THREE TESTING PROCEDURES FOR A COMPETING RISKS MODEL

1995 ◽  
Vol 02 (02) ◽  
pp. 133-146
Author(s):  
DANIEL Y.T. FONG ◽  
PAUL YIP
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Competing Risks ◽  
Linear Regression Analysis ◽  
Wald Test ◽  
Ratio Test ◽  
Multivariate Linear Regression Analysis ◽  
Testing Procedures ◽  
Failure Type ◽  
Competing Risks Model

This paper is concerned with testing for the equality of failure rates in a competing risks model with two risks. Three testing procedures are investigated, namely Score test, Likelihood Ratio test and Wald test. Wald test has been considered to be the most powerful in multivariate linear regression analysis.1 However in our application, Wald test is the most efficient one when the failure rate of the second failure type is strictly smaller than the first failure type, otherwise the Score or the Likelihood Ratio test is preferred. This phenomenon is illustrated by data from a mechanical-switch life test.2 The results has been extended to a k-competing risks model. A simulation study is also given to examine the performance of the three tests.

A global test for competing risks survival analysis

2020 ◽  
Vol 29 (12) ◽  
pp. 3666-3683
Author(s):  
Dominic Edelmann ◽  
Maral Saadati ◽  
Hein Putter ◽  
Jelle Goeman
Keyword(s):  
Survival Analysis ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Competing Risks ◽  
Cox Model ◽  
Wald Test ◽  
Ratio Test ◽  
Type I ◽  
Multistate Models ◽  
Global Test

Standard tests for the Cox model, such as the likelihood ratio test or the Wald test, do not perform well in situations, where the number of covariates is substantially higher than the number of observed events. This issue is perpetuated in competing risks settings, where the number of observed occurrences for each event type is usually rather small. Yet, appropriate testing methodology for competing risks survival analysis with few events per variable is missing. In this article, we show how to extend the global test for survival by Goeman et al. to competing risks and multistate models[Per journal style, abstracts should not have reference citations. Therefore, can you kindly delete this reference citation.]. Conducting detailed simulation studies, we show that both for type I error control and for power, the novel test outperforms the likelihood ratio test and the Wald test based on the cause-specific hazards model in settings where the number of events is small compared to the number of covariates. The benefit of the global tests for competing risks survival analysis and multistate models is further demonstrated in real data examples of cancer patients from the European Society for Blood and Marrow Transplantation.

scholarly journals Power comparison of Rao′s score test, the Wald test and the likelihood ratio test in (2xc) contingency tables

2015 ◽  
Vol 52 (2) ◽  
pp. 95-104
Author(s):  
Anita Dobek ◽  
Krzysztof Moliński ◽  
Ewa Skotarczak
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Contingency Tables ◽  
Score Test ◽  
Wald Test ◽  
Likelihood Ratio Tests ◽  
Ratio Test ◽  
Power Comparison ◽  
Testing Hypotheses

Abstract There are several statistics for testing hypotheses concerning the independence of the distributions represented by two rows in contingency tables. The most famous are Rao′s score, the Wald and the likelihood ratio tests. A comparison of the power of these tests indicates the Wald test as the most powerful.

scholarly journals Detecting Differential Item Functioning Using the Logistic Regression Procedure in Small Samples

2016 ◽  
Vol 41 (1) ◽  
pp. 30-43 ◽  
Author(s):  
Sunbok Lee
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Wald Test ◽  
Small Samples ◽  
Ratio Test ◽  
Chi Square ◽  
Ml Estimation ◽  
Item Functioning

The logistic regression (LR) procedure for testing differential item functioning (DIF) typically depends on the asymptotic sampling distributions. The likelihood ratio test (LRT) usually relies on the asymptotic chi-square distribution. Also, the Wald test is typically based on the asymptotic normality of the maximum likelihood (ML) estimation, and the Wald statistic is tested using the asymptotic chi-square distribution. However, in small samples, the asymptotic assumptions may not work well. The penalized maximum likelihood (PML) estimation removes the first-order finite sample bias from the ML estimation, and the bootstrap method constructs the empirical sampling distribution. This study compares the performances of the LR procedures based on the LRT, Wald test, penalized likelihood ratio test (PLRT), and bootstrap likelihood ratio test (BLRT) in terms of the statistical power and type I error for testing uniform and non-uniform DIF. The result of the simulation study shows that the LRT with the asymptotic chi-square distribution works well even in small samples.

A likelihood ratio test for detecting patterns of disease-marker association

1997 ◽  
Vol 61 (4) ◽  
pp. 335-350 ◽  
Author(s):  
A. P. MORRIS ◽  
J. C. WHITTAKER ◽  
R. N. CURNOW
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Ratio Test ◽  
Disease Marker
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