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Dictionary of Evidence-based Medicine.pdf

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

Causal relationship<br />

In observational studies, the investigator has no control over the factors<br />

which may influence a particular outcome and therefore erroneous causal<br />

associations may be inferred. To reduce this risk, Bradford Hill has suggested<br />

a number <strong>of</strong> features which should be carefully considered when<br />

assessing the causation <strong>of</strong> a disease: (i) the strength <strong>of</strong> association; (ii) consistency<br />

in the data (in other words, do all or most <strong>of</strong> the published studies<br />

point in the same direction); (iii) specificity; (iv) temporal relationship,<br />

i.e. exposure to putative factor must precede onset <strong>of</strong> disease; (v) biological<br />

gradient or dose-response relationship; (vi) biological plausibility. It is<br />

important to note, however, that biological plausibility depends on the<br />

state <strong>of</strong> knowledge <strong>of</strong> the day (Hill AB, Hill ID (1991) Bradford Hill's<br />

principles <strong>of</strong> medical statistics. 12th edn. Edward Arnold, London).<br />

Cause-specific rate (see Specific rate)<br />

CBA (see Cost benefit analysis)<br />

CEA (see Cost-effectiveness analysis)<br />

Ceiling effect<br />

A ceiling effect is said to occur when a high proportion <strong>of</strong> subjects in a study<br />

have maximum scores on the observed variable. This makes discrimination<br />

between subjects at the top end <strong>of</strong> the scale impossible. For example,<br />

an examination paper may lead to, say, 50% <strong>of</strong> the students scoring 100%.<br />

While such a paper may serve as a useful threshold test, it does not allow<br />

ranking <strong>of</strong> the top performers. For this reason, examination <strong>of</strong> test results<br />

for a possible ceiling effect, and the converse floor effect, is <strong>of</strong>ten built<br />

into the validation <strong>of</strong> instruments such as those used for measuring quality<br />

<strong>of</strong> life.<br />

Censoring<br />

Censoring is a phenomenon <strong>of</strong>ten seen in the analysis <strong>of</strong> survival data or<br />

event history analyses. To illustrate, consider the follow-up <strong>of</strong> two groups<br />

<strong>of</strong> terminally ill patients in a comparative study <strong>of</strong> a new drug and the<br />

standard drug. Unfortunately, because we do not have curative drugs for<br />

most cancers, time to death is <strong>of</strong>ten used as an outcome measure in such

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