basic concepts in biostatistics

7/29/2019 Basic Concepts in Biostatistics

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BASIC CONCEPTS IN

BIOSTATISTICS

Dr Peter Olutunde Onifade

FMCPsych


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Definition

Numerous definitions by numerousauthors

Example: it is a set of concepts, rules, andprocedures that help us to:

organize numerical information in the form of

tables, graphs, and charts; understand statistical techniques underlying

decisions that affect our lives and well-being;and

make informed decisions.

Dr P.O Onifade: Use of SPSS 2


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Our working definition

It is the scientific study of numerical databased on natural phenomena.

Scientific.. Dataquantities of information..groups of

individuals.

Numerical: quantified in one way or anothers Natural phenomena: but natural and

introduced



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statistics (singular) vs statistics

(plural) The word "statistics" is also used in

another, though related, way. It can be

the plural of the noun statistic, whichrefers to any one of many computed orestimated statistical quantities, such as

the mean, the standard deviation, or thecorrelation coefficient. Each one of theseis a statistic.



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Variable

Variable in general sense property of an object or event that can takeon different values.

More than one variable can be measured on

each smallest sampling unit.

Variable in strict sense It as a properly with respect to which

individuals in a sample differ in someascertainableA variable is measured throughcounting (eg. weight), sorting (eg. gender) orordering (severity of depression)



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Variate/datum

any particular measured instance of avariable spoken of as a variate: eg the

measured weight of the body weight ofthis, that, or the other person; male andfemale are variates/data of the variable

gender.



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Classification of variable

Classified based on Limit of possible values - Discrete vs

continuous Variable Cause-effect 1 - Independent vs dependent

Variable

Cause-effect 2 - Intervention vs outcomeVariable

Qualitative vs Quantitative Variable

Method of measuremenr - Measurement

scale: nominal, ordinal, interval, ratioDr P.O Onifade: Use of SPSS 7


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Discrete vs continuous Variable

Discrete Variable - a variable that canassume only whole number of values

(e.g., gender (male/female), college class(freshman/sophomore/junior/senior).

Continuous Variable - a variable that can

take on many different values, in theory,any value between the lowest and highestpoints on the measurement scale.



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Independent vs dependent Variable

Independent Variable - a variable that ismanipulated, measured, or selected by the

researcher as an antecedent condition toan observed behavior.

Dependent Variable - a variable that is not

under the experimenter's control It is thevariable that is observed and measured inresponse to the independent variable.



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Qualitative vs Quantitative Variable

Qualitative Variable - a variable based oncategorical data. Nominal:

Ordinal:

Quantitative Variable - a variable based onquantitative data.

Interval: meaningful distance, but no absolute

zero Discrete no decimal continous

Ratio e.g weightDr P.O Onifade: Use of SPSS 10


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The Population in biostatistics

The biological definition refers to all theindividuals of a given species (perhaps ofa given life-history stage or sex) found ina circumscribed area at a given time.

In statistics, population always means thetotality of individual observations (and attimes totality of individuals) about which

inferences are to be made, existinganywhere in the world or at least within adefinitely specified sampling area limited

in space and time.Dr P.O Onifade: Use of SPSS 11


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Finite and infinite population

Finite: a concrete collection of objects orcreatures, such as the tail lengths of all

the white mice in the world, the leucocytecounts of all the Chinese men in the world

Infinite: outcomes of experiments, such as

all the heartbeat frequencies produced inguinea pigs by injections of adrenalin; theexperiment can be repeated an infinitenumber of times (at least in theory).



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Census, Parameter, Statistic Census: enumeration or count of every

member of the population. (mental healthsurvey of all Nigerians

Parameter: summary measure of theindividual observations made in census ofan entire population. E.g., average GHQscore

Statistic: summary measure obtained froma sample. E.g., average GHQ score of5,000 Nigerians selected for the 6

Geopolital zonesDr P.O Onifade: Use of SPSS 13


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Samples in biostatics

A sample is a subset of a population. Sampling unit is a single instance of the

sample about which observations ormeasurements are taken. The samplingunits frequently, but not necessarily, are

also individuals in the ordinary biologicalsense. Discuss



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Sampling frame

A complete list of all the eligible samplingunits, from which sample is drawn for the

study.A requirement for simple random sampling



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TESTS OF SIGNIFICANCE 1:Conditions for parametric tests

The data must have normal distribution Homogeneity of variance: 1. equality of variance

of different groups; 2. the variance of onevariable should be stable at all levels of theother variable.

Data must be Interval or ratio , not categorical

Independence of different set of data, that issets of data are not from same sample, excepton case of repeated sample T-test

TESTS OF SIGNIFICANCE 2: Difference in means


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Dr P.O Onifade: 17


Analysis

Parametric

test

Non-parametric

test

Difference between a mean value from a

population and that of a specified value

One sample

T-Test

One sample

??kolmogorov_Smirnov (K-S) test

Difference between two means from

(two) independent samples/populations:

parametric; non-parametric

Two-

Independent-

Sample T-

Test

Mann_Whitney U,

??Kolmogorov

Smirnov Z, Moses

Extreme

Reactions,a nd

Wald-Wolfowitz

runs

Difference between two means from

repeated measures of same

sample/population

Paired

sample T-test

Wilcoxon Signed

Ranks Test, Sign,

and McNemar Two-

Related Sample

Tests



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Dr P.O Onifade: 18


Difference between more than

two means from independent

samples:

One-Way

ANOVA

Kruskal-Wallis

Test


two means from repeated

measures: independent variable

is categorical; dependent is

quantitatve

RepeatedmeasureANOVA

Friedman,

Kendalls,

Cochrans



samples with two or more

independent variables:

ANCOVA



samples with two or more

dependent variables:

MANOVA


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Dr P.O Onifade: 19

Analysis

Parametri

c test

Non-parametric

test

Correlation between two sets of

quantitative variables (assuming

NO cause-effect relationship: both

variables quantitative)

Pearson

Correlation

Kendall's tau_b;

Spearman's rho



NO cause-effect relationship: both

variables quantitative) plus

controlling for confoundingvariables

Partial

correlation

coefficient

Partial

correlation

coefficient

TESTS OF SIGNIFICANCE 3: relationships


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Dr P.O Onifade: 20

Analysis

Parametri

c test

Non-parametric

test



there is cause-effect relationship:

both variables quantitative)

Here, we talk ofdependent and

independent variables

Linear

Regression

Logistic

Regression



there is cause-effect relationship:both variables quantitative)

Here, we talk of one dependent

and two or more independent

variables

Multiple

Linear

Regression

TESTS OF SIGNIFICANCE 3: relationships


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Dr P.O Onifade: 21

TESTS OF SIGNIFICANCE 4: Reconciliation

Analysis

Parametr

ic test

Non-

parametric

test


quantitative variables(assuming NO cause-effect

relationship: both variables

quantitative)

MANOV

A

Several variables, need toreduce to dimensions/

factors eg cattel 16 factors

PartialExplorator

y factor

analysis


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Dr P.O Onifade: 22

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