randomising every patient you see: software for clinic seat research bruce arroll douglas kingsford...

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Randomising every patient you see: Software for clinic seat research Bruce Arroll Douglas Kingsford Antonio Fernando III 2010 Department of General Practice & Primary Health Care Faculty of Medical & Health Science University of Auckland, Auckland, New Zealand

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Randomising every patient you see:Software for clinic seat research

Bruce Arroll

Douglas Kingsford

Antonio Fernando III

2010

Department of General Practice & Primary Health Care

Faculty of Medical & Health Science

University of Auckland, Auckland, New Zealand

The problem with RCTs

Expensive Exclude many of the patients we see

May not be the patients we usually see-restrictive Expensive Time consuming Huge amount of paperwork If paper based need data entry

How generalisable are they How to get a cheap computer generated randomisation

code to ensure concealed allocation.

McVickers paper

– Vickers AJ, Scardino PT. The clinically integrated randomised trial proposed novel method for conducting large trials at low cost. Trials (Biomed Central 5/3/2009)

Integrate RCT in to clinical practice Randomise every eligible patient Eligible if clinician uncertain-broad-crucial point Adverse effects, me too drugs; lifestyle Patients could enter own data eg adverse effects

Randomisation “daily”

20 starter packs of Varenicline (Champix)

Give them out haphazardly or systematically

“Pressured” my colleagues to be systematic Mailed a letter to 40 Maori smokers offering 4 free visits

with or without a starter pack of Champix 4/20 with starter pack replied and 0/20 in control Conclusion “don’t send out letters to Maori patients”

Without a starter pack

Better way would be phone call or face to face

Pediatric Oncology-the challenge

Every child with a malignancy in the (developed

world) is in a randomised controlled trial

Contributed to increase in survival for leukemia from about 30% to 90%

Generalisability RCT

300 patients from 250 GPs

300 consecutive patients from 3 GPs

Which has the most generalisability?

Office RCT

Internet based software

Make RCTs from office chair simple

Answer simple research questions as part of every day clinical work

Ideas for RCT

Gratitude diaries

Telephone call from practice nurse

Gratitude diaries 3 things grateful for and what did to cause them daily for

one week. Control group writes down early life memories. ? Then once per week

RCT done with internet sample had an effect up to 6 months later. CES-D 13 vs 10.5 Seligman ME, American psychologist 2005;60;5:410-21

Once per week enough Lyubomirsky S.The How of Happiness: Penguin

No trials in primary care Anecdote 90%+ patients happy to do them.

Research Question Does giving depressed patients a gratitude diary vs

control writing improve their PHQ outcomes at 4 (?) months

Inclusion criteria broad Any one with PHQ ≥ 10 (600 pts in 4000 patients) Excl Bipolar; severe drug/alcohol;dementia; personality

disorder; eating disorder; persisting psychotic illness; life expectancy < 2 years; patient unreliable at attending appointments

Research Issues ? Follow up as they come in versus attempt a formal

follow up

A sample size of 98 patients in each arm will be required to demonstrate a 2 point reduction on the PHQ from 13 to 11 with a standard deviation of 5

Issues Follow up and analysis

Stop trial at 4 months and do last value carried forward

Or

Stop trial at 4 months and take data as found (akin to Kaplan Meir)

– Analyse using random coefficients which would compare the gradient of the intervention group and the control group

Develop office based software Able to get randomisation code from clinic computer

Data gets stored on web No extra data entry required

Multiple doctors/nurses can submit data from different clinics-international collaboration possible

Privacy assured as each clinic uses own patient file identifier Simple entry criteria and simple outcome criteria Eg 600 patients with PHQ ≥ 10

Doug Kingsford-sleep deprived

Validated in primary care In NZ Arroll et al 2010 (not published)

Short Gives a “diagnosis” categorical Also continuous score to monitor improvement Free from June 2010 Currently been used in decision support in our district

health board therefore familiar and used

Why PHQ-9 and GAD-7

Data collected after randomisation-next slide

Previous psych contact and admissions asked after

randomisation

Done to simplify recruiting

Not part of primary outcome

Used for exploring for future studies

? Cause any bias

Spectrum

No gold standard ie post mortem won’t tell you if the

person is depressed.

Sadness →→→→→→→→→→Depression

Consider Treatment if severe or

persistent

DSM IVDiagnostic and statistical manual of mental disorders

What is in paper form?

Consent form –need to be kept/scanned

Information sheet

Intervention instructions

Follow up data entry

Less than 1 minute

PHQ

9 questions over past month

Score 10-14 mild major depression

Score 15-19 moderate major depression

Score 20+ severe major depression

Advantage a dichotomous score and a continuous scale

to monitor improvement

PHQ 2009

Total = 19 Below 10 = 16 people 10-14 = 1 person 15-19 = 2 person 20+ nil

GAD

Below 8 =14 persons 8 or more = 5 persons

Missed consent tick

Analysis

Both ways i.e. all at 4 months –send out/phone for follow up At 4 months according how they have come in

– Using random coefficients analysis of slopes

Analyse all Analyse by PHQ 10-14 and >14

>14 more difficulty with functioning and may be less able to do Gratitude diaries

Start testing July 2010

Ethics underway

Beta test in own clinic

Advice

Control activity ? 3 memories of past

Ok to collect data over randomisation

Follow all at 4 months or as they have come in