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