How to Think about Cause-Effect Relationships: Multiple and Single Observations 1
Milos Jenicek in How to Think in Medicine, 2018
Likelihood refers to the general state of being likely or probable that an event has already occurred and would yield a specific outcome. Formally, it differs from probability as probability refers to the occurrence of future events, and likelihood refers to events that have already occurred. Statistically speaking, a likelihood ratio is the ratio of the values of the likelihood function at two different parameter values or under two different data models.3,4 In other words, a likelihood ratio is the likelihood that some observed outcome will occur if a set of parameters exist, compared to the likelihood that a second observed outcome will occur if the same set of parameters does not exist. Considered as a diagnostic methods and test, a likelihood ratio test is the probability that a given test result would occur in a person with the target disorder divided by the probability that the same result would occur in a person without that disorder.3
Randomised controlled trials, other study designs and meta-studies
R. Paul Thompson, Ross E.G. Upshur in Philosophy of Medicine, 2017
With respect to replacements of classical statistical methods of statistical inference, the focus is on replacing the evidential use of p-values and confidence intervals with likelihood methods (see Richard Royall (1997), J.O. Berger (2006), Donald Berry (2012) and Jeffrey Blume (2011)). The likelihood ratio measures the degree of support that some evidence confers on one of two competing hypotheses and follows as a deductive consequence of the law of likelihood: Along with many others, we argue that the law of likelihood is an a priori truth: a theorem of mathematics. Embedded in Bayes’ theorem is the likelihood ratio: Pr(E/H)/Pr(E/~H). In this sense, then, Bayesian methods are consistent with likelihood methods, and so hold the added theoretical virtue of being consistent with the law of likelihood.11
An Introduction to Statistics and Proposition Setting
Jo-Anne Bright, Michael D. Coble in Forensic DNA Profiling, 2019
This is the odds form of Bayes’ theorem. Pr(Hp)/Pr(Hd) are the prior odds, Pr(E | Hp)/Pr(E | Hd) is the likelihood ratio (it is now the ratio of two likelihoods), and Pr(Hp | E)/Pr(Hd | E) are the posterior odds. The odds form of Bayes’ theorem demonstrates the clear distinction of the roles of the scientist, judge, and jury. The posterior and prior odds are the domain of the jury and judge. In forensic science, the prior odds are the jury and judge's view of the truth of the prosecution and defense propositions before the DNA evidence is presented, and the posterior odds is their view after the DNA evidence is presented. The forensic scientist reports the likelihood ratio, or LR.
Evaluation of the GeneXpert MTB/RIF assay performance in sputum samples with various characteristics from presumed pulmonary tuberculosis patients in Shiselweni region, Eswatini
Published in Infectious Diseases, 2022
Durbbin Lupiya Mulengwa, Maropeng Charles Monyama, Sogolo Lucky Lebelo
All samples tested on GeneXpert MTB/RIF assay were also processed on MGIT culture. Statistical analysis was performed to determine sensitivity and specificity as well as positive and negative predictive values on both GeneXpert MTB/RIF and MGIT culture. The sensitivity was defined as the ability of the test to correctly identify those patients (or samples) with the disease. Specificity was defined as the test’s ability to correctly identify those patients (or sputum samples without the disease. The positive predictive value was described as the probability that subjects with a positive screening test truly have the disease while the negative predictive value is the probability that subjects with a negative screening test truly don't have the disease. The likelihood ratio was defined as how much more likely was it that a patient (or sample obtained which tests positive has the disease compared with one that tests negative. To measure the effects of each characteristic on the GeneXpert MTB/RIF positive results, a univariate and multivariate analysis was performed using simple logistic regression and multiple linear regression respectively. The difference was declared as statistically significant if P-value was less than .05. P-value is the probability of obtaining results as extreme as the observed results of a statistical hypothesis assuming that the null hypothesis is correct.
A generalized likelihood ratio test for monitoring profile data
Published in Journal of Applied Statistics, 2021
Yang Liu, JunJia Zhu, Dennis K. J. Lin
If a parametric function is used to model both the baseline profile 25]. The likelihood ratio test is a powerful and popular statistical hypothesis test for testing the departure of a vector of parameter values from their hypothetical values. Define c is any number satisfying 3]. Among all tests with a given probability of Type-I error, the likelihood ratio test is shown to minimize the probability of a Type-II error [17].
Association of Dry Eye with Laryngopharyngeal Reflux in Clinical Practice
Published in Current Eye Research, 2022
S. Bonini, M. Labetoulle, E. Messmer, P. Aragona, J. M. Benitez Castillo, G. Ciprandi, V. Damiani, M. Irkec, C. Baudouin, M. Rolando
The logistic and linear regression models were performed (logistic and linear family functions were used for continuous and count outcomes, respectively). Moreover, an interaction analysis was performed to test the intergroup impact of LPR on DED using the MRMs. Finally, an intragroup analysis was performed using the MRMs to assay the RSI effect on outcomes in the groups. The odd ratios and rate ratios associated with clinical outcomes were calculated with their 95% confidence interval for each factor from the MRMs. The Likelihood Ratio test was used as a test of statistical significance. The possible variability among clinical centers was considered adding the center variable as a random effect in all MRMs. Due to the exploratory nature of this study, adjustment for multiple testing was not performed. With a p-value less than 0.05, differences were selected as significant, and data were acquired and analyzed in R v4.0.3 software environment.35
Related Knowledge Centers
- Null Hypothesis
- Sampling Error
- Statistical Significance
- Power of A Test
- Alternative Hypothesis
- Wilks' Theorem
- Sampling Distribution
- F-Test
- G-Test
- Pearson'S Chi-Squared Test