Further improvements to the sensitivity and specificity of the method include stratifying by age and sex, examining serious or fatal reactions only.
Further improvements to the
sensitivity and specificity of the method include stratifying by age and sex,
examining serious or fatal reactions only. (The proportion of reactions that
are fatal, stratified by age, is itself a potential signalling method.) Where a
drug has a well-known ADR that is reported very frequently, such as
gastrointestinal (GI) bleeding with non-steroidal anti-inflammatory drugs
(NSAIDs), this will distort the PRR for other reactions with that drug. The
best approach is then to remove the known reac- tions from the totals for that
drug and the database as a whole and recalculate the PRR for all other reac-
tions with that drug. This is simple to do on an ad hoc basis but is more difficult to implement in an automated
way.
The comparison used need not be
the entire database. It is possible to use PRRs within drug classes or
indications so that the comparator is all drugs in that class or those used for
a particular indication.
The expected number of reactions
could also incor- porate prior beliefs about the ADR profile, using a fully
Bayesian method. (The approaches used at the FDA and WHO do not incorporate
prior beliefs.)
The grouping of terms used in the
medical dictio- nary for the database is an important feature. Little empirical
study of the effect of choosing different levels in the hierarchy of terms has
been done. In most instances, the grouping is at ‘Preferred Term’ (PT), which
is a relatively low level. There are a large number of medical terms at this
level, so that the numbers for any particular combination of drug and reaction
can be small. This can lead both to the general statistical problem of
multiplicity, with many possibilities for signals, and to instability in the
PRR based on small expected numbers.
It is possible to use a two-stage
process – using, say SOC to screen for raised PRRs, then to re-examine the PRRs
using PTs within the SOC where the PRR was raised. The automation of this
process is possi- ble in principle, but has not been done yet. An alternative
is to use an intermediate level within the hierarchya ‘High Level Term’ (HLT),
for exam- ple. This has the advantage of being a single stage process and
avoids the use of too many terms, reduc- ing the problems of multiplicity and
small expected numbers.
The use of the method in general
is easiest within a large database that contains a wide range of drugs, but it
can be used within a pharmaceutical company database. Here, the potential for
incorporating prior beliefs is at its greatest. A further possibility for
companies is to use the proportions of reactions from the FDA database, which
is publicly available, to calculate expected numbers for their own drugs. Other
regulatory
databases are not yet publicly available but increasing transparency may change
this in the future.
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