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Doing your own research David Goldberg Institute of Psychiatry King’s College, London Workshop on the Professional Development of Young Psychiatrists,

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Presentation on theme: "Doing your own research David Goldberg Institute of Psychiatry King’s College, London Workshop on the Professional Development of Young Psychiatrists,"— Presentation transcript:

1 Doing your own research David Goldberg Institute of Psychiatry King’s College, London Workshop on the Professional Development of Young Psychiatrists, Nairobi, Kenya 20 - 22 March 2007

2 Snow White & the 7 Dwarves 1Infatuation (psychotherapy?) 2Methodological fad (dexamethasone?) 3Needless replication 4Methods not available locally 5Patients not available locally 6No funding! 7No-one to supervise your work (Snow White = A real gap in knowledge)

3 RUMBA! RRelevant UUnderstandable MMeasurable BBehaviour should be influenced AAttainable

4 This goes though stages: Thinking of an idea Reading round the subject Deciding on the method Research protocol & project log Finalising the procedures: a pilot Doing the fieldwork Processing the results Writing up

5 Thinking of an idea: Read a journal: would it work here? - adapt an instrument? Help an experienced investigator think of your daily work experiments of opportunity value of training course? KISS : Keep It Simple, Stupid

6 Reading round the subject: Medline, Psychlit Decide on keywords Limit your search Follow Key papers How to make notes! - note FULL reference right away

7 THE RESEARCH PROTOCOL: Title Aim (disproving the null hypothesis) Background Method Power calculation Statistical treatment of results Your name Supervisor’s name

8 THE PROJECT LOG: Time Budget Pilot study Main study - dates - patient quotes  Relevant papers

9 A Time budget Start with today’s date, end with time research must be handed in Time for instrument preparation Pilot study Main field work Processing your results Writing up Time for supervisor to read it Time for you to make corrections and INJURY TIME!

10 Deciding on methodology: Simple descriptive studies 2-stage screening studies Case-control studies Efficacy of treatment studies

11 Descriptive studies: DON’T just look at level of mental morbidity! Look for an internal comparison: measure mental status, AND other characteristics (eg physical feature - extent of diarrhoea, extent of eczema; social features - quality of parenting, social deprivation)

12 Two stage designs Deciding on your threshold - but, do you really need one? PPV and prevalence Weight your sample back to the parent population before computing validity coefficients! Example: 200 cases selected from 553 consecutive attenders - these 200 must be weighted back to represent same proportions as occurred originally

13 Raw data: So, high scores were undersampled by 178/102 = 1.75 Low scoreres were undersampled by 375/98 = 3.80 Multiply last 4 cells by these numbers

14 Corrected data: Number of cases interviewed now equals 553, the original number screened This has effect of reducing sensitivity, but improving Sp & PPV. Sp = 35970/382.4 = 94% Se = 15530/170 = 91%; PPV = 15530/178 = 89%

15 Studies of risk: COHORT STUDY forwards in time a / (a+b) c / (c+d) RELATIVE RISK a / (a+b) c / (c+d)

16 Example of relative risks: Norman Kreitman, Edinburgh: Compares each stratum with risk for employed people UNEMPLOYED MEN RR <4 weeks 4.3 1/12 to 6/12 3.0 6/12 to 1 year 4.6 > 1 year13.5

17 Studies of risk: Retrospective study, backwards in time a/c b/d ODDS RATIO a/c = ad b/d cb

18 Example of odds ratios Risk of any mental disorder in - Epidemiological Catchment Area Data, Los Angeles: Odds Ratio: Hypertension1.28 Diabetes1.29 Physical Handicap1.44 Cancer1.73 Heart Disease1.97 Neurological disease2.14

19 Odds ratios for depression: Sam Dworkin, Primary Care, Seattle USA : No. of pains: No. of patients:ODDS RATIO None3711 One pain3461.04 Two pains2055.74 3+ pains 948.55

20 Case-Control Studies Incident or prevalent cases? (incident, for aetiology) Selection bias (are controls representative of sick?) Information bias (subject; observer) Confounding - factors that produce spurious results

21 Information bias RECALL BIAS Those with disorder recall exposure better REMEDY: Structured questionnaires, incident cases, i/v close relatives, controls with different disorder OBERVER BIAS: You may probe index cases more closely REMEDY: “blind” the observer, non-medically trained interviewers, tape record, computer administered i/vs

22 Selection bias Do controls give biassed estimate of risk? DON’T use hospital volunteers: friends or neighbours of patient, or non-affected relatives are much better GENERAL RULE: A control should become part of the index group if he or she were to develop the condition

23 Example: children at home: (Selection bias) So, hospital depressives are equally likely to have children at home as controls (OR = 1.01) But, community depressions are much MORE likely to have children at home (OR = 3.77)

24 Confounding: CONFOUNDERS can lead to spurious associations, OR can eliminate an association that is really present Possible confounders: Sex, age, social class, presence of children at home

25 Example of confounding Is depressions related to liking chocolate? So, depressives are more than three times as likely to love chocolate? OR = 3.38

26 Stratify data by gender: OR = 1

27 Conclusion from this? No relationship whatever between liking chocolate and depression: however Females more likely than males to like chocolate, and more likely to be depressed - the “chocolate/gender” relationship has CONFOUNDED the pooled analysis MORAL: stratify your data for variables that may be confounding the main relationship you wish to explore; AND match cases and controls very carefully

28 Confounding: CONFOUNDERS can lead to spurious associations, OR can eliminate an association that is really present Possible confounders: Sex, age, social class, presence of children at home REMEDIES: MATCH groups for potential confounder; multiple controls for each case [increases power] RESTRICT study to narrow range of variables STRATIFY by presence/absence of confounder

29 Some outcome measures: Clinical status : SCAN, CIDI, BPRS, PAS, PROQSY Social Functioning : SIS, SP scales Disability : WHO BDS, Groningen Quality of Life : EUROQUOL, Lancashire Unmet needs : CAN, PeliCAN Satisfaction : Verona Satisfaction Scales

30 Sample size POWER: is the ability of a test to show that a relationship exists, when it DOES exist. Also called “false negative”, or Type 2 error. Is sample size big enough? SIGNIFICANCE: is the probability we shall make a false claim, and say a relationship exists which did so by chance. (Also called “false positive”, or Type 1 error). Usually set power at 0.80 (giving an 80% chance of showing a relationship), with significance at 0.05 (giving a 5% chance of a false claim

31 For example - How big a difference would you be impressed by? (The smaller the difference, the larger the sample!) With power at 0.80, and significance at 0.05 Difference Sample size 30% 95 15%160 10%360 Can be looked up in nomograms!

32 OK, now you can start?  Are you now trained in the clinical interview?  Are the measuring instruments appropriate in your setting?  Are the patients available, will the nurses co- operate?  Have you obtained ethical committee approval?  YES: Then try it all out, noting date in your project log. If you have to change ANYTHING, call it a pilot study  Arrange a meeting with your supervisor very quickly, to report that all is now ready

33 During the fieldwork: Have you seen a statistician yet? Are you familiar with the statistical tests you will be using? You can now write up the “method” Why not make a start on your literature review, as well? Remember, note down interesting things in your “project log”

34 Processing the results: Be sure the see your supervisor at least once during this time Discuss your findings with your statistician Finish the “literature review”, if you are writing a thesis

35 Finishing it all off: Decide on your main findings Discuss new findings in the light of the literature review Admit any shortcomings in the work What are the implications of your findings - if there are any! Write the abstract, the conclusions, and finally - the introduction (the hardest bit) Why not publish a paper as well - but, which journal?


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