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Goldsmith’s teachers lecture 2011 Medical statistics Joan Morris Professor of Medical Statistics. To describe medical statistics To give examples of where medical statistics has contributed to society Use of statistics in screening To mention some novel statistical methods. Aims.

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slide1

Goldsmith’s teachers lecture 2011

Medical statistics

Joan Morris

Professor of Medical Statistics

slide2
To describe medical statistics

To give examples of where medical statistics has contributed to society

Use of statistics in screening

To mention some novel statistical methods

Aims
statistics definition
Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. Statistics - definition
data collection1
Florence Nightingale

She realised that soldiers were dying from malnutrition, poor sanitation, and lack of activity.

She kept meticulous records of the death toll in the hospitals as evidence of the importance of patient welfare.

Data Collection
national data collection
National Mortality Statistics

Health survey for England and Wales

Population statistics …..

Large amounts of data are available on the web

National Data Collection
slide10

All births in England and Wales according to maternal age :1989-91 compared with 2005-2007

2005-07 : 1991,000 births

1989-91 : 2090,000 births

slide11
Comparisons of individuals

Observational

cross-sectionalcase-control studiescohort studies

InterventionalRandomised controlled trials

Comparisons of populations

Time trends

Ecological studies:Geographical variationsAge/sex patternsSocial variations

Epidemiology

Epidemiology

comparison of individuals
Study Design

Ensure “valid” data is collected

Ensure enough data is collected

Main designs

Case control studies

Cohort studies

Clinical trials

Comparison of Individuals
is there a relationship between smoking and lung cancer

British Doctors Cohort Study

(BMJ 1994;309:901-911)

34,000 British male doctors who replied to a postal questionnaire in 1951 and further questionnaires in 1957, 66, 72, 78, 90, …

Flagged the doctors at NHSCR and obtained their death certificates as they died. Compared death rates in smokers and non-smokers..

Is there a relationship between smoking and lung cancer?
what causes sudden infant death syndrome
Sudden Infant Death Syndrome Case Control Study

Methods

Collected information about infants that were potential “SIDS”

Identified “similar” children who had not died

Compared the differences

Results

Children who died were much more likely to have been put on their fronts to sleep than children who did not die

What causes Sudden Infant Death Syndrome ?
randomised controlled trial
A clinical trial is an experiment in which a treatment is administered to humans in order to evaluate its efficacy and safety

Randomised = allocated to groups on basis of chance e.g. tossing a coin (ensures fair comparison)

Controlled = a comparison group

Randomised Controlled Trial
can folic acid reduce neural tube defects e g spina bifida
MRC Vitamin trial - randomised controlled trial

Large: 1817 women who had had a previous NTD, 33 centres, 7 countries

Can folic acid reduce neural tube defects (e.g. spina bifida)?
can folic acid reduce neural tube defects e g spina bifida1
Results : Women who did not receive folic acid were 3 times more likely to have a second NTD pregnancy

Impact : Women are advised to take folic acid prior to becoming pregnant

Majority of countries around the world fortify flour with folic acid

Can folic acid reduce neural tube defects (e.g. spina bifida)?
collection of data
Study Design

Cohort

Case Control

Clinical Trial

Collection of Data
analysis
Could the observed results have arisen by chance ?

Given that we have a sample what can we say about the population from which the sample comes

Analysis
slide24

Neural Tube Defects

Yes

No

Total

Folic Acid

Yes

6

587

593

No

21

581

602

Folic Acid vs Placebo forNeural Tube Defects

Risk of NTD in treated group =

Risk of NTD in control group =

Relative Risk of NTD in treated group compared to control group =

p values
P is the probability of the observed event or one more extreme occurring if the null hypothesis is true

Null hypothesis : No difference in treatments

P = probability out of 27 babies with an NTD what is the chance that 6 or less are in the FA group and 21 in placebo group IF FA has no effect

P values
interpreting the results of a trial
Interpreting the results of a trial

RR death in A vs B = 2.0

Is it due to chance or not ?

p values1
P < 0.05 is taken to mean statistical significance

This means if there is no difference between treatments, and you do 20 trials one will be statistically significant

P values
folic acid vs placebo for neural tube defects
Folic Acid vs Placebo forNeural Tube Defects

RR = 0.29

P = 0.008

Therefore we assume there is a real difference between the folic acid group and the placebo group

But how big is the reduction ?

folic acid vs placebo for neural tube defects1
Folic Acid vs Placebo forNeural Tube Defects

RR = 0.29

P = 0.008

95% Confidence Interval : 0.10 to 0.76

95% confidence intervals means that 95% of the time this interval contains the true reduction

Therefore it gives an indication of the likely size of the reduction

interpretation
The same proportional increase in serum folate has the same proportional reduction in NTD

All women benefit from taking folic acid. There is not a threshold effect

Interpretation
so far
Collection

Nightingale

National statistics

Study design

Presentation

Estimates and confidence intervals

Analysis

Vital to interpretation

So far….
slide34

Use of Statistics in Screening

Screening is the identification, among apparently healthy individuals, of those who are sufficiently at risk from a specific disorder to benefit from a subsequent diagnostic test, procedure or direct preventive action.

Screening for Heart Disease

slide35

Relative odds of major IHD event by fifths of the distribution of haemostatic and lipid markers for all men (•——•) and for men free of IHD at baseline examination (∘–––∘).

Yarnell J et al. Eur Heart J 2004;25:1049-1056

The European Society of Cardiology

slide36

Unaffected

Affected

Biomarker : ZZ

slide37

Unaffected

Affected

Biomarker : ZZ

Screen positive

Screen negative

slide38

False negatives

False positives

Biomarker : ZZ

Screen positive

Screen negative

slide39

Screening for a medical disorder

Good test

Affected

Unaffected

Risk Factor

slide40

Screening for a medical disorder

Poor test

Affected

Unaffected

Risk Factor

is cholesterol any good for screening

Unaffected

Affected

Is Cholesterol any good for screening ?

Risk screen converter

www.wolfson.qmul.ac.uk/rsc/

slide42

Detection Rate

False Positive Rate

slide46
Are there any good screening tests ?

Antenatal screening for Down’s syndrome

slide47

Quadruple test markers

AFP

uE3

Unaffected

Down’s syndrome

Unaffected

Down’s syndrome

Total hCG

Inhibin-A

Unaffected

Unaffected

Down’s syndrome

Down’s syndrome

slide48

Distribution of risk in Down’s syndrome and unaffected pregnancies using AFP, uE3, total hCG and inhibin-A measured at 14-20 weeks (+ maternal age)

Unaffected

Down’s syndrome

1:108 1:106 1:104 1:102 1:1 102:1 104:1

Risk of a Down’s syndrome pregnancy at term

recent developments
Collection

Analysis

Interpretation or explanation

Presentation

Recent Developments
collection
Danish mother and child study

Recruiting people on the internet

Linking data sets

Probability linking eg

Date of mother’s birth fairly accurate

Gestational age of baby often wrong

Weight of baby –REALLY ACCURATE !!!

Collection
analysis1
Meta-analysis

Monte-carlo simulations

Bayesian analysis

Analysis of micro-arrays

Analysis
several studies looking at the same thing
Each study may be relatively inconclusive because of too much uncertainty (too small)

Statistical (mathematical) method of combining and presenting results from several studies

Can indicate more robust results

Several studies looking at the same thing
slide53

Prophylactic synthetic surfactant for preventing mortality in preterm infants

Study

Treat

Cont

RR (95% CI)

RR (95% CI)

Bose 1990

11/176

20/185

0.58 (0.29, 1.17)

0.58 (0.29, 1.17)

Corbet 1991

27/208

44/202

0.60 (0.38, 0.92)

0.60 (0.38, 0.92)

Halliday 1984

6/49

6/51

1.04 (0.36, 3.01)

1.04 (0.36, 3.01)

Phibbs 1991

3/36

7/38

0.45 (0.13, 1.62)

0.45 (0.13, 1.62)

Stevenson 1992

55/109

56/106

0.96 (0.74, 1.24)

0.96 (0.74, 1.24)

Ten Centre 1987

23/159

40/149

0.54 (0.34, 0.85)

0.54 (0.34, 0.85)

Wilkinson 1985

0/16

2/16

0.20 (0.01, 3.86)

0.20 (0.01, 3.86)

Overall (I-squared = 35.2%,

p = 0.159 for heterogeneity)

0.70 (0.58, 0.85)

0.70 (0.58, 0.85)

.1

.2

.5

1

1

2

5

10

Favours treatment

Favours control

comparing institutions individual doctors and identifying outliers
What’s the problem?

Lots of variables important

Random variation

Random variation greater for smaller units or institutions

Way of presenting the values for units so that this is taken into account

Comparing institutions, individual doctors and identifying outliers