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.

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 The Weighting is the Hardest Part: On the Behavior of the Likelihood Ratio Test and the Score Test Under a Data-Driven Weighting Scheme in Sequenced Samples

2017 ◽  
Vol 20 (2) ◽  
pp. 108-118 ◽  
Author(s):  
Camelia C. Minică ◽  
Giulio Genovese ◽  
Christina M. Hultman ◽  
René Pool ◽  
Jacqueline M. Vink ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Association Studies ◽  
Score Test ◽  
Weighting Scheme ◽  
Data Driven ◽  
Ratio Test ◽  
Sequencing Data ◽  
Exome Sequencing Data

Sequence-based association studies are at a critical inflexion point with the increasing availability of exome-sequencing data. A popular test of association is the sequence kernel association test (SKAT). Weights are embedded within SKAT to reflect the hypothesized contribution of the variants to the trait variance. Because the true weights are generally unknown, and so are subject to misspecification, we examined the efficiency of a data-driven weighting scheme. We propose the use of a set of theoretically defensible weighting schemes, of which, we assume, the one that gives the largest test statistic is likely to capture best the allele frequency–functional effect relationship. We show that the use of alternative weights obviates the need to impose arbitrary frequency thresholds. As both the score test and the likelihood ratio test (LRT) may be used in this context, and may differ in power, we characterize the behavior of both tests. The two tests have equal power, if the weights in the set included weights resembling the correct ones. However, if the weights are badly specified, the LRT shows superior power (due to its robustness to misspecification). With this data-driven weighting procedure the LRT detected significant signal in genes located in regions already confirmed as associated with schizophrenia — the PRRC2A (p = 1.020e-06) and the VARS2 (p = 2.383e-06) — in the Swedish schizophrenia case-control cohort of 11,040 individuals with exome-sequencing data. The score test is currently preferred for its computational efficiency and power. Indeed, assuming correct specification, in some circumstances, the score test is the most powerful test. However, LRT has the advantageous properties of being generally more robust and more powerful under weight misspecification. This is an important result given that, arguably, misspecified models are likely to be the rule rather than the exception in weighting-based approaches.

Multiway Contingency Tables: Monte Carlo Resampling Probability Values for the Chi-Squared and Likelihood-Ratio Tests

2010 ◽  
Vol 107 (2) ◽  
pp. 501-510 ◽  
Author(s):  
Michael A. Long ◽  
Kenneth J. Berry ◽  
Paul W. Mielke
Keyword(s):  
Monte Carlo ◽  
Likelihood Ratio ◽  
Contingency Table ◽  
Contingency Tables ◽  
Likelihood Ratio Tests ◽  
Ratio Test ◽  
Test Statistics ◽  
Test Statistic ◽  
Resampling Methods ◽  
Chi Squared

Monte Carlo resampling methods to obtain probability values for chi-squared and likelihood-ratio test statistics for multiway contingency tables are presented. A resampling algorithm provides random arrangements of cell frequencies in a multiway contingency table, given fixed marginal frequency totals. Probability values are obtained from the proportion of resampled test statistic values equal to or greater than the observed test statistic value.

Homogeneity testing for binomial proportions under stratified double-sampling scheme with two fallible classifiers

2020 ◽  
Vol 29 (12) ◽  
pp. 3547-3568
Author(s):  
Shi-Fang Qiu ◽  
Qi-Xiang Fu
Keyword(s):  
Least Squares ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Weighted Least Squares ◽  
Score Test ◽  
Double Sampling ◽  
Ratio Test ◽  
Sample Sizes ◽  
Log Transformation ◽  
Binomial Proportions

This article investigates the homogeneity testing problem of binomial proportions for stratified partially validated data obtained by double-sampling method with two fallible classifiers. Several test procedures, including the weighted-least-squares test with/without log-transformation, logit-transformation and double log-transformation, and likelihood ratio test and score test, are developed to test the homogeneity under two models, distinguished by conditional independence assumption of two classifiers. Simulation results show that score test performs better than other tests in the sense that the empirical size is generally controlled around the nominal level, and hence be recommended to practical applications. Other tests also perform well when both binomial proportions and sample sizes are not small. Approximate sample sizes based on score test, likelihood ratio test and the weighted-least-squares test with double log-transformation are generally accurate in terms of the empirical power and type I error rate with the estimated sample sizes, and hence be recommended. An example from the malaria study is illustrated by the proposed methodologies.

A Note on the Asymptotic Distribution of Likelihood Ratio Tests to Test Variance Components

2006 ◽  
Vol 9 (4) ◽  
pp. 490-495 ◽  
Author(s):  
Peter M. Visscher
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Distribution ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Variance Components ◽  
Degrees Of Freedom ◽  
Likelihood Ratio Tests ◽  
Likelihood Ratio Test Statistic ◽  
Ratio Test ◽  
Test Statistic

AbstractWhen using maximum likelihood methods to estimate genetic and environmental components of (co)variance, it is common to test hypotheses using likelihood ratio tests, since such tests have desirable asymptotic properties. In particular, the standard likelihood ratio test statistic is assumed asymptotically to follow a χ2 distribution with degrees of freedom equal to the number of parameters tested. Using the relationship between least squares and maximum likelihood estimators for balanced designs, it is shown why the asymptotic distribution of the likelihood ratio test for variance components does not follow a χ2 distribution with degrees of freedom equal to the number of parameters tested when the null hypothesis is true. Instead, the distribution of the likelihood ratio test is a mixture of χ2 distributions with different degrees of freedom. Implications for testing variance components in twin designs and for quantitative trait loci mapping are discussed. The appropriate distribution of the likelihood ratio test statistic should be used in hypothesis testing and model selection.

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