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Dictionary of Evidence-based Medicine.pdf

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140 <strong>Dictionary</strong> <strong>of</strong> <strong>Evidence</strong>-<strong>based</strong> <strong>Medicine</strong><br />

Recognition lag<br />

The recognition lag is the time it takes for policy and decision makers<br />

to recognize that a policy change is necessary (see Impact lag and Implementation<br />

lag).<br />

Reference pricing<br />

Reference pricing refers to the use <strong>of</strong> a reference drug from a particular<br />

therapeutic class to calculate the reimbursement to be made for any drug<br />

belonging to that category dispensed by third parties. Such schemes are<br />

usually adopted by prescription pricing agencies to control drug costs.<br />

Therefore the reference drug is usually the cheapest in the category concerned<br />

unless a case can be made for the use <strong>of</strong> a more expensive alternative<br />

<strong>based</strong> on cost-effectiveness analysis.<br />

Relative income hypothesis<br />

The relative income hypothesis (see also Absolute income hypothesis) postulates<br />

that the distribution <strong>of</strong> income in a society affects the individual's risk<br />

<strong>of</strong> mortality (Gravelle H (1998) How much <strong>of</strong> the relation between population<br />

mortality and unequal distribution <strong>of</strong> income is a statistical artifact<br />

BMJ. 316: 382-5).<br />

Relative price effect<br />

The relative price effect is the change in the unit cost <strong>of</strong> a governmentprovided<br />

good or service relative to the general price level.<br />

Relative risk (see under Risk)<br />

Relative risk reduction (see under Risk reduction and Risk)<br />

Residual error<br />

The residual error is the difference between an observed value and the<br />

value predicted by a statistical model. For example, consider a linear<br />

regression model <strong>of</strong> the form y = ax + b, where x is the predictor variable,<br />

y is the response variable and a and b are constants. If a set <strong>of</strong> y values are<br />

observed at different x values, the coefficients a and b can be estimated<br />

from the paired x and y values. For any new x value, we can obtain a

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