0:00
Welcome to this presentation.
My name is Patrick Onghena
and together with you,
I want to look
into the question,
whether it is possible
to draw casual inferences
from single-patient data.
The answer will turn out
to involve
the introduction
of randomization
in N-of-1 clinical trials.
0:21
So we'll kick off
this presentation
by defining our terms.
What is an N-of-1 clinical
trial?
And what do we mean
by randomization
in N-of-1 clinical trials?
After setting the scene,
we are ready to answer
our main question,
demonstrate how we can
achieve casual inference,
how designs can be classified,
and how the computations
can be made feasible.
We close this presentation
with the discussion
and general reflection
on the current
scientific consensus
about N-of-1 clinical trials
and on the issue
of generalizability.
0:59
N-of-1 clinical trial is defined
as a clinical trial
in which only one single
patient participates.
The purpose of such a trial
is to examine
the relation between one
or more treatments on one hand,
and one or more health related
outcome variables
on the other hand
for that particular patient.
This examination is carried out
by repeatedly introducing
and withdrawing the treatment
and conducting
repeated measurements
for the outcome variable
or variables of interest.
So the N-of-1 part of the term,
refers to the number of patients
involved,
not to the number
of observations,
which has to be much larger
as implied by the definition.
N-of-1 clinical trials are used
in evidence-based medicine,
if the intention is to focus
on a single patient
or if a large-scale
clinical trial
is difficult or impossible,
for example, for rare diseases,
patients with diverse
comorbid conditions
or patients
using concurrent therapies.
Also, financial
and other practical restrictions
may lead the researcher
to opt for N-of-1 clinical trial
or small series
of N-of-1 clinical trials.
For a practitioner
an N-of-1 clinical trial
may also be
the only tool available,
if no large group trial
has been conducted yet
for a specific constellation
of patient symptom
and setting specific
characteristics and nonetheless,
immediate evidence-based
decision and action are needed.
