Welcome to the Centre for Biostatistics Showcase

Weds 3rd May 2017

Biostatistics Collaboration Unit

(BCU)

Andy Vail, Centre Lead for Biostatistics

Four of ten cats were tabbies

What is estimate (‘best guess’) of population proportion?

Answer: 40% = 4/10

Or: 33% = 3/9?

Or: It depends!

Biostatistics Collaboration

Seek input from Senior Biostatistician

Expertise in design, analysis and interpretation

Co-applicant or Named Collaborator as appropriate

Include costs for Biostatistics RA/RF if required

Time-consuming activity

Part posts

‘Medical model’ (typical NIHR clinical research)

Senior Statistical co-applicant, small percentage supervision time

RA/RF Biostatistician to

Agree data collection & management mechanisms

Undertake interim monitoring

Program analyses, graphs, tables

Co-author results and discussion sections

E.G. Randomised trial rule of thumb: average 33% throughout

Advantages

Statistical work by professional statisticians: reassures funders

Full recovery of costs: pleases Faculty

Higher quality publications: more likely to be rated 3* or 4*

Possible to appoint to part-time, short-term posts

Cover for prolonged absences

Attraction and retention of high quality staff: career progression

and professional support for staff

Contacts

http://research.bmh.manchester.ac.uk/biostatistics/collaborationunit

Wendy Lamb, Biostatistics Administrator: 0161 275 5764

Links

http://www.rds-nw.nihr.ac.uk Research Design Service

http://www.mahsc-ctu.co.uk Clinical Trials Unit

Biostatistics collaborations with the Centre for Primary Care

David Reeves

Centre for Biostatistics Showcase, May 2017

Primary Care embedded stats team

Currently:

David Reeves Mark Hann Fiona Williams Stephen Pye

Previous members:

Evan Kontopantelis David Springate Ivan Olier

Hannah Gaffney Cath Fullwood Liz Middleton

Damien McElvenny Isla Gemmell Chris Roberts Mark Harrison

Part of NIHR National School for Primary Care Research - network of 9 leading academic units

“Outpost” of biostatistics housed in the Centre for Primary Care, in existence since around 2000. DR on management teams of both Biostats and CPC.

Primary Care embedded stats team Team members also collaborate with research groups outside Primary Care

Nursing, Pharmacy, Neuroscience, Psychology and mental health, Human Communication, Manchester Royal Infirmary, Central Manchester Foundation Trust Prima-eDS (Andreas Sönnichsen, Germany) EU Framework 7. Development and testing of decision-support software to help GPs reduce polypharmacy. 5-country RCT (UK, Germany, Italy, Austria, Finland)

Sense-Cog (Iracema Leroi, Neuroscience) EU Horizon 2020. Sensory impairment screening tools for PwD. 3-country (UK, France, Greece) RCT of sensory support intervention.

Epihealth (Daniel Brison, CMFT) EU Framework 7. Observational study of impact of parental and IVF factors on babies’ neonatal health and subsequent growth.

Neighbourhoods and dementia (John Keady, Nursing) ESRC international. Lived experience of PwD. Core outcome set for dementia trials; staff dementia awareness training and hospital outcomes for PwD.

Pathway (Adrian Wells, MRI) NHIR Programme grant. RCT of meta-cognitive therapy for anxiety and depression in cardiac rehabilitation patients.

Major themes of Primary Care research

90% or more of team’s work done is applied research. Major primary care research themes include:

Supported self-management Quality of care Financial incentivisation Patient safety Primary care workforce Collaborative care Multimorbidity/complex needs Methodological studies often done as “spin-offs” :

Measuring quality of care Meta-analysis methods Validity of PCDs Apps to facilitate use of PCDs Measuring patient frailty

Patient self-management Major programme 2000 - ongoing

Key researchers:

Anne Rogers, Anne Kennedy, Pete Bower, David Thompson, Tom Blakeman, Gerry Richardson

Statisticians:

David Reeves, Hannah Gaffney, Liz Middleton, Mark Hann, Evan Kontopantelis, Chris Roberts, Mark Harrison

Pragmatic RCTS

IBS – information and self-help groups for people with IBS

EPP (Expert Patients Programme) – evaluation of national programme to provide EPP training to people with long-term conditions

WISE – training of practice staff to support patient self-management

BRIGHT – information and telephone support to people with stage 3 CKD

Cohort multiple RCT

CLASSIC with PROTECTS – cohort multiple RCT of telephone-based health coaching for people with two or more chronic conditions

Patient self-management Social network methods:

U-NET – The contribution of social networks to care for people with chronic conditions

Moderator/predictor analysis:

Quality and incentivisation of Care Evaluating QOF using national data for ~8,000 practices

Researchers and statisticians:

Martin Roland, Stephen Campbell, Tim Doran, David Reeves, Evan Kontopantelis, Cath Fullwood, David Springate

Indicator development and roll-out

GAP and QUIP – projects to develop and pilot PC quality indicators

Quality and incentivisation of Care Evaluating QOF using CPRD “big data”

Other methods Meta-analysis Factorial designs

Cohort and case-control studies

Apps

Psychometrics

Structural equation modelling

Now trending….

Digital apps - technology for health

Kinecting for Health Feasibility trial of use of Microsoft Kinect for remote monitoring of health risk events in the houses of elderly people. MRC CIC-funded.

Big data – big in volume and big in linkage

Proposal to use ML methods to develop an improved dementia risk prediction tool based on the GP record.

Novel research designs - more answers quicker!

CLASSIC with PROTECTS Cohort multiple RCT of telephone-based health coaching

Primary care apps - making a dash for the dashboard! Pfish frailty index study Measure of patient frailty derived from the GP record. Further development of the eFI. SPCR-funded 18 month study

Embedded versus “centralised” collaboration

Embedded Centralised

Advantages Accessible to researchers

Continuity of involvement

Involved in study from the design stage

Develop expertise in topic area and relevant methods

Easier for statisticians to draw on peer-group expertise

More chances to develop statistical methodology research

Opportunities to collaborate with broader range of research groups

More involvement with national and international stats community

Disadvantages Risk of isolation from rest of biostats

Fewer chances to do stats methods research

More difficult to become a research leader.

Tendency to become involved only in latter stages of studies!

Less contextual knowledge of a specific research area

More likely to be involved on a purely “support” basis

MRC North West Hub for Trials

Methodology Research

Dr Matthias Pierce, Research Fellow

Centre for Biostatistics Showcase, Weds 3rd May

The Trials Network

• The MRC-funded Hub provides a network of researchers addressing methodological

priorities established by stakeholders

– CTU’s, industry, charities and government bodies.

• ‘Improve health by improving trials’

– Improve trials by improving their: efficiency, validity, scope and robustness

• The Network funds specific projects, PhD’s, workshops and training events.

– 5 Hubs placed across the UK.

– Nine-working groups targeting areas of interest

North West

CTSU BSU

London

MRC Network of Hubs for Trials

Methodology Research (HTMR)

and partners

ConDuCT II

www.methodologyhubs.mrc.ac.uk

The North West Hub

• Spread across Liverpool, Lancaster, Manchester and Bangor

• Themes covered:

– Theme 1: early phase trial design and analysis

– Theme 2: later phase trial design and analysis

– Theme 3: patient perspectives

– Theme 4: stratified medicine

– Cross-cutting clinical and health economics theme

Stratified medicine theme

• Stratified medicine: can we move beyond ‘ one size fits all’ or ‘trial and error’

approaches to medicine?

• Psoriasis: understanding determinants of response to biologic therapies

• Schizophrenia: predicting those are unresponsive to dopamine-targeting drugs

• Can biomarkers (proteomics, genomics, patient characteristics etc.) identify

subgroups with different treatment responses?

• What trial designs are there to assess the validity of biomarkers and how can we

choose between them? http://www.bigted.org/

Predicting treatment

response Forming a model for predicting treatment response is different (harder) to one for

predicting disease (diagnosis) or disease outcome (prognosis)

Consider a patient who died 2 years after a heart attack:

Prognosis: what was their mortality rate?

Consider a patient in a trial who received treatment X (rather than Y) and died after 2

years:

What was their response to treatment?

We need to infer what would have happened to the patient under alternative

treatment

0.5 per person per year

0.5 per person per year vs mortality rate under treatment Y

Personalised treatment

recommendations

• A PTR is an algorithm that recommends treatment A over B for patients with particular

biomarker values

• For example, prescribe statins: >45 years old AND CVD risk >10% over 10 years

• Using trial data:

– How to identify the best PTR using multiple biomarkers?

– How to evaluate a personalised treatment recommendation, over an alternative

policy where everybody receives the same treatment?

ptr.ado

• Find out more at Centre for Biostatistics seminar 12th June!

NIHR Research Design Service Design Service

Sarah Rhodes

Frontline Adviser/Case Manager

Research Design Service

• The Research Design Service (RDS) provides

free advice on research design to researchers

who are developing proposals for national, peer-

reviewed funding competitions for applied health

or social care research.

Statistics input to RDS

Greater Manchester

• 2 Senior Methods Leads (Andy Vail, David

Reeves)

• 3 Advisers (Mark Hann, Sarah Rhodes, TBC)

• Expertise in Clinical Biostatistics, Community

Biostatistics and Systematic Reviews

Caseload of an RDS Statistician:

• Formulating research question

• Study Design

• Choosing outcome measures

• Planning statistical analysis

• Identifying statistical collaborators (BCU)

• Training events

Case Management

• Appropriate funding streams

• Putting together team

• How to write a good application

• Critical review

• Managing support from other advisers

• Clinical Trials Unit involvement

• Public involvement

• RDS outreach

• Funding panel observations

Successes

• Research funding secured with RDS NW help

– Over £163 million since 2010

– Over £78 million with Manchester Statistician as case manager

– Over £2 million personally since Sept 2015 (7 projects)

Examples of success

Subject Funding stream RDS involvement

Mental health NIHR HTA Evidence Synthesis • Suggested statistical collaborator

• Critical read

Suicide prevention NIHR Efficacy and Mechanism Evaluation

• Help to address Stage 1 comments

• Critical read • Methodology

Cardiovascular British Heart Foundation • Design (including sample size)

• Behaviour change

Collaborations

• RDS statisticians work with CTU, CLAHRC,

Biostatistics Collaboration Unit and often have

multiple roles

• Sometimes it is appropriate for RDS adviser to

become a collaborator

• Personally awaiting decisions on RfPB

(Renwick), HTA (Yorke) and HS and DR

(Cotterill)

Other Collaborations

PhD Aim: Develop statistical

methodology for the analysis

of observational data for

personalised/stratified

medicine

Antonia Marsden. Centre for Biostatistics Showcase 2017

Funder: NIHR Manchester Musculoskeletal Biomedical

Research Unit

Supervisors: Prof Richard Emsley, Prof Graham Dunn & Prof

Will Dixon

PhD overview

Is variable X predictive of differential

treatment response?

Antonia Marsden. Centre for Biostatistics Showcase 2017 Antonia Marsden. Centre for Biostatistics Showcase 2017

Compare

subgroup-specific

treatment effects

Stratify

Motivating example: are any patient characteristics associated

with the onset of type 2 diabetes as a side-effect of glucocorticoid

(steroid) therapy in adults with rheumatoid arthritis (CPRD data).

Research Topics:

1. Comparison of relative vs absolute treatment effects

2. Confounding adjustment for the comparison of subgroup-specific

treatment effects.

3. Study design when using routinely collected observational data.

PSORT

Antonia Marsden. Centre for Biostatistics Showcase 2017 Antonia Marsden. Centre for Biostatistics Showcase 2017

Project Aim: To better understand

response to biologic therapies for the

treatment of psoriasis.

Project Motivation:

- Response to biologic therapy varies

- There are several different biologic

therapies to choose from

- Biologic therapies are expensive.

Developing the evidence base for evaluating dementia training in NHS hospitals

40% of acute hospital beds are occupied by people with dementia (PwD) (Dementia Strategy, Dept. of Health 2016).

Staff training per se is only one domain of dementia-friendly care.

Mix of training packages in dementia care being delivered.

Data sources:

National Audit of Dementia (NAD) care surveys in 2010, 2012 and 2016.

Lancaster University & UoM organizational survey launched

in Jan 2017.

Data for all patients admitted to ~200 NHS acute hospitals in England is captured in Hospital Episode Statistics (HES). Will use a dementia diagnosis code in last 5 years to identify a PwD.

• At the hospital level will link NAD & organizational survey data to HES data for financial years 2010/11, 2012/13 and 2016/17.

Matched cohort design and analysis

• For each PwD in each analysis year will select up to 5 matched

control patients without dementia at same hospital. • Comparison outcomes to include: length of stay ; readmission within

30 days; death within 30 days of discharge; pressure sores and number of in-hospital falls.

• Survival & multi-level linear/logistic regression controlling for

confounders. • Which components of training are most strongly related to outcomes

for PwD?

Stephen Pye

The Patient Frailty Informing Stratified Health Care (Pfish) study

Clinical Practice Research Datalink (CPRD)

• Previously known as General Practice Research Database (GPRD)

• Anonymised electronic GP health records

• Around 700 GP practices

• Over 15 million patients

• Representative of UK population

Electronic Frailty Index (eFI) • Uses cumulative deficit model of

frailty

• Comprises of ~1,500 coded signs, symptoms, tests, referrals, diagnoses etc.

• Codes mapped to 36 ‘deficits’

• Deficits combined to obtain frailty score and classification

• Score has been shown to relate to patient outcomes

Deficit: Mobility and transfer problems

Code Description

1381. Exercise physically impossible

13C2. Mobile outside with aid

13C4. Needs walking aid in home

13CD. Mobility very poor

13CE. Mobility poor

398A. Depend on help push wheelchair

Aims of Pfish

0

0.12

0.24

0.36

0.48

0.6

70 72 74 76 78 80

Frai

lty

sub

-do

mai

n s

core

s

Age (years)

Physical

Cognitive

Social

2. Multidimensional frailty – map deficits to sub-domains and test with factor analysis

3. Investigate epidemiology of frailty and individual patient trajectories

1. Refine measure – extra deficits (55), remove “odd” codes, allow signs to resolve

Sub-domains of the FI-CGA

eFI deficits Cognitive Mood Communication Mobility Balance Bowel Bladder Nutrition ADLs Social Comorbidity

Activity limitation

Anaemia and haematinic deficiency

Arthritis

Atrial fibrillation

Cerebrovascular disease

Chronic kidney disease

Diabetes

Dizziness

Dyspnoea

Falls

X

X X

X X X

X

X

X

X