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Doing your own research
David GoldbergInstitute of Psychiatry
King’s College, London
Workshop on the Professional Development of Young Psychiatrists, Nairobi, Kenya
20 - 22 March 2007
RUMBA!
R Relevant
U Understandable
M Measurable
B Behaviour should be influenced
A Attainable
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
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
Reading round the subject:
Medline, Psychlit
Decide on keywords
Limit your search
Follow Key papers
How to make notes!
- note FULL reference right away
THE RESEARCH PROTOCOL:
Title
Aim (disproving the null hypothesis)
Background
Method
Power calculation
Statistical treatment of results
Your name
Supervisor’s name
THE PROJECT LOG:
Time Budget
Pilot study
Main study
- dates
- patient quotes
Relevant papers
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!
Deciding on methodology:
Simple descriptive studies
2-stage screening studies
Case-control studies
Efficacy of treatment studies
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)
Studies of risk:
DISEASE"sick"
NO DISEASE"healthy"
EXPOSURE "a" "b"
NOEXPOSURE "c" "d"
COHORT STUDY
forwards in time
a / (a+b)
c / (c+d)
RELATIVE RISK a / (a+b)
c / (c+d)
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 year 13.5
Studies of risk: DISEASE
"sick"NO DISEASE
"healthy"
EXPOSURE "a" "b"
NOEXPOSURE "c" "d"
Retrospective study, backwards in time
a/c b/d
ODDS RATIO
a/c = ad
b/d cb
Example of odds ratiosRisk of any mental disorder in -
Epidemiological Catchment Area Data, Los Angeles: Odds Ratio:
Hypertension 1.28
Diabetes 1.29
Physical Handicap 1.44
Cancer 1.73
Heart Disease 1.97
Neurological disease 2.14
Odds ratios for depression:
Sam Dworkin, Primary Care, Seattle USA:
No. of pains: No. of patients: ODDS RATIO
None 371 1
One pain 346 1.04
Two pains 205 5.74
3+ pains 94 8.55
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
Information bias RECALL BIAS Those with
disorder recall exposure betterREMEDY: 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
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
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
Example of confoundingIs depressions related to liking chocolate?
So, depressives are more than three times as likely to love chocolate?
Bothsexes
Depressed NotDepressed
LikesChocolate
65 500No special
preferences25 650
OR = 3.38
Stratify data by gender:MALESONLY
Depressed NotDepressed
LikesChocolate
5 200No special
preferences15 600
OR = 1
FEMALESONLY
Depressed NotDepressed
LikesChocolate
60 300No specialpreference
10 50
OR = 1
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
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
Sample sizePOWER: 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