Title: Patient Preference and Comprehensive Cohort Designs
1Patient Preference and Comprehensive Cohort
Designs
2Background
- Patients often have a preference for treatment.
Patients with strong preferences for usual care
often do not get into a trial because
randomisation does not guarantee that they will
get what they want. - Patients who do get into a trial with strong
preferences for the novel treatment can bias
results.
3Strong preferences
- When a treatment is ONLY available within a trial
context patients who want the treatment may
decide to consent to randomisation with the hope
they may get the treatment. - This can lead to the following problems
- Demoralisation
- High drop out
- BIAS
4Effect of Preferences
- Patients may refuse to fill in follow-up
questionnaires due to resentful demoralisation,
which can lead to bias. - Patients who DO get what they want may
exaggerate the effectiveness of their treatment
again this MAY lead to bias.
5Quality of Life and Preference
- Preferences are particularly a problem when
quality of life is a major outcome as this is
more susceptible than objective measures of
outcome (e.g death).
6Patient Preference Trial A solution
- One approach to the issue of preferences is to
undertake a patient preference trial. - Only patients indifferent to which treatment
they receive are randomised. - Trial also known as Brewin-Bradley or
Comprehensive Cohort Design.
7Patient Flow in Preference Trial
8Preference Example
- A trial of two methods of abortion medical
termination (mifepristone) vs surgical
aspiration. - Some women had strong preferences and therefore
were allowed their treatment choice.
9Abortion Trial
Heshaw BMJ 1993307714-7.
10Data from Abortion Study
- The extra benefits of the preference study showed
that clinically there was no difference. - HOWEVER, women with a preference should be
allowed their choice women who were indifferent
with late gestation would find surgical abortion
more acceptable.
11Comprehensive Cohort
- In a comprehensive cohort study, Porthouse looked
at the fracture rates among women who took part
in a fracture prevention trial compared with
those who were either ineligible or would not
participate.
12Comprehensive Cohort Design
Source Porthouse MSc thesis and QJM 200497569.
13Results
- Those taking part had significantly lower risk of
fracture compared with similar, eligible, women
refusing to take part. - Recruitment to fracture prevention trials selects
individuals who are at lower risk than those for
whom the treatments will eventually be used.
14Problems with Preference Design
- Because preference arms are not formed by
randomisation they WILL be exposed to selection
bias. - This makes the comparisons of these arms
hazardous.
15Patient Flow in Preference Trial
16Preference Recruitment
- Trial recruitment is unaffected by the inclusion
of preference arms EXCEPT for the extra resources
needed to follow-up the preference arms. - Cooper et al. undertook a RCT of the preference
design and found no advantage to it.
Cooper et al. Br J Obs Gynae 19971041367-73.
17An Alternative?
- One approach to preserve the benefits of
randomisation is to undertake a fully randomised
preference trial. - ALL patients would be randomised irrespective of
their preferences and preference would be used in
the analysis.
Torgerson et al. 19961194-7.
18Example
- York Back Pain trial randomised 187 people with
low back pain to an exercise programme or control
(benign neglect?). - Before randomisation patients were asked their
preferences. - 63 expressed a preference for the new treatment
37 had no preference.
19Backpain Preference Trial
Klaber Moffett et al. BMJ 1999319279-83.
20Back Pain Trial
- In the back pain trial we were able to show that
the intervention was just as effective among
patients who were indifferent to having the
exercise therapy compared with those who were
really keen.
21Antenatal Care
- In a trial of increased visits to women for
antenatal care it was found that women with a
strong preference for the alternative were much
more dissatisfied with treatment.
22Antenatal Trial Dissatisfaction with Treatment
Clement et al. 1998 BMJ 31778
23The most interesting example
- SPRINTER is a RCT of treatments for neckpain.
- Two treatments a Brief Intervention (1-2
sessions with a physio using CBT) vs usual care
(5 sessions). - BEFORE randomisation we asked patients their
treatment preference.
24SPRINTER Preferences
- In SPRINTER preferences were mixed
- 53 did not have a preference
- 16 wanted brief intervention
- 31 wanted usual care.
- ALL patients were randomised IRRESPECTIVE of
their preference.
25Patient Flow and 12 month Results - SPRINTER
Overall 12 month improvement -0.840
Overall 12 month improvement -2.825
26SPRINTER Results
27SPRINTER interpretation
- Had we not asked for preferences we would have
concluded usual care is best for all. - BUT we can now say that BI is best for those who
want that treatment (also much cheaper) and UC
should be reserved for those who want it or
indifferent patients.
28Where now preference?
- In MY view if there is likely to be a problem
with preferences we should elicit these at the
start of the trial and include them in the
analysis. - Patient preference trials of the Brewin-Bradley
design are fraught with analytical problems
selection bias.
29Doctor Preference
- As well as patients one could use doctor
preferences to allocate treatment. - In a RCT of orthopaedic surgery vs orthopaedic
medicine patients were only randomised if GP was
indifferent to the specialist needed. - Patients with a named consultant were included
and followed.
Leigh-Brown et al. 2001 Health Bulletin
59198-210.
30Doctor Preference
- In the OMENS trial Leigh-Brown et al found that
outcome did not seem to be affected by physician
preference. - Outcomes were similar across groups with
orthopaedic medicine being more cost effective.
31Summary
- Patient preferences CAN affect outcome.
- Elicitation of preferences when this is an issue
BEFORE randomisation can be important.