Dictionary of Evidence-based Medicine.pdf

150 Dictionary of Evidence-based Medicine
usually for predictive purposes. For example, in modelling patient throughput
at a hospital pharmacy or clinic, the build-up of queues may be
stochastically modelled by a computer simulating the arrival of patients by
sampling from a Poisson distribution and the service time by sampling
from an exponential distribution. Such a model can be used, for example, to
predict the number of servers which would be required based on what
would be judged to be acceptable waiting times. With computers, it is
relatively easy to simulate even highly complex events such as the time
spent by a cohort of patients in different health states (see State-transition
model). In contrast, models which do not take account of the randomness
of data and/or parameters by simulation are called deterministic models.
These may be based on simple algebraic formulae such as number of
servers required equals average number of patients arriving during the
day divided by average number of patients which one pharmacist or doctor
can attend to. Alternatively, differential equations may be used to describe
the system being modelled. Such deterministic models are often used in
pharmacokinetic studies although stochastic modelling is being increasingly
used too, particularly when making predictions about individuals
from population data. Note that precision estimates can still be made for
predictions based on deterministic models provided we are willing to
make assumptions about the precision or probability distribution of the
input data.
Studentized value
A value which has been standardized by dividing it with its associated
error to yield a dimensionless score. Thus the standardized residual is the
residual divided by the estimated standard deviation of that residual.
Subjective preference
In the context of clinical trials or choice of treatment, subjective preference
describes the choice of a particular treatment based on hunch, advice from
friends or relatives or similar types of information. This is to be contrasted
with informed choice when patients base their preferences on reliable
estimates of risks and benefits from reliable data such as those derived
from robust clinical trials or systematic overviews. However, irrespective of
which approach is used, patients' preferences are often highly variable
particularly when there are (i) major differences in possible outcomes;
(ii) major differences between treatments in the range, likelihood and
severity of outcomes; (iii) choices involving trade-offs between short- and
long-term outcomes; (iv) choices involving a small probability of a major