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Dictionary of Evidence-based Medicine.pdf

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L<br />

L'Abbe plot<br />

This is a plot displaying individual trial results so that readers can rapidly<br />

identify which <strong>of</strong> the trials included in a systematic review show benefits<br />

in favour <strong>of</strong> the test treatment and which do not (Figure 13). The two axes<br />

<strong>of</strong> the plot represent the response <strong>of</strong> interest for the two treatment groups.<br />

Identical scales are chosen for each group's responses (y axis for test<br />

treatment and x axis for the control treatment) and the plane subdivided<br />

into two equal areas separated by a 45° diagonal line <strong>of</strong> equality. Therefore,<br />

trials which show results in favour <strong>of</strong> the test treatment fall in the region<br />

above the diagonal while those which favour the control treatment fall<br />

below the diagonal. The symbol (e.g. circles) chosen to represent the individual<br />

trial may be sized to reflect the sample size or inverse variance <strong>of</strong><br />

the estimate and hence the weight which should be attached to each <strong>of</strong> the<br />

trials (L'Abbe KA, Detsky AS, O'Rourke K (1987) Meta-analysis in clinical<br />

research. Annals <strong>of</strong> Internal <strong>Medicine</strong>. 107: 224-33).<br />

Libertarianism (see under Utilitarianism)<br />

Likelihood ratio<br />

The likelihood <strong>of</strong> an outcome is the probability <strong>of</strong> its occurrence under a<br />

given probability model.<br />

Likelihood = p(y|model)<br />

The likelihood ratio (LR) is simply the ratio <strong>of</strong> two likelihoods, i.e.<br />

LR = p(y|model-l) / p(y|model-2)<br />

In Bayesian analysis, the likelihood ratio is used to transform the prior<br />

odds <strong>of</strong> one model compared to another, to the posterior odds as follows:<br />

p(model-l|i/) / p(model-2) = LR x [p(model-l) / p(model-2)]<br />

Posterior odds = LR x prior odds

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