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Scientific article
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English

How to use likelihood ratios to interpret evidence from randomized trials

Contributors
Published inJournal of clinical epidemiology, vol. 136, p. 235-242
Publication date2021-08
First online date2021-04-27
Abstract

Objective: The likelihood ratio is a method for assessing evidence regarding two simple statistical hypotheses. Its interpretation is simple - for example, a value of 10 means that the first hypothesis is 10 times as strongly supported by the data as the second. A method is shown for deriving likelihood ratios from published trial reports.Study design: The likelihood ratio compares two hypotheses in light of data: that a new treatment is effective, at a specified level (alternate hypothesis: for instance, the hazard ratio equals 0.7), and that it is not (null hypothesis: the hazard ratio equals 1). The result of the trial is summarised by the test statistic z (ie, the estimated treatment effect divided by its standard error). The expected value of z is 0 under the null hypothesis, and A under the alternate hypothesis. The logarithm of the likelihood ratio is given by z·A - A2/2. The values of A and z can be derived from the alternate hypothesis used for sample size computation, and from the observed treatment effect and its standard error or confidence interval.Results: Examples are given of trials that yielded strong or moderate evidence in favor of the alternate hypothesis, and of a trial that favored the null hypothesis. The resulting likelihood ratios are applied to initial beliefs about the hypotheses to obtain posterior beliefs.Conclusions: The likelihood ratio is a simple and easily understandable method for assessing evidence in data about two competing a priori hypotheses.

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Keywords
• Clinical trials
• Confidence interval
• Evidence
• Likelihood ratio
• P-value
• Statistical inference
• Data Accuracy
• Data Interpretation, Statistical
• Humans
• Likelihood Functions
• Models, Statistical
• Proportional Hazards Models
• Randomized Controlled Trials as Topic / standards
• Randomized Controlled Trials as Topic / statistics & numerical data
• Research Design / standards
• Research Design / statistics & numerical data
Research group
Citation (ISO format)
PERNEGER, Thomas. How to use likelihood ratios to interpret evidence from randomized trials. In: Journal of clinical epidemiology, 2021, vol. 136, p. 235–242. doi: 10.1016/j.jclinepi.2021.04.010
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Article (Published version)
Identifiers
ISSN of the journal0895-4356
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