Dictionary of Evidence-based Medicine.pdf
Dictionary of Evidence-based Medicine.pdf
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