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Research Methods

Dent 313

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Research fields in medicine

Biological sciences Biology of disease

Clinical sciences

Information to care forindividual patients Clinical Epidemiology Population sciences

Epidemiology Study of disease occurring in human population

Health services Study of how non-biological factorsaffect the patients

health

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- - Biology of the disease : how the disease works, the etiology of the disease,

the pathology of the disease (how the disease progresses, stops).

- - Clinical sciences : collect some information from patients.

- Population sciences : in certain population (e.g. schoolchildren), not always

studying diseases but also phenomena .

- Health services : e.g. study the effect of the distance between patients and

hospitals on the life of those people.

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Clinical epidemiology

The science of making predictions about

individual patients

By counting clinical events of similarpatients

And using strong scientific methods

To ensure that the predictions areaccurate

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Clinical epidemiology- - For example : the significance of studying the possible effect of smoking on

lung cancer

Predict whats going to happen to those patients and apply the results to the

non-patients because we dont those people to become patients.

- - Using strong scientific methods : in order for me to say smoking causes lung

cancer I have to rely on a very strong evidence

- Ensure that the predictions are accurate : to come up with recommendations,

the significance or the relevance of the research what will this research provide

to the health sector

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Purpose of clinical epidemiology

Develop and apply methods of clinical

observation that will lead to valid

conclusions by avoiding being misleadby systematic error and the play of

chance

Obtaining the kind of information

clinicians need to make good decision in

the care of patients

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The enemies of anyresearch

The systematic error

The play of chance

- Systematic error can be avoided.- The play of chance can be minimized (by increasing the sample).- The larger the sample, the more accurate the conclusion will be.

- The chance should not exceed 5%.

(if it exceeded the 5%, this means that the conclusion cannot be applied)

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Clinical epidemiology

It is clinical

it answers clinical questions(Questions related to the care of patients, people having an abnormality,

people receiving tretment)

It guides clinical decision making

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Evidence-based medicine

Application of clinical epidemiology to

the care of the patient

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Basic principles

Clinical question

Variables

Health outcomes Numbers and probability

Quantitative vs. qualitative

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Clinical question

Is the patient sick or well (abnormality)

How accurate are tests used to diagnose disease (diagnosis)

How often does a disease occur (frequency)

What factors are associated with an increased risk of disease

(risk)

What are the consequences of having a disease (prognosis)

How does treatment change the course of disease (treatment)

Does an intervention on well people keep disease from arising(prevention)

What lead to disease (cause)

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Variables

Things that vary and can be measured

Independent vs. dependent variables

Other synonyms (depending on the context)

Independent

Explanatory, controlled, manipulated, predictor, exposure,input Dependent

Response, measured, observed, explained, outcome, output Independent variables

Can be entirely manipulated (dose of a pesticide) Can be taken in different values (age) Can be unethical to modify (smoking)

Dependent variables cannot be modified but observed

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Variables

- Consider smoking is a factor that causes lung cancer, here there are two

variables, smoking & the incidence of lung cancer

Independent variables : smoking (causes lung cancer), explanatory- explains

the occurrence of lung cancer, controlled- smoking can be controlled (we can

advertise or put a propaganda about the bad effects of smoking) but lung

cancer cannot be, predictor- it predicts the occurrence of lung cancer,exposure- people exposed to smoking may develop lung cancer, input- the first

variable that causes the second variable

dependent variables : occurrence of lung cancer (depends on smoking),

observed- smoking cannot be observed but the occurrence lung cancer can be,

explained- the occurrence of lung cancer is explained because of smoking,

measured- the occurrence of lung cancer is measured not smoking, smoking is

studied to see the effect which is lung cancer

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Variables

Example : the age of children affects the timing of tooth eruption (or emergence)

- the independent variable : the age.

- the dependent variable : eruption.

Example 2 : sugar causes caries

- the independent variable : sugar.

- the dependent variable : caries.

Example 3 : studying the effect of smoking on lung cancer

- independent variables can be unethical to modify : its unethical to ask a group

of people to start smoking and to see the effect of smoking.

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Health outcomes

Events that can be studied directly in intact

humans only

Include the five Ds Disease Discomfort Dissatisfaction

Disability Death

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Numbers and probability

Clinical science depends on quantitative measures

Impressions, instincts and beliefs are only importantwhen added to a solid grounds of numericalinformation

This allows for better confirmation And estimation of error Prediction of treatment outcomes or disease

sequence Better be expressed as a percentage

(Probability) needs to be expressedquantitatively Estimated by referring to past experience with groups of

similar patients

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Numbers and probability

- Impressions :

whats the impression of old people who wore dentures.

Whats the impression of student about the way of teaching (of the teacher)-

good, average, poor or adding numbers ( the teacher was as good as 90%)

Prediction of treatment outcomes or disease sequence, better be expressed asa percentage:example : whats the prognosis of people recently have breast cancer- good,average, poor (this is not enough)example 2 : people who have lung cancer have a mortality rate of about 90%after 5 years (adding numbers provides a solid ground)example 3 : the effect of an antibiotic- strong, average, weak- its better to say90% of those who have taken that antibiotic healed from the disease.

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Numbers and probability

(Probability) needs to be expressed quantitatively, estimated by referring to past

experience with groups of similar patients : e.g. a physician have a patient who

had breast cancer wants to provide that lady with some information because

she asks him, she will ask for example what about my future? How long will I

live ?, if this physician wants to answer that lady, he has to refer to a previous

research thats been done, that research for example involved 100 ladies and itstudied those ladies for about 5 years and the outcome was 80% of those with

breast cancer cured from the disease, so according to this information that the

physician has, he can provide his patient with information

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Populations

All people in a defined setting with

certain defined characteristics Examples:

The general populationA hospitalized populationA population of patients with a specific

disease

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Population: a group of people but they have to be defined according to the

research thats going to be done, the term population is not the same as used

in demographic data (the population of Jordan, the population of arabic

contries) so you have to think about the target group that youre going to study

and then you have to define it.

for example : the population of Jordan (those people have a common thingwhich is : theyre Jordanian citizens), the males in Jordan, third year dental

students in JUST.

Example : a research about the timing of tooth eruption (by dr.Ashraf), the

population was the Jordanian children, so he didnt take a person who is above

15 years old (that was outside his population).

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Examples:

The general population (a research about whos taller males or females, people

who are growing shouldnt be taken, the population will be all people who are

above 18 years old)

A hospitalized population (people in hospital having a certain disease, e.g.

periodotitis, the poplulation will be people with periodontitis, periodontitis affects

adult people)

A population of patients with a specific disease

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A sample

Is a subset of people in the defined population

Selected from that population It is not practical to test all the population

Clinical research is carried out on samples

A sample makes inference about the

population

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Sample :

for example : the population of Jordanian children that I want to study, I cannot

study the whole population (maybe they are more than 2 millions), so its very

impossible to study 2 million people thats why I take a sample.

Example 2 : people who registered in elections (the whole population is taken

because its computerized)Example 3 : the possibility of win of a candidate (take a sample)

The best is to take the whole population but when its impossible we have to take a

sample, but this sample should be representative of the population.

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Representative sample :

Example : in the research of the timing of tooth eruption the sample were 3000

children, we cannot take only the children in Irbid and say they are

representative of the whole population of the Jordanian children, in order for the

sample to be representative there should be certain characteristics in that

sample, we take a certain percentage of people living in Irbid, anotherpercentage for people living in Amman, another percentage for people living in

the south, and we have take into consideration that some children are urban,

other children are rural, these are factors that may affect the timing of tooth

eruption, people living in poor life standards like people living in Jordan valley or

in the desert may have different timing of tooth eruption thats why part of the

sample should include people living in remote areas or people have poor

standards of living, and so on.

The sample should be structured, its not totally random, if we take a random

sample and we want to study Jordanian children, we may have an Iraqi or

Egyptian child for example, because we have Iraqis or Egyptians living in

Jordan, we dont want to include the non-Jordanian in the study thats why the

sample should be representative.

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Two important points in sampling

Are the conclusions of the research correctfor the people in the sample?

If so, are the conclusion correct for adifferent sample of people belonging to thesame population ( a 100% representative sample is similar toany other sample within the population )

does the sample represent fairly thepopulation of interest?

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A sample is representative

Depends on how a sample was selected

Purely random sample

Clustered sample Stratified sample

Equal chance for all members vs.

misrepresentation

Computerized programs for selection ofsamples

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A sample is representative

Depends on how a sample was selected- for a sample to be representative, it

should be structured, you have to think about the factors or about the different

categories in your sample.

Purely random sample (depending on the research and the population that should

be defined)- studying the prevalence of caries among the students in JUST,

any student in JUST can be taken BUT if I want to study the prevalence ofcaries among dental students, I shouldnt go and select randomly from the

university, I have to select from the science hall 2 for example (during research

methods lecture)- another example of purely random sample if I want to take

the opinion about the government but if I want to see the opinion about the

government among educated people I have to go to a university or a school for

example.

Clustered sample- categorized, for example you have to define the population of

Jordan, we have children belonging to (Irbid, Amman, Aqaba), we have people

who are rural, urban, children who belong to private schools or governmental

schools, all these are important factors so thats why it should be categorized

and clustered.

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A sample is representative

Stratified sample- the big sample, then under this big sample

Jordanian Children

Irbid

UrbanPrivate Schools

Governmental SchoolsRural

Amman

Aqaba

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A sample is representative

Equal chance for all members is important in random sampling- if I want to do a

sample, there should be an equal chance for any potential member in the

population to be selected- for example : Dr.Ashraf wants to study the people in

the scince hall 2 (at the time of the lecture), everyone should have an equal

chance to be selected, otherwise its misrepresentative.

If you want to randomize, the best to do is to use computer programs

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Sampling

Random sample Each member in the population has an equal probability of

being selected

Sample is only different from population because ofchance (difference should be small

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Sample is only different from population because of chance (difference should be

small

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Probability sample :

In the study of timing of tooth eruption, not a purely random sample was taken, it

was a probability sample, for example (Irbid represents 20% of the Jordan

population, so the sample should contain 20% from Irbid, so any child from Irbid

now has a known probability of being selected within the 20%).

oversampling low frequency groups- assume that 2% of the Jordanian population

are Bedwens, but in the sample I make the percentage of those people slightly

more that 2%, to guarantee that there is enough subjects from this minority.

Example : we want to study periodontitis, then I advertise for that, because it may

be difficult for old people to travel, they may not participate, so I do

oversampling and go to them and chose them.

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Non-random sampling

Present generalizability problems and bias

Very common in the literature

Examples

Convenience samples Chosen because they are more convenient E.g., patient visiting DTC in Irbid Dental students

Grab samples Subjects are grabbed wherever they could be found

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In non-random sample we cannot generalize the outcome, because the sample

was not representative.

Convenience samples- if I want to study who is taller males or females but its

difficult for me to go and select my sample randomly, I have a group of people

who are just next to me.

Example : the prevalence of caries among university students, its difficult for me to

go to all the faculties and take random samples from those faculties, but I have

my students who I am teaching now, I take this sample, because its mor

convenient for me, but the question is : are the dental students representing the

whole students in the university ? May be no, because dental students may

brush their teeth more than others.

Grab samples : a research on people who have torus palatinus, everytime I saw

someone with torus palatinus, I grab that patient and include this patient in my

sample, its a grab sample, its not a representative sample

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Definition:

A process at any stage of inference tendingto produce results that depart systematically

from the true values

Any trend in the collection, analysis,interpretation, publication, or review of the

data that can lead to conclusions that aresystematically different from the truth

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Bias:

Example : the average height of Jordanian males is 170 cm, and I made a study, I

went to basketball players, then I said the average of Jordanian males height is

190 cm, this is bias.

Its a systematic error because the results depart systematically from the true value

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Categories of bias

Selection bias

Measurement bias

Confounding bias

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Occurs when comparisons are made

between groups of patients that differ in

ways other than the main factors understudy

Example:

Examine dental caries among different agegroups

Examine perio condition without adjustmentfor smoking

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Selection bias- bias resulting because I didnt select my sample properly and as a

result I have a systematic error.

Example:

Examine dental caries among different age groups : if I want to study dental

caries, I have to define the group of people that I am studying, because

the prevalence of caries among children is different from adults oramong all people, as we go in age the prevalence of caries decreases

Examine perio condition without adjustment for smoking : I wanted to study

periodontitis and I didnt consider the factor of smoking

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Occurs when the methods ofmeasurement are not similar amongdifferent groups of patients

Examples Examine dental caries visually vs.

radiographically

Examine the WL (working legth) of Rootsusing different techniques- radiography, apex locator.

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Confounding bias

Occurs when two factors or processes are

associated or "travel together " and the effect

of one is confused with or distorted by the

effect of the other

Example:

TG and cholesterol levels are associated with riskfor coronary heart disease

Education and/or income with good health Folic acid vs. lower rates of colon cancer

People taking multivitamins are health consciousabout diet and exercise

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Confounding

Studying the incidence of lung cancer among people who work in mines, you didnt

control for another important factor which is smoking.

The confounder (smoking) affects the relationship between the independent

(working at a mine) & the dependent variables (occurrence of lung cancer).

The confounder can be controlled, in this example by doing the study on mine

workers who are non-smokers.

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Confounding bias

A variable is not confounded if it is

directly along the path from cause to

effectA confounding variable is not necessarily

a cause itself

May be related to the suspected cause andthe effect in an instance but not related in

nature

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Confounding bias

Cause Effect

Confounder

A confounding variable is not necessarily a cause itself :

Example : people taking folic acid have a lower rate of colon cancer

the confounder : healthy people may have a lower rate of developing colon

cancer.

In this case the confounder is not cause by itsel because health is not related to

lowering the rate of colon cancer.

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Selection bias is an issue in patients

selection for observation, and so it is

important in the design of a study

Selection bias all the time comes when you dont pay attention to selecting of

defining your sample, so you can control or oppose that before doing the study.

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Confounding bias is an issue in

analysis of the data, once the

observations have been made

Confounding bias can be avoided while you are analyzing your data.

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Often in the same study more than one

bias operates

A distinction must be made betweenthe potential for bias and the actual

presence of bias in a particular study

Sometimes, there is a possibility for bias to operate. Sometimes, its actually

present.

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Dealing with bias

Identification of bias

Measuring the potential effect of bias

Modifying the research design when thepotential effect on the result is big Changing the conclusions in a clinically

meaningful way when the effect is not big

enough

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Identification of bias, if you dont identify it, you may miss it and it will appear in

your study and you dont pay attention.

Modifying the research design, in the example of mine workers and developing

lung cancer, the study is modified by studying only those who are non-smokers

to avoid bias.

Changing the conclusions in a clinically meaningful way when the effect is notbig enough, for example, among those who work at mines, only 2% are

smokers (which is usually not true), because the effect is very small, I continue

in my study but I have to provide a good explanation while I am writing the

report, so in the conclusion I say : where we have to consider that 2% of those

are smokers, so the effect could not be 100% true but its very much true.

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Bias in general leads to a systematic error

The truth should be here It will become here

Because of bias

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Example : I want to have an idea about the average blood pressure in the

Jordanian population (we know the average is 120/80 mmHg), if I do a study

and the average was 105/75 mmHg, this is a systematic error, there must be

some bias because the truth isnt in its actual position, the truth has departed to

one way (either lower or higher)

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Unbiased samples may misrepresent the

population because of chance

Chance is the divergence of anobservation on a sample from the true

population value

is called also random variation

Example: Tossing a coin 100 times The larger the sample size the less the

chance

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For the chance to happen, the sample should be unbiased, there shouldnt be

bias

Chance is the divergence, so the true diverges to one way or another, its very

important to remember that the effect of chance can happen on both directions

around the truth.

Example : if I did a study on the average blood pressure among Jordanians

(the average should be 120), and I have something like 115 or 125, so in the

chance its possible for the result to be higher or lower, and the percentage of

those higher should be equal to the percentage of those lower than the truth,

but in bias the observation will be really away from the actuality of the truth, itcan be away to that side or the other side, but in chance its possible for the

data to be higher or lower and the percentage or the distribution is equal

around the truth

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Chance Truth Chance

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Example: Tossing a coin (Head:Tail)

You want to toss a coin 10 times (Head or Tail will appear), you cannot

guarantee that head will appear 5 times and tail will appear 5 times, its possible

to have 6:4 or 7:3 or sometimes 8:2 (but not really very possible).

If you do it 100 times, its not a very big possibility to have 70:30, you may have

55:45 If you do it 1000 times, its not very big possibility to have 700:300, you may get

something like 520:480

This means that as you increase your sample, the effect of chance decreases.

The effect of chance makes the times for the coin to appear Tail not equal to

the times where the coin is Head, (all the time it should be 50%), but its not

really possible when the sample is very small. So, this means that chance can be controlled by increasing the sample, but

bias cannot be avoided by increasing the sample, bias has nothing to do with

sample.

The larger the sample size, the less the chance.

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Example: I want to do a study, who is taller males or females (in reality in

Jordan for examples ,ales are taller than females), but what if I only took 20

people (a sample of twenty), and all the time the effect of chance shouldnt

exceed 5%, I should allow one female to be taller than male because this is the

5% (1:20), but if I took 20, its possible that in this sample I have 2 or 3 or 4

females taller than males, the effect of chance now has exceeded 5%, but whythis happened ?, because the sample is very small, what happens if I take the

whole student here in this theatre (science hall 2), maybe 300 people, in this

300, those females who are taller than males should be lower than 5%, what

happens if I took the whole Jordanian population ?, of course I will get

something very true.

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So thats why all the time think about increasing the sample, and all the time

they say the minimum for any sample is 30, usually this number is very critical,

30 people or 30 something, this is the minimum for any sample to have a good

result, and this depends on the type of research that you want to do, maybe if

you want to do a research on something common or on something not related

to disease, you have to take a very big sample, but if you want to make aresearch on a disease, sometimes its really difficult to collect more than 30

people with disease, so depending on the condition or the design of your study.

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Chance vs. bias

Bias distorts the situation in one directionor another

Chance / random variation results in anobservation above the true value aslikely as one below it. The mean of many unbiased observations of

a sample approximates the true observationof the population

In small samples this may not be close to thetrue observation of the population

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Chance vs. bias

Bias distorts the situation in one direction or another but it cannot go in both

directions, either bias takes the result to the right or to the left but doesn't take it

to the right and to the left in the same percentage.

Chance / random variation results in an observation above the true value as

likely as one below it, so the percentage of observation above the true value

should all the time be equal to the percentage of those below the value, but inbias the true departs to one side or another but cannot go to two sides or

cannot be in the same percentage to the right and to the left

The mean of many unbiased observations of a sample approximates the true

observation of the population, because all the time in chance theres a

possibility for some people to have higher data as likely as the possibility of

some people having lower data, so because of that they cancel each other out,

but in bias the results depart systematically from the true value.

When the sample is very small you cannot guarantee that those above the true

value should be equal to those below the true value, but as you enlarge the

sample, of course we call this the normal variation of people.

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Its the same example as tossing a coin 10000 times, you may have occasions

where the Heads are more than the Tails and you have occasions where the

Tails are more than the Heads, so they cancel each other out, but when you

toss it only 10 times, you cannot guarantee that.

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Bias can be prevented by proper conduction of

clinical investigations (selection & measurement bias)

Bias can be corrected through proper data analysis

(confounding bias)

Chance cannot be eliminated

Its influence can be reduced by proper research

design (for example, taking a big sample)

Statistics can be used to estimate the probability ofchance or random variation

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You can eliminate or avoid bias but you cannot eliminate chance.

Chance cannot be eliminated, is it possible kill all females who are taller than

males ?!, No, of course, we cannot do that, we cannot eliminate that, its

present in our observation, but we can reduce the effect of chance by taking a

big sample.

Statistics can be used to estimate the probability of chance or random variation,

because chance can be measured thats why we have statistical tests that can

be employed to estimate the probability of chance, and this is called the P value

P value is the probability value, this is the estimation of the effect of chance, allthe time it shouldnt exceed 5%, sometimes according to the research the P

value can be defined to be 1% (for example, a study on a drug causing reallybad side effects), so we dont want the 5%, we only allow 1%.

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In order for me to say males are taller than females, the percentage of females

who are taller than males should not exceed 5%, if they exceeded 5%, I cannot

say that males are taller than females, I am not allowed to say that.

If I did a study and I discovered that females who are taller than males make up

10%, in this case I say: there was no statistically significant differencein heightbetween males and females, but if I found that females who are taller than

males were only less that 5%, I can really very confidently say: there was

statistically significant differencein height between males and females,

because of that, males are taller than females.

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9080

bias

chance

SphygmomanometerIntra-arterial canula

True BP BP measurement

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The true BP is measured by inserting an intra-arterial canula, but this is not

really possible all the time.

The more common way of measuring BP is via the sphygmomanometer.

Because we did not select the proper way of measuring BP, we ended up with

what we call measurement bias, the true value should be 80 but because of

measurement bias, it departed systematically and became 90 (it goes to oneside).

The effect of chance is really different, its now 90, the effect of chance, we

have data above 90 and data below 90, and the percentage of those data to

those data should be all the time equal when the sample is very big, but if the

sample is small, we cannot guarantee that.

So the effect of chance is on either side of the value, but bias departs thereadings to one side.

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Systematic error vs. random error

Systematic error(results because of bias) Biases pushing the values of separate

measurements away from the true value

Remains systematically different no matter howmany times the measurement is repeated

Random error(results because of chance)

Even distribution about the true value

Various biases (or readings) tend to balance eachother out

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All the time, we want random error, but we dont want systematic error.

If you have a big systematic error in your data, your research is really rubbish,

you need to redo it, but if you have a random error, thats fine, we accept

random error in research, because its a result of chance, we cannot avoid

chance but we can reduce it.

Remains systematically different no matter how many times the measurement

is repeated, if you did a study, for example, you want to measure caries but you

didnt define age groups, this is a systematic error (selection bias), if you repeat

the study again without modifying the design of your research, you will have the

same error again,

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In random error if you repeat the study again, if you had a high random error ,

you can do the study again and you may have something different.

Example: you took 20 students to measure who is taller males or females, and

the percentage of females who are taller that males was 10%, its not really

accepted, if I do the study again, taking another 20 students, I may have lessthan 5% females taller than males, so if you repeat the study, you can actually

have different random errors, but of course, when you enlarge your sample, all

the time the effect of those above the reading and those below the reading

should be the same, they cancel each other out, and then you get the actual

reading.

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Truth

Validity is correspondence to the true value

measured or searched for

For an observation to be valid, it must be

neither biased nor incorrect due to chance

Types

Internal validity External validity

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Is the degree to which the results of a study

are correct for the patients being studied

Internal

Applies to the conditions of the particular group ofpatients being observed and not to others

Is determined by how well the design, data

collection and analyses are conducted and

threatened by biases and random variation

Necessary but not sufficient by itself

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Example: if you take a sample and you want to measure the height of people in

that sample (suppose that the actual height is 165 cm) but you get something

like 175 cm, this is a problem in internal validity because your reading is not

similar to the actual reading of that sample.

So you want your results to be true for the sample that you studied, this is

internal validity. We dont want the threatening of bias and random variation to be very big.

Necessary but not sufficient by itself, its not enough, I am not interested in

having the results true in the group that I studied, I also want these results to be

true if I studied another group as well.

For example, I studied the height of males and I was very accurate and the

average of height of males in this group was 170 cm (the internal validity wasperfect), but if I did the study on another group and the result was 180 cm, this

means it was true for that sample (internal validity), but its not enough, I want it

if I did the study again to be the same result (external validity or

generalizability).

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(Generalizability)

Is the degree to which the results of anobservation hold true in other settings

The answer of:

Assuming that the results are true in other settings,do they apply to my patients as well? Generalizability assumes that patients in a

study are similar to other patients

A study with high internal validity may bemisleading if its results are generalized to thewrong patients

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Generalizability

I want my results to be generalized, if I take another sample the results should

be the same.

The answer of:

Assuming that the results are true in other settings, do they apply to mypatients as well?

In research, we have a sample that were studied and gave results, I wantthose result not to be true only for those people who were studied, I wantthose results to be true on my patients, I want them to be applied onanother sample, so its the most important thing.

Internal validityAll patients with condition of interest

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A B

Conclusion

sampling

Selectionbias

sample

sample

population

patients

chance

External validityGeneralizability

Measurement & confounding

bias?

?

p

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Modified By :Muad Salahuddin Al-Zoubi