lecture 1+2, introduction lesson (slide)

8/3/2019 Lecture 1+2, Introduction Lesson (Slide)

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BIOSTATISTICS

M391By

Dr. Atallah Z. Rabi


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Definitions

Statistics: is a field of study concerned

with:

The collection, organization, summarization,

and analysis of data and

The drawing inferences about a body of data

when a part of data is observed Biostatistics: Tools of statistics used in

biological sciences and medicine


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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 to biologicalproblems.

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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Sources of Data

Data is the raw material of statistics, Data is

used to answer a question, Sources of data

are: Routinely kept records (hospital medical records)

Surveys (information about mode of pt. transportation)

Experiments (best strategy for pt. compliance)

External sources (Published reports)


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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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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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Types of variables

Quantitative variables: (weight, height, age, they conveyinformation regarding amount)

Qualitative variables(Sick, diabetic, they convey informationregarding attribute)

Random variableDiscrete random variable(# of daily admissions,

represented by whole number)

Continuous random variable (Height, weight, skullcircumferences)


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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 is known)

Ratio scale (height, weight, & length, there is zero point.)


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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 ina sample that has been drawn from that

population.


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Simple random sample

If a sample of size n is drawn from a

population of size N in such a way that

every possible sample of size n has thesame chance of being selected, the sample

is called a simple random sample


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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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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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Probability and Statistics

Probability generalizes the concept of replicability.

Statistics are often used for decisions about specific

(non-replicable) situations. These decisions are often made in the context of

what is likely to happen in that specific situation.

Probability (likelihood) reflects our belief aboutreplicability


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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 asample 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 oldpeople!

You cant test everyone with the flu because the cases

havent even occurred yet!

So you study a sample, and you try to generalize to the

population. The sample 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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Looking at data: categorical or continuous?

Most data fall into two broad classes.

Continuous data are used to report a measurement of theindividual 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 theindividual 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 and summarize 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. A statistic 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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Measures of central tendencyMeasures of central tendency tell you in some sense where

you might expect a typical person to be, in the middleof the data.

Themeanis the arithmetic average. For example, if 3people were in hospital 8, 10 and 30 days respectively,the mean time is 48/3 = 16 days.!

Themedian is the value at which half the numbers are

higher and half are lower. If number of individuals isodd, it is the middle value (rank (n+1)/2) and if numberis even, it is average of two middle values.!.

Themodeis the most common value; rarely used


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Mean Calculation


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Measures of dispersion

Range

Variance

Standard


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Line histogram showing

distribution of HR in womenWomen's HR at 1 ltr O2/mi

02

4

6

8

10

12

14

105 110 115 120 125 130 135 140 145 150 155 160 165 170 175

heart rate

lecture 1+2, introduction lesson (slide)

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