slide 1+2 - introduction_lecture

7/29/2019 Slide 1+2 - Introduction_Lecture

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BIOSTATISTICS

M391By

Dr. Atallah Z. Rabi


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Biostatistics

(a word made from biology and statistics)

The application ofstatistics to a wide range of

topics in biology.
http://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Biologyhttp://en.wikipedia.org/wiki/Biologyhttp://en.wikipedia.org/wiki/Statistics


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Biostatistics

It is the science which deals with developmentand application of the most appropriate

methods for the:

Collection of data.

Presentation of the collected data.

Analysis and interpretation of the results.

Making decisions on the basis of such

analysis


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Why Statistics?

You should not ignore it. It is too useful.

You cannot fight it. Everyone else uses it.

It gives the right answers (95%) of the time.

But you do not know which 95%.

It is great fun. Trust me.


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Why Statistics? Examples

Which drugs should be allowed on the market?

What Public Health programs should be pursued?

What programs would reduce infant mortality?

Are cell phones a good idea for drivers?

Is it a good idea for post-menopausal women to

take estrogen?


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Role of statisticians

To guide the design of an experiment or surveyprior to data collection

To analyze data using proper statistical

procedures and techniques

To present and interpret the results to researchers

and other decision makers


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Definitions (cont.)

Statistics is the science and art of collecting,summarizing, and analyzing 'data that are subject torandom variation (Last, 1995).

Biostatistics is the application of statistics tobiological problems.

Data refers to a collection of items of information,

A variable is any quantity that varies. It is any

attribute, phenomenon, or event that can havedifferent values.


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What is data?

Data are numbers, numbers result from:

Measurement (body Temp., Body weight)

Counting (Number of patients admitted)


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Most data fall into two broad classes.

Continuous data are used to report a measurement of the

individual that can take on any value within an acceptable range.For example, age, systolic BP, [K+], change in weight over 6

months.

Categorical data are used to report a characteristic of the

individual that has a finite, usually small number of possibilities.The categories should be clear cut, not overlapping, and cover all

the possibilities. For example, sex (male or female), vital status

(alive or dead), disease stage (depends on disease), ever smoked

(yes or no).Make sure you are very clear about the definitions. Does one

cigarette and I didnt inhale count as smoking?

When designing a study, allow for missing values and refusals.


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All biostatistics begins with description. Before you do anything

else, you lookat the data andsummarize the data. Our goal in this

hour is to show you how to get a first look at the data and get readyto do more elaborate procedures. Astatistic is just a numerical

summary of the data, like the largest number in the data set.

Descriptive statistics should be clear and easily interpreted. They

should not mislead you about the data they are summarizing.


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Common terms used in statistics

Population

Sample

Variables

Measurements

Statistical Inference Simple random sample


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Population

Population is the largest collection of entities for

which we have an interest at a particular time.

(Weights of all new born babies in a hospital) Population of values is the largest collection of

values of a random variable for which we have an

interest at a particular time.

Finite population (values consist from fixed numbers)

Infinite population (values consist of endless succession of values


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Sample

Sample is a part of population. (weights of some

selected new born babies)

There are different types of samples

There are different types of sampling

techniques


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Populations, Samples, and Individuals

Aristotle speculated about thepopulation of all women (compared

to the population of men). He had immediately available to him a

sample of two women, and he could have counted the number of

teeth for two individuals.

The population is the collection of all people about whom you

would like to ask a research question. This might be a fairly clear-cut easily defined set of people:

What proportion of people 65 or older in the US today

have Alzheimers disease?

Or it might be a more hypothetical group:

How much of a reduction in symptomatic days could a

person expect if treated with a new antiviral for flu?


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Typically, you cant study everyone in the population.

You cant afford to have everyone 65 or older in the US

seen by a neurologist, even if you could find all the old people!You cant test everyone with the flu because the cases

havent even occurred yet!

So you study asample, and you try to generalize to the

population. Thesample size is the number ofindividuals in the

sample (not the number of measurements you make on each

person!)

A good study design will help make your sample

representative of the population you are concerned about.

Good statistical analysis will help tell you the best answer to

your question about the population, and also how far off you

might be.


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Types of data


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Variables

A variable is a characteristic that takes

different values in different persons, places,

or things.Examples of variables :

diastolic blood pressure, heart rate, height of

adult males, weight of new borne babies, agesof patients.


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Numerical presentation

Graphical presentation

Mathematical presentation


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1- Numerical presentation

Tabular presentation (simplecomplex)

Name of variable

(Units of variable) Frequency %

-

- Categories

-

Total


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Age

(years)

Frequency %

20-


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Table (III): Distribution of 20 lung cancer patients at the chest

department of KAUH and 40 controls in May 2011 according to

smoking

Smoking

Lung cancerTotal

Cases ControlNo. % No. % No. %

Smoker 15 75% 8 20% 23 38.33

Nonsmoker 5 25% 32 80% 37 61.67

Total 20 100 40 100 60 100

Complex frequency distribution Table


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Complex frequency distribution Table

Table (IV): Distribution of 60 patients at the chest

department of KAUH in May 2011 according to smoking

& lung cancer

Smoking

Lung cancerTotal

positive negativeNo. % No. % No. %

Smoker 15 65.2 8 34.8 23 100

Nonsmoker 5 13.5 32 86.5 37 100

Total 20 33.3 40 66.7 60 100


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Line Graph

0

10

20

30

40

50

60

1960 1970 1980 1990 2000Year

MMR/1000

Year MMR

1960 50

1970 45

1980 261990 15

2000 12


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Frequency polygon

Age

(years)

Sex Mid-point of

intervalMales Females

20 - 3 (12%) 2 (10%) (20+30) / 2 = 25

30 - 9 (36%) 6 (30%) (30+40) / 2 = 35

40- 7 (8%) 5 (25%) (40+50) / 2 = 45

50 - 4 (16%) 3 (15%) (50+60) / 2 = 55

60 - 70 2 (8%) 4 (20%) (60+70) / 2 = 65

Total 25(100%) 20(100%)


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Frequency polygon

AgeSex M

-

PM F

20- (12%) (10%) 25

30- (36%) (30%) 35

40- (8%) (25%) 45

50- (16%) (15%) 55

60-

70(8%) (20%) 65

0

5

10

15

20

25

30

35

40

25 35 45 55 65Age

%Males Females


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Histogram

0

5

10

15

20

25

30

35

0 25 30 40 45 60 65

Age (years)

%


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Bar chart

0

5

10

15

20

25

30

35

40

45%

Single Married Divorced Widowed

Marital status


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1- Measures of central tendency(averages)


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Measures of central tendency

Midrange

Smallest observation + Largest observation

2

Mode

the value which occurs with the greatestfrequency i.e. the most common value


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Measures of central tendency

(cont.)Median

the observation which lies in the middle of

the ordered observation.

Arithmetic mean (mean)

Sum of all observations

Number of observations


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Measures of dispersion

Range

Variance

Standard dviation

Semi-interquartile range

Coefficient of variation

Standard error


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Standard deviation SD


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Standard error of mean SE

A measure of variability among means of

samples selected from certain population

SE(Mean) =

Sd

n


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Statistical Inference

Statistical Inference is the procedure by which

we reach a conclusion about a population on

the basis of the information contained in asample that has been drawn from that

population.


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Measurement

Measurement is defined as the assignment of numbers to

objects or events according to a set of rules.

Measurement has different scales: Nominal scale (male - female; well-sickmutually and collectively exclusive)

Ordinal scale (observations can be ranked, low, medium , & high economic status)

Interval scale ( distance between 2 measurements) Ratio scale (height, weight, & length, there is zero point.)

slide 1+2 - introduction_lecture

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