Dictionary of Evidence-based Medicine.pdf

86 Dictionary of Evidence-based Medicine
treat analysis: implications for quantitative and qualitative research. International
Journal of Epidemiology. 21: 837-41. Peduzzi P, Detre K, Wittes },
Holford T (1991) Intent to treat and the problem of cross-overs. An example
from the Veterans Administration coronary bypass surgery study. Journal
of Thoracic and Cardiovascular Surgery. 101: 481-7). While intention to treat
analysis is rather straightforward to apply when the outcome is discrete,
with continuous outcomes the analysis requires imputation of data and how
to deal with those who actually switched treatments is more problematic.
Interim analysis
When designing a clinical trial, there are often areas surrounded by much
uncertainty. For example, in calculating the sample size of the trial, we
require an estimate of the variance of the patients' responses and there may
not be much prior knowledge about this and a rough estimate has to be
made. In other situations, for ethical reasons, we may wish to terminate a
trial early if a treatment appears to be obviously superior to another from
the early data. To enable us to decide when to stop or whether recruitment
has to be increased, we need to do an analysis which may not be the
final planned analysis. This is termed an interim analysis. As such, it is a
scientific inferential process which uses appropriate statistical methods
and hence has to be planned for with decision rules defined prospectively
at the trial design stage. Such interim analyses are usually conducted when
there is clearly a lack of intended effect, emergence of serious unpredicted
adverse effects or overwhelming efficacy results in life-threatening or
severely debilitating illness. Unplanned interim analyses outside these
situations should not be conducted as they interfere with the internal
validity of the trial (Sankoh AJ (1995) Interim analyses: an FDA reviewer's
experience and perspective. Drug Information Journal. 29: 729-37).
Internal validity
Internal validity refers to whether conclusions drawn with respect to the
specific population under study are valid. For example, in an observational
study evaluating the significance of various potential risk factors
for a particular disease, systematic differences in the characteristics of
different groups pose a serious threat to its internal validity. In other
words, an association which is identified between a risk factor and the
disease may arise solely because the risk factor is associated with another,
more significant one.