MedI7 Intro 2011 Mortalitate Alcool

7/23/2019 MedI7 Intro 2011 Mortalitate Alcool

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Research design and methodology,statistics and data handling (MedIs7)

Biostatistics II (KVT3)

Lecturers:

Alina Zalounina (AZ)Carsten Dahl (CD)Dan Karbing (DK)

Department of Health Science and TechnologyAalborg University


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Aim of the course

Learn to use and understand normally appliedstatistics in medical research (advanced level)- Software tool: SPSS

Introduce important aspects of research work(only for MedIs7)

- Funding and intellectual property rightsMedical/research writingPlanning studies


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ProgrammeDate Topic Lecturer

Sept 2 Introductory statistics AZ

Sept 5 Funding academic research DKSept 9 Developing and testing a hypothesis AZ

Sept 12 Medical writing DK

Sept 16 Contingency tables CD

Sept 21 Parametric analysis DK

Sept 23 Non-parametric analysis CD

Sept 26 Intellectual Property Right (IPR) and patents DK

Sept 30 Regression analysis CD

Okt 5 Study design AZ

Okt 7 Survival analysis CD

Okt 19 Meta-analysis. Evidence-based medicine AZ

= only for MedIs7


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Learning material


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Software: SPSS

http://spss.software.aau.dk/

The students are expected to bring a laptop.

SPSS should be installed before the coursestarts using guidelines at:


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Examination

Written exam - 4 hours, in January

Pensum: slides + learning material specifiedfor each lecture

Exam questions will reflect lectures and course

assignments

Some questions will require use of a software (SPSS)

Hjlpemidler: everything, but not Internetand communication with others

More detailed Exam info can be found at

http://person.hst.aau.dk/az/MedIs7


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Today:Introductory statistics

Alina Zalounina

Center for Model-basedMedical Decision Support


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Learning material:

Chapter 1: DataChapters 2-5: Descriptive StatisticsChapters 7-8: Statistical Inference


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Learning Objectives

Identify the type of data

Define and understand the main termsof Descriptive Statistics

Understand the purpose of InferentialStatistics

Outline the major measures of risk

Outline the basic operations in SPSS


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Type of data

Categoricaldata

Metricdata

Nominal

etnicitygendermarital status

type of operationsmoking status

Ordinal

score

Discrete

number ofchildren

Continuous

weightheighttemp.

ageblood pressuretimecholesterolbody mass index


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Samples and populations

Sample = collected dataPopulation = all possible data


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Type of Statistics

Descriptive used to organize and

describe a sample

Inferential used to extrapolate from asample to a larger population


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Learning Objectives


Define and understand themain terms of Descriptive Statistics





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Descriptive Statistics.Issues for today

Frequency

Measures of Central Tendency- Mean

- Median

Measures of Variability- Variance

- Standard deviation- Standard error

Descriptive Plots- Boxplot- Histogram

- Q-Q plot

Data distibutions- Normal- Binomial


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Frequency table

Relative frequency


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Measures of Central TendencyMean(average)

Sample Mean

PopulationMean

1

n

i

ix

n

x

1

N

i

i

N

x


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Median(middle)


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Measures of VariabilityVariance

Sample Variance Population Variance

2

2 1

-1

( )n

i i

n

xxs

2

2 1( )

N

ii

N

x


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Standard deviation, Standard error

Sample SD Population SD

2

1

( )N

i

i

N

x

2

1

-1

( )n

i

i

sn

xx

sse=

nStandardError

D i i Pl


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Descriptive PlotsBoxplot


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Histogram

The histogram shows the frequency distribution across aset of measurements as a set of physical bars.

Overall shape curve shows distribution


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Bell-shaped

In a perfect normal frequency distribution, the mean and

median are equal. The data is continuous andsymmetrically distributed around the central point.

Variability is represented by the width of the distribution.

Normal distribution


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Normal distribution: formulae

X~N(, 2) =>

b

a

f(x)dxb)XP(a

2

2

1

x-

2

1f(x) e

Note: about 95% of observations liewithin 1.96*standard deviation ofthe mean

+1.96*-1.96*

95%

X = a continuous variablef(x) = probabilitydistribution function of X= mean= standard deviation


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Without inspecting the data it is risky to assume a normaldistribution.There are a number of graphs that can be used to check thedeviations of the data from the normal distribution:

A histogramshould reveal a bell shaped curve.

QQ plot: Curvature of the points indicates departures of

normality

Check normality


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Skew distribution

This population is skewed to the right(i.e. it has a long right hand tail)


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Binomial distribution

o There are nidentical independenttrialso Each trial can have only 2 outcomes: success or failureo Probability pof success in each trial is constanto Variable of interest is X=the number ofsuccesses in ntrials

binomial variable


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Binomial distribution: formula

x n-xn!P(X=x)= (1 p)px!(n-x)!

Note: n!=n(n-1)(n-2)1

X~Bin(n,p) =>


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Binomial distribution: Example

The probability that a student is accepted tothe Department of Medicine is 0.3.

If 5 students from the same school apply,what is the probability that 2 are accepted?

P(X=2)=?

p=0.3

n=5

5-225!=> P(X=2)= (1 0.3) 0.310.32!(5-2)!

probabilitydistribution


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Learning Objectives



Understand the purpose ofInferential Statistics




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Inferential Statistics

Can your experiment make a statement about

the general population?

Two types of tests:

1. Parametricassume that the variable in question has a knownunderlying mathematical distribution that can bedescribed (normal, binomial, etc.)

2. Non-Parametricare considered distribution-free methods becausethey do not rely on any underlying mathematicaldistribution.


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Learning Objectives







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Risks and Odds.Issues for today

Risk(probability) = a measure of the chance ofgetting some outcome of interest (e.g., disease) fromsome event (e.g., exposure to a risk factor)

Absolute risk Relative risk Odds Odds ratio


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Mother smoked duringpregnancy

Yes No Totals

Apgarscore


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Relative risk(Risk Ratio, RR)= the risk for theexposed group compared to the risk for the non-exposedgroup.

The risk of low score among those having smoked

compared to those who did not smoke is

RR= p1/p2 = 80%/15% = 5.3

Interpretation of RR:

Mothers who smoked during pregnancy had more than 5 timesthe risk of getting low Apgar score as those who did not smoke.

Risk (low score | smoking)= p1= 80 %Risk (low score | no smoking)= p2= 15 %


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Apgar score


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Odds ratio (OR)

The ratio between the odds is the odds ratio for smokingamong mothers with low score compared to mothers withhigh score:

OR= odds1/odds2 = 22.67

Interpretation of OR:Mothers with low Apgar score were more than 22 times aslikely to have smoked during pregnancy as those with highApgar score.

Odds (mothers with low score smoked) = odds1= 2.7Odds (mothers with high score smoked) = odds2= 0.12


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RR versus OR

A B

C D

Outcome No Outcome

Exposed

Non-Exposed BC

ADOR

A B

C D

Exposed Non-Exposed

Outcome

No Outcome C)B(AD)A(BRR

RR=1 or OR=1 => there is no association between the outcomeand exposure to risk factor


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Learning Objectives







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Example

Introduction to SPSS

D t i


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Data view

Variable view


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Variable view

Smoking

LowApgarScore

Frequences


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Frequences

Cross - Tabulations


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Risk estimate

Odds ratio: 22.667=(17/2)/(3/8)

Relative risks:4.25=(17/20)/(2/10)0.188=(3/20)/(8/10)

odds ratio for non-smoking among mothers withhigh score compared to mothers with low score:

risk of low score among those who did notsmoke compared to those having smoked

risk of high score among those who did notsmoke compared to those having smoked


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Box-plot

Descriptives


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Histogram

Q Q Plot


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Q-Q Plot


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Learning Objectives


Define and understand the main termsof Descriptive Statistics




E i


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Exercises:http://person.hst.aau.dk/az/MedIs7

Documents

MedI7 Intro 2011 Mortalitate Alcool