Transcript Patients The Key to Real Data?

Patients
The Key to Real World
Data?
Alan G. Wade
Real World Data Sources
Sources of Data
6%
6%
29%
18%
41%
Clinical Trials
Patient Data
Registries
Prescribing Data
Big Data
What is real world data?
Reflects the average patient in a real-world setting
• Demographics
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Sex
Age
Social status
Education
Diagnosis
Co-morbidities
Co-medications
Medical system
Social impact
Work impact
Family impact
Impact on Quality of Life
Uses for “real world” data?
Pre market entry
disease, burden, unmet need,
treatment pathway mapping
Licensing
when impact only known by patient or
difficult to assess
Labelling extension
Validated PRO’s
Market Access
HTA; effectiveness
Post license
safety, benefit:risk
(registry),risk/management plan
Guideline development ???
Traditional data sources
Source
Randomised Controlled Trials
PMS Studies
Physician driven
Patient registries
Prescribing data
Database – physician recorded
Focus groups
Patient organisations
Patients
Hierarchies of evidence
1. Systematic reviews of randomised
controlled trials (RCTs).
2. RCTs.
3. Controlled observational studies - cohort
and case control studies.
4. Uncontrolled observational studies - case
reports.
5. Expert opinion
?KoL
Randomised Controlled Trials
• “the gold standard” for demonstrating (or
refuting) the benefits of a particular
intervention.
• Important limitations
Limitations of RCT’s
Patients
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Age - Effectiveness in younger or older patients
Sex
Severity - effectiveness in mild or severe
Risk factors - effectiveness in patients with risk factors for the condition (eg, smokers)
Co morbidities - Influence of other conditions
Ethnicity - effectiveness in other ethnic groups
Socioeconomic status - effectiveness in disadvantaged patients
Treatment
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Dose - high dose used in RCTs?
Timing of administration Influence on adherence (compliance) to treatment regimens
Duration of therapy - effectiveness during long-term use
Co medication - adverse interactions
Comparative effectiveness - in comparison with other products used for the same indication
Setting
–
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Quality of care
Prescription and monitoring by less specialist (expert) healthcare providers
Care pathway
Effectiveness and efficiency
Efficacy
Does it work in
clinical trials?
Effectiveness
Does it work in
real life?
Efficiency
Does it contribute to a
more efficient use of
resources?
 COST-EFFECTIVENESS
Post Marketing Surveillance
To assess performance of drug in real world setting
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Large numbers
Off-label prescribing
Comorbidities
Concomitant medications
Speed of reporting
Do we achieve this with formal PMS?
Hierarchies of evidence
1. Systematic reviews of randomised
controlled trials (RCTs).
2. RCTs.
3. Controlled observational studies - cohort
and case control studies.
4. Uncontrolled observational studies - case
reports.
5. Expert opinion
?KoL
6. Patient reports ?
Focus groups
• Small numbers
• Representative?
• Skill of co-ordinator/observers
Who in the world most influences the
pharmaceutical industry?
1. Barack Obama
2. Michael Rawlins
Harveian Oration
Hierarchies of evidence should
be replaced by accepting—
indeed embracing—a diversity
of approaches.
The Lancet Vol 372 December 20/27, 2008
NICE
Patient and public involvement
The views of patients, carers and the public
matter to NICE. We want to involve them, as well as
doctors, nurses, other healthcare professionals and
managers in our work.
http://www.nice.org.uk – accessed 22 06 09
EMA
The assessment of the benefit-risk balance should be based on the
available tests and trials, which are designed to determine the efficacy and
safety of the product under normal conditions of use (Directive
2001/83), and which are generally performed under ideal conditions.
It is important to be explicit about the perspectives of different stakeholders
that are taken into account in the assessment of the benefit-risk balance, in
particular the perspectives of patients and treating physicians.
Considerations about how the treatment is expected to perform under real
conditions of use are relevant in the context of pharmacovigilance activities,
for example, to take into account any available information on misuse and
abuse of medicinal products which may have an impact on the evaluation
of their benefits and risks (Directive 2001/83).
Patient groups - the
patient?
EPF is the umbrella organisation of
pan-European patient organisations
active in the field of European public
health and health advocacy.
The European Patients’ Forum (EPF)
currently represents 57 patient
organisations and an estimated 150
million patients across the 27 Member
States throughout Europe.
Patient organisations
EU drugs agency working with patient
groups bankrolled by big pharma
23.04.10 @ 19:17
Harveian Oration
But equally, we have a right and responsibility
to look at the interests of other patients who
use the healthcare system. What I am critical
of, however, is patient organisations that are
acting on behalf of pharma companies. I am not
alone in complaining about them.
The Lancet Vol 372 December 20/27, 2008
“Big Data”
Big Data - definition
No single agreed definition
but
The bottom line: whatever the disagreements
over the definition, everybody agrees on one
thing: big data is a big deal, and will lead to huge
new opportunities in the coming years.
http://timoelliott.com/blog/2013/07/7-definitions-of-big-data-youshould-know-about.html Accessed 27 01 14
Big Data – Pharma???
Registries
Claims Databases (US)
e - Medical Records
Also
Pharmacy databases
Specific hospital databases
Specific disease or procedure databases
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What do you want from a
registry?
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Large numbers
Patient level data
Immediacy of data
Longitudinal data
Representative population
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Presence of YOUR required data
Linkage of data fields of interest
Confirmation of diagnosis
Standardised measurement
Validated PRO’s
Existing registries
Strengths
Weaknesses
• Large numbers
• Immediate access
• Longitudinal data
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• Prescribing data
Inherent biases
Representative population
Diagnostic drift
Patient level data
Surrogate outcomes
Completeness of data
Family social and work history
To effectively use any registry it is important to understand how it has
been developed and its strengths and weaknesses
Patient Reported Outcomes
Why & How?
• Some treatment effects only known to patient
• Pts provide unique perspective on treatment
• Provide information on QoL, work, social and
family
• Formal assessment may be more reliable than
informal interview
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Addressing registry weaknesses
What the
Patient Knows
What the Patient Shares
What the physician
understands
What the
Physician records
Big Data
Ask the patient
– but how?
Real World Data Sources
Sources of Data
6%
6%
29%
18%
41%
Clinical Trials
Patient Data
Registries
Prescribing Data
Big Data
Conclusion
Collect data directly from patients
Patient Reported Outcomes
PRO’s
Patient Reported Outcomes - Definition
• “any aspect of patients health status that
comes directly from patient” - FDA
• “insight into way patients perceive their
health & impact treatments or adjustments
to lifestyle have on their quality of life” –DH
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FDA Guideline report Dec 09
Patient Reported Outcomes – concerns
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Pt recording versus doctor
Pt understanding of question/form
Validity of question (in that format, pt popn)
Reliability of question
Ability of question to detect change
For licensing – need set as per RCT
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Definition of
“Real Data from Real Patients”
• Collecting data from patients receiving
“routine care”
BUT
• Not affecting their prescribers/ healthcare
professionals behaviour
Methodology
PROBE
Patient Reported Outcome Based Evaluation
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Process set-up
• Define question - protocol
• Define patient group of interest
• Determine location of group e.g. Specialist unit,
community setting, geography
• Review options for accessing patient group
– Orphan indications
• Design questionnaires and reports
– development and testing
• Structure customised database
Structured patient registries
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Bespoke – ask required questions
Innovative
Prospective
Hosted on Patients Direct site
Interactive
Global Coverage
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Problems
• Is on-line collection satisfactory?
• Will patients cooperate
• How do you recruit?
– Methodology
– Achieving a representative population
www.InternetWorldStats.com 2009
On-line reporting
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Age?
Education?
Social class?
Carer reporting?
– Alz Dis
– children
Age and Social Media
Conclusion
Generally require alternative data collection
routes
Nurse manned telephone
Why do people participate?
...the benefits and attraction to each individual will differ but we
believe the main reasons are :
• Outlet for their feelings and views – might be a threat to their
relationship if they report problems to their healthcare professional –
we’re neutral
• Altruistic - Assist in developing new and better treatments
• Obtain better information and knowledge through participation
• Feel valued through regular contact/ interaction
• Desire to make sure the patients voice is heard
42
Will patients cooperate?
7. REPORT CONCLUSIONS.
The above report has shown the public’s enthusiasm towards a system that would let
consumers report adverse drug reactions through the Internet. The findings of the
survey carried out by us revealed to us this enthusiasm. The report has further shown
that health professionals have a positive opinion towards such a system, which
works
in favour of the overall mission of Patients Direct. Not only has the report articulated
the publicity campaign that Patients Direct can carry out to raise its awareness, but
also examples of different medicine inserts has been provided with reasoning behind
them that could be used by the company to make itself different from its competition.
Patients Directs corporate identity is important to begin the process of publicity. IT
has been noted that establishing a user friendly website that is easily navigable is
fundamental to setting the pace for a strong brand and image that will appeal to
Patient’s Direct customers.
EDGE Survey - Market Research, 2008
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Recruitment
...tailored to attract patients of interest with a conscious effort to reduce
bias and population selection issues
Core recruitment methods include :
• social networking sites
• search engine and website optimisation
• public affairs articles and traditional methods of publicity e.g. Newsprint/TV
• Healthcare professionals –Doctors, pharmacists
• wholesalers, distributors
• Clinical trial participants
• patient groups
Special focus can be given to groups of interest such as children if appropriate and
recruitment monitored to ensure sufficient numbers in each cohort.
Recruitment Examples
• General – statins
– Pharmacy/wholesale distributor
– Advertising - Google
• Vaccines
– Direct at vaccination
• Families and children
– Appropriate web-sites
• Specialist product – home delivery
– Invitation with delivery
• OTC – Strepsils
– General advertising
– Pharmacy
– Package wrap
Inflammatory Back Pain
3rd February 2014
Recruitment
• Recruitment Method - Facebook only
• First Participant Recruited – 22nd Dec
2013
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Facebook Advert
Example
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Facebook Advert
Example
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Landing Page
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Respondents
300
262
250
200
151
150
108
100
79
50
0
Started
Completed Incomplete Meets IBP
Criteria
52
Age Profile
80
70
60
50
40
30
20
10
0
77
52
57
36
15
2
Under
30
31 to
40
41 to
50
51 to
60
61 to
70
Over
70
53
Sex Ratio
200
188
148
150
100
40
50
0
Total
Male
Female
54
Meets IBP Criteria
79
80
53
60
37
40
31
20
0
Total
Calin
ASAS
Calin and
ASAS
55
Age Profile of
Respondents with IBP
35
30
25
20
15
10
5
0
32
24
16
13
4
Under
30
31 to
40
41 to
50
51 to
60
61 to
70
1
Over
70
56
Non Completers
120
108
100
80
52
60
40
20
0
Total
EQ5D
57
Influenza Family Study
Family Influenza Survey
Households completed study
946
Individuals in completer households
3695
Flu-like Illness episodes in
households
540
Total “Flu-like” Illness
851
Considerations
• Data Protection/ Confidentiality
• Ethics
– NRES response
• Industry Code of Practice
• Safety Reporting systems – MHRA
– Automated A/E reporting
– A/E cascade
• Medical considerations/standards
– No interference with prescribing/treatment
Process - management
• Patient response handling and back up
• Review of data as study progresses
• Design of reports
– A/E reporting in agreement with sponsors
• Statistical interpretation and reporting
• Publications
• If appropriate, patient education or further action
e.g. Adherence schemes
Managing data – a dynamic process
Real data from Real patients
Patient
www......./ tel
Database
“continuous”
Output
Analysis and
report
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Registries
Retrospective
Prospective
patient
Presence of YOUR required data
+
+++
Representative population
+
++
Large numbers
+++
++
Linkage of data fields of interest
?
+++
Confirmation of diagnosis
++
++
Standardised measurement
+
+++
Validated PRO’s
+
++
Patient level data
+
+++
Unfiltered patient data
-
+++
Response to unexpected findings
+
+++
Immediacy of data
+++
++
Longitudinal data - Retro/pro ..... spective
+++
+++
Sensitivity
+
++
Summary
• Medical interventions are now being assessed
on the basis of “real world” data
• Current collection methods are inadequate
• Novel systems for collection are required
• Patients are increasingly being involved in
medical decision making
• Collecting “real world” data directly from patients
may be one possible method
www.patients direct.org
Patients Direct
3 Todd Campus
G20 0 XA
United Kingdom
Output Examples
Practicality
Cover all 4 areas of use
65
Project examples
Mapping
Treatment
Pathways
Burden of
Illness
CVA evaluation Problem
Periods survey
Sleep
evaluation
Impact of
opioid use
Wellbeing
Study
CVA evaluation
(depression)
Problem
Periods survey
Family Flu
survey
Drug
Real world
Safety/ PV “effectiveness”
benefit
QOL
Patient
Satisfaction
/experience/
Adherence
Statin
survey
EQ 5D mapping 3 Pulse Rate
level to 5 level
survey
Swine vs
seasonal flu
vaccination
Etanercept
survey
Satisfaction with
Seasonal flu analgesia in OA
vaccination
2008
Etanercept
survey
Satisfaction
with analgesia
in OA
66
PV - Flu Vaccination
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•
•
•
102 leaflets distributed
73 PIN numbers have been entered on the database (72%)
40 male and 33 female aged between 21 and 99 years.
They were asked about pain and discomfort from the vaccination
67
Flu Vaccination - Day 2
•
•
70 responses were entered for Day 2
14 having side-effects.
In response to “What did you do
about these side effects?”
Reported Side Effects
8
redness
15
diarrhoea
10
6
4
2
0
loss of
appetite
other
5
Nothing
Treated it
yourself
0
68
HiVE - H1N1 Vaccination Evaluation
Vaccination Received
800
700
600
Count
500
400
300
200
100
0
H1N1 only
seasonal only
both
+travel/pnemococcal
HiVE - Demographics
Age of Participants
45%
40%
35%
Male – 449
Female - 663
30%
25%
20%
15%
10%
5%
0%
<5
5-29
30-49
50-69
70 +
HiVE - Adherence
Number of Participants
1200
1112
1000
800
716
646
600
570
400
200
0
Baseline
Week 6
Week 12
Week 26
HiVE – Side effects
Any Side Effects over time
50
45
χ2-test
p <0.001
40
35
Swine
H1N1only
only
30
Seasonal only
Both
25
20
15
10
5
%
0
Baseline
week 6
week 12
week 26
Injection
site
discomfort
36.4%
Flu-like
symptoms
23.5%
Injection
site pain
20.1%
HiVE - Absenteeism
Time of Work due to flu like symptoms
6
% with time off
5
H1N1 only
4
Seasonal Only
3
Both
2
1
0
Baseline
Week 6
Week 12
Week 26
HiVE – Pain/Discomfort Reports
Category
Sex
Chr illness
Swine flu vaccine
Seasonal flu vaccine
Odds ratio
P value
Male
female
2.08
0.052
No
Yes
1.32
0.052
No
Yes
4.49
<0.001
No
Yes
0.89
0.481
Subject
participates
“Buy-in”
Subject +
Buddies +
“Virals”
Enrols
“Buddy”
The
Virtuous
Cycle
“Viral
Transmission”
Feedback on
outcome
Engaged for
next year
Enbrel Project
• Delivery at home
• Compliance with drug still low
• Understand agency effectiveness, reasons for
drop outs
• Understand real life prescribing vs guidelines
• Evaluate and then implement ways of improving
outcomes
• Measures – AEs, compliance, QOL – disease
specific and generic EQ5D
76
Enbrel - Process
• Patient group recruited through leaflets with
hospital clinic or HaH nurse visit to train on
injection
• Initially 6 month follow up at 1 month intervals
• Recently extended to 2 year follow up at 3
month intervals
77
Enbrel – First 6 months
•
•
•
•
Enrolled 344 patients
Out of ~1000 leaflets distributed
284 patients by web site
60 patients by telephone
• 93-100% completion of questions at
baseline
• 140 patients have reached month 6
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If you are experiencing problems with the website or any of the questions please contact the following number and we will endeavour te help you: 0800 731 2647
Age of Participants
180
160
140
Number enrolled
120
100
All
80
RA only
60
40
20
0
< 10
10-19
20-39
40-59
60 +
missing
Baseline Use of
Methotrexate
100%
90%
80%
70%
60%
no
50%
yes
not sure
40%
30%
20%
10%
0%
All
RA
AS
PsArth
Intermittent events- data gathering
• Examples – MS, epilepsy, gout, infectious
illness, depression
• Pre programmed questionnaire – timing ??
• Baseline then every month/ 2 weeks recall?
• Simple email – yes or no
• Rely on people saying when they have
“event”
82
Intermittent Condition –
Problem Periods
• Capture QoL changes when worst time of
cycle
• Variation within and between women
• Compare to “normal” time
83
Results – Screening survey
 2699 respondents
 Age – good range from < 20 to > 49 (61% < 30)
 Absenteeism – 3+ days/mth 6%
1-2 days/mth 16%
84
Results – Main Outcomes
 Significant impact on ALL Quality of Life Scales
 Significant change between the different times of
period cycle
85
Main outcomes – EQ5D
EQ5D Score
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Mean
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6
86
SF 36 utility score
Boxplot of SF6D_R2
1.0
0.9
SF6D_R2
0.8
0.7
0.6
0.5
0.4
0.3
Day 1
Day 6
Day
87
Resource utilisation
•
•
•
•
•
•
•
Routinely gather PRO on
All Medication related to condition
Doctor visits (Primary & Secondary care)
Nurse visits
Pharmacy visits
Telephone calls (if relevant)
Hospital In/ Out Patient visits and number of
days
88
A real world study using Patient Reported Outcomes to assess
the consequences associated with the forced switching of asthma
medication/device in stable adult asthma patients
• Data to be captured from Patients
themselves:
•
baseline patient reported level of Asthma
control/satisfaction with device
•
Reasons for switch (if known) (prospectively)
•
Outcomes of switch: Clinical (FEV1, Control) Health
resource use (GP visits, hospitalisations) lifestyle impact
(days off work) (prospective)
• The questionnaires would be administered monthly
89 (prospectively) so that any changes are captured
Cystic Fibrosis
• Unmet need
• Gathering information on children/ adolescents/
parents
• Adherence, satisfaction, burden of illness
90
Conclusion
• If you need real world PRO data in Europe/US
• Databases don’t collect info you need
• Patients Direct can collect the data, directly from
the patient
Quick, efficient, cost effective solution
91
Market Access
• Burden Of Illness Study – Depression
Management
• Understanding Patients ability to monitor
their own condition – Heart Rate survey
• Disease treatment pathway mapping –
CVA Study
• Mapping new EQ5D
• Are QALYs appropriate across EU ?
92
Burden Of Illness
- 300 pts depression
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•
•
•
•
•
Socio-demographics,
PMHx,
Resources use – Client Service Receipt Inventory
QOL - EuroQoL
Productivity – WHO Health & Productivity Questionnaire
Depression – HAM-D, MADRS
93
Burden Of Illness
• Utility weights UK population data
• and EQ5D
• → QALYs
94
Burden Of Illness
• Depression severity - cost
• Depression severity - QALYs
95
Disease Pathway and Management
• Medical pathway of ischaemic stroke until 1 year
acute episode
• Cost of stroke management
• Cost drivers
• Comparison between UK, France, Germany
96
Disease Pathway and Management
• Socio-demographic
• Pre hospital management – PMHx, 1st contact,
transport
• Hospital management – treatments,
investigations
• Post hospital management - rehabilitation
97
EQ 5D Mapping
• New 5 level questionnaire (from 3 level)
• 500 pts UK
• Different levels disability
• CV disease, Respiratory, Neurological, RA
98
Use of QALY across EU
• FP 7 grant
• Pan European – UK arm (with A Walker)
• University of Lyon
• Identification of different methods in HTA
• Review of different methods
• Alternatives to QALY
99
HE and OR
• Involved in questionnaire mapping
• Gathered data via various QoL scales
• Gathered data on inputs and outputs costs, diseases, outcome
• Individuals involved in SMC and NICE
submissions and Advisory Boards
• Access and use of UK experts –
Robertson Centre
100
Summary
• Bespoke Innovative solutions
• Professional Quality Assurance /control
• History of delivering results
– On time
– Value for Money
• Tailored to sponsor brief
• Multiple Applications
101
Current data sources
Source
Randomised Controlled Trials
PMS Studies
Physician driven
Patient registries
Prescribing data
Database/physician input
Focus groups
Patient organisations
Patients