ◽  
Vol 36 (29) ◽  
pp. 4646-4659 ◽  
Author(s):  
Suhyun Kang ◽  
Wenbin Lu ◽  
Rui Song
Keyword(s):  
Sample Size ◽  
Survival Data ◽  
Proportional Hazards ◽  
Sample Size Calculation ◽  
Proportional Hazards Regression

A unified approach to power and sample size determination for log-rank tests under proportional and nonproportional hazards

2021 ◽  
pp. 096228022098857
Author(s):  
Yongqiang Tang
Keyword(s):  
Sample Size ◽  
Proportional Hazards ◽  
Sample Size Calculation ◽  
Unified Approach ◽  
Rank Tests ◽  
Trial Duration ◽  
Nonproportional Hazards ◽  
Equivalence Trials ◽  
Superiority Trials ◽  
Log Rank Tests

Log-rank tests have been widely used to compare two survival curves in biomedical research. We describe a unified approach to power and sample size calculation for the unweighted and weighted log-rank tests in superiority, noninferiority and equivalence trials. It is suitable for both time-driven and event-driven trials. A numerical algorithm is suggested. It allows flexible specification of the patient accrual distribution, baseline hazards, and proportional or nonproportional hazards patterns, and enables efficient sample size calculation when there are a range of choices for the patient accrual pattern and trial duration. A confidence interval method is proposed for the trial duration of an event-driven trial. We point out potential issues with several popular sample size formulae. Under proportional hazards, the power of a survival trial is commonly believed to be determined by the number of observed events. The belief is roughly valid for noninferiority and equivalence trials with similar survival and censoring distributions between two groups, and for superiority trials with balanced group sizes. In unbalanced superiority trials, the power depends also on other factors such as data maturity. Surprisingly, the log-rank test usually yields slightly higher power than the Wald test from the Cox model under proportional hazards in simulations. We consider various nonproportional hazards patterns induced by delayed effects, cure fractions, and/or treatment switching. Explicit power formulae are derived for the combination test that takes the maximum of two or more weighted log-rank tests to handle uncertain nonproportional hazards patterns. Numerical examples are presented for illustration.

Sample Size Calculation for Survival Data

2012 ◽  
pp. 1200-1205
Author(s):  
Qi Jiang ◽  
Steven Snapinn ◽  
Boris Iglewicz
Keyword(s):  
Sample Size ◽  
Survival Data ◽  
Sample Size Calculation

Effect of HPV Status on Survival of Oropharynx Cancer with Distant Metastasis

2020 ◽  
Vol 163 (2) ◽  
pp. 372-374 ◽  
Author(s):  
Adam W. Kaplon ◽  
Thomas J. Galloway ◽  
Mihir K. Bhayani ◽  
Jeffrey C. Liu
Keyword(s):  
Distant Metastasis ◽  
Survival Data ◽  
Regression Models ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
National Cancer Database ◽  
Proportional Hazards Regression ◽  
Kaplan Meier ◽  
Hpv Status ◽  
Oropharynx Squamous Cell Carcinoma

Human papillomavirus (HPV)–positive oropharynx squamous cell carcinoma (OPSCC) is known to have improved survival over HPV-negative disease. However, it is largely unknown whether HPV status similarly affects survival in patients presenting with distant metastatic disease. We queried the National Cancer Database for OPSCC with distant metastasis. Kaplan-Meier curves and Cox proportional hazards regression models controlling for relevant demographics were used to evaluate overall survival. In total, 768 OPSCC cases were available for analysis with HPV and survival data: 50% of cases were HPV negative and 50% were HPV positive. The 1- and 2-year survival for HPV-negative disease was 49% and 27%, respectively, as compared with 67% and 42% in the HPV-positive cohort. HPV positivity was associated with improved median survival in treated and untreated patients. Age, comorbidities, and HPV status were predictive of improved survival on multivariate analysis. HPV-positive OPSCC has improved survival in the setting of distant metastatic presentation as compared with HPV-negative disease and shows greater responsiveness to treatment.

scholarly journals Sample size calculation for studies with grouped survival data

2018 ◽  
Vol 37 (27) ◽  
pp. 3904-3917
Author(s):  
Zhiguo Li ◽  
Xiaofei Wang ◽  
Yuan Wu ◽  
Kouros Owzar
Keyword(s):  
Sample Size ◽  
Survival Data ◽  
Sample Size Calculation

Sample Size Calculation for Survival Data

2003 ◽  
pp. 892-898 ◽  
Author(s):  
Qi Jiang ◽  
Steven Snapinn ◽  
Boris lglewicz
Keyword(s):  
Sample Size ◽  
Survival Data ◽  
Sample Size Calculation

Sample Size Calculation for Survival Data

2010 ◽  
pp. 1200-1205
Author(s):  
Qi Jiang ◽  
Steven Snapinn ◽  
Boris Iglewicz
Keyword(s):  
Sample Size ◽  
Survival Data ◽  
Sample Size Calculation

scholarly journals Gene selection in Cox regression model based on a new adaptive penalized method

2020 ◽  
Vol 8 (1) ◽  
pp. 16
Author(s):  
Oday Isam Alskal ◽  
Zakariya Yahya Algamal
Keyword(s):  
Regression Model ◽  
Survival Data ◽  
Proportional Hazards ◽  
Gene Selection ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression ◽  
Selection Operator

The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Cox proportional hazards regression model is the most popular model in regression analysis for censored survival data. In this paper, an adaptive penalized Cox proportional hazards regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox proportional hazards regression model with the weighted least absolute shrinkage and selection operator (LASSO) method. Experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes.  

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