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Randomising every patient you see: Software for clinic seat research

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.

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Randomising every patient you see: Software for clinic seat research

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  1. 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

  2. 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.

  3. 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

  4. 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

  5. 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%

  6. Generalisability RCT 300 patients from 250 GPs 300 consecutive patients from 3 GPs Which has the most generalisability?

  7. Office RCT Internet based software Make RCTs from office chair simple Answer simple research questions as part of every day clinical work

  8. Ideas for RCT Gratitude diaries Telephone call from practice nurse

  9. 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 • LyubomirskyS.The How of Happiness: Penguin • No trials in primary care • Anecdote 90%+ patients happy to do them.

  10. 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

  11. 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

  12. 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

  13. 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

  14. Doug Kingsford-sleep deprived

  15. Why PHQ-9 and GAD-7 • 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

  16. 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

  17. Spectrum No gold standard ie post mortem won’t tell you if the person is depressed. Sadness →→→→→→→→→→Depression ↓ Consider Treatment if severe or persistent

  18. DSM IVDiagnostic and statistical manual of mental disorders

  19. What is in paper form? Consent form –need to be kept/scanned Information sheet Intervention instructions

  20. Follow up data entry Less than 1 minute

  21. 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

  22. PHQ 2009 Total = 19 Below 10 = 16 people 10-14 = 1 person 15-19 = 2 person 20+ nil

  23. GAD Below 8 =14 persons 8 or more = 5 persons

  24. Missed consent tick

  25. 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

  26. Start testing July 2010 • Ethics underway • Beta test in own clinic

  27. Advice Control activity ? 3 memories of past Ok to collect data over randomisation Follow all at 4 months or as they have come in

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