Lecture 3- Basic Principles of Research- part three :

8/3/2019 Lecture 3- Basic Principles of Research- part three :

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Research 3

rdlecture

Sun. 9.10.2011

Dr. Ashraf Shaweesh

The doctor started this lecture by notifying us that this is the 4th

week and we are still on the first concept, as we didnt know

that before!!!!

This lecture is a continuation of the previous two lectures, and

we are going to complete talking about Basic Principles of

research design and this lecture will cover the slides from 20 tothe last slide which is 36.

Now here is a little note for our colleagues who dont

understand Arabic very well, dont worry as every single word

in Arabic was translated to English, at least in this part of the

lecture, Im sure most of you didnt understand this lecture

because most of the time the doctor was explaining things in

Arabic, so dont panic, it is a very nice and easy lecture.......

So lets start,

Slide no.20

Last time we talked about Bias, we identified bias and we said

that we have three types of bias; the selection bias which is

usually committed during the selection of the sample, and we

gave an example that if you want to examine dental cariesamong people of different age groups, if you want to do a

research on caries the sample should be consistent to the same

age group, so you have to examine people belonging to the same

age group like for example school children, you cant

incorporate people who are older like young adults or adult

people or old people, in this case you will have selection bias,

because each age category has its own mares, for example when


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we grow older the trans-occurrence of caries decreases in that

type if you dont pay attention to the selection of your samples

this may lead to a selection bias.

Slide no.21

Measurement bias, if you want to examine dental caries both

visually and radio graphically, the identification of caries

clinically is different from the identification of caries on a radio

graph, thats why if you want to do a research either you

do all the research on clinical examination or on radio graphical

examination, but you cant mix both of them otherwise you will

have what we call measurement bias.

Slide no.22

And Finally the confounding bias, we will give some examples

for example people who take folic acid where found to have

lower rate of colon cancer, but at the same time people who take

folic acid usually they take multi vitamins, thats why they are

careful about their health, so they are health conscious aboutdiet and exercise, thats why may be they did not have colon

cancer or they have decreased rate of colon cancer because they

are aware about their health and not because they take folic acid

alone, so thats why here is what we call the confounding bias,

when we have a relationship between two factors and a third

factor interferes with this relationship, for example you want to

examine the effect of people who work in the mine the effect ofcoal on lung cancer but you didnt control for smoking, because

it is possible that in the sample that you selected people who had

lung cancer where heavy smokers and not because they work at

the mine, so that is why it is very important that you identify the

confounding factor.

Slide no.23

A variable is not confounded if it is directly along the path from


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the cause to effect; if you identify a factor and another factor

and if you identify the ratios between these two factors when the

relationship is the same from the beginning to the end and we

dont have any other factor that affect this relationship then wesay that the variable is not confounded, again another example;

you want to measure the effect of smoking on lung cancer, in

this case what you did in your research is that you collected a

sample of smokers and you studied the existing of lung cancer

in these people, in this way you control all other factors, so we

dont have any confounding factor, the only possible cause of

lung cancer in this group that you selected is smoking only, soin this case we say that the variables are not confounded.

A confounding variable is not necessarily a cause itself, may be

related to the suspected cause and the effect in an instance but

not related in nature; a confounding variable is not all the time

a cause of the problem itself, it can be associated with the

problem, for example education and/or an income with good

health, it sounds that educated people have good health, but atthe same time people with high income also have a good health,

so if you want to do a research it is very important to control for

the effect of education and for the effect of income, we cant so

do them together, we said usually education is associated with

good health, but education is not the cause of good health,

because we have other factors affecting the good health.

Slide no.24

Selection bias is an issue in patients selection for observation,

and so it is important in the design of the study; so selection bias

usually occurs in the selection of the sample, thats why it is

very important when you design your study to identify any

possible selection bias and try to avoid it; the selection bias is

very important in the phase of selection of the sample, thats

why we can identify the bias that is related to the selection


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selection biasat the beginning or at the design of the study,

and you can avoid it.

Slide no.25

The confounding bias is an issue in the analysis of the data, the

confounding bias appears with you after you did the experiment,

when you are doing the analysis of the results, you find that

there is an effect of a factor, that you didnt notice or think about

it before, thats why you need now to justify, now once the

observation have been made; we did the observation and we did

the measurements and everything, and when we about to write

our research we find that there is a confounding factor, thats

why in such a case you have to identify the confounding

factor, and now you already did your research, that means it is

difficult now to avoid it, so now what you can do is to try to

explain that the confounding bias didnt give us a very bad

results, may be your sample was very huge or very large, so that

the effect of the confounding bias wasnt that much and didnt

have that impact, and here is an example of a research I did

myself, and I think may be all of you know it, which is timing of

tooth eruption among the Jordanian population, because it was

cross sectional study we didnt see the effect of the premature

loss of deciduous teeth, we know that the premature loss of the

deciduous tooth - if it falls before the normal age for it to fall -

may accelerate the eruption of the permanent tooth, and because

my study was cross sectional; so it was difficult to know the

children who have their deciduous teeth lost before their normal

time or the children who have their deciduous teeth lost in the

normal time, that happens because it was cross sectional and not

longitudinal study, now that is considered as a

confounding bias, but in the end when I published the research, I

used a big part of the explanation in the discussion of the

research to explain that those children percentage was very


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small, and the research was on 3000 child, so the effect of the

confounding factor if it exist will be very small, and not that big

to think about it, thats why again if the confounding bias is not

controlled, but when you write the research or when you analyzethe research you have to explain to the reader that the effect of

this confounding factor was not that big.

Slide no.26

Often in the same study more than one bias operates, it is not all

the time that we have only one bias, sometimes we have more

than one bias at the same time.

A distinction must be made between the potential for bias and

the actual presence of bias in a particular study, you have to

have the ability to identify the bias or to feel like there is an

effect of that bias, the bias does not always appears strongly,

sometimes the bias is potential, thats why you have to

distinguished between the potential for the bias and the actual

presence of the bias in a specific study.

Slide no.27

Dealing with bias, if you want to deal with bias you have to

identify the bias, and you have to measure the potential effect of

bias, thats why you have to know its effect as well, to know

for example if the effect of the bias is very big, because that may

ruin the whole study, so that the whole study that you did is

wrong, but sometimes if your sample is very big, and the effectof bias is small you can proceed in your research, but you have

to explain in the discussion in your writing that I could control

the bias or the effect of bias was not that big.

Sometimes we tend to modify the research design when the

potential effect on the result is big; sometimes we may change

the design of the whole research when we feel like there is a

potential for bias, for example I was going to do a research onthe dental caries measuring it clinically and radio graphically,


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but when I see that there is a measurement bias I change the

whole research and make it just a clinical examination and not a

radio graphical examination.

Changing the conclusions in a clinically meaningful way whenthe effect is not big enough; when the effect of the bias is small,

for example like that in my study about timing of tooth eruption

and the effect of the premature lose of the predecessor teeth,

because I constricted or I found that the effect of the premature

lose in our community was not that significant or was not that

big and the sample was very big, so that I justified and

explained very well, but I didnt change the design of the study,that means when the effect is not that big you can change the

conclusions or you can modify or you can explain the results in

a meaningful way especially when the effect is not big.

Slide no.28

Now lets talk about chance, which is another enemy - the

researcher has two enemies; bias and chance -, we have already

talked about bias, now lets talk about chance, Unbiased

samples may misrepresent the population because of chance, as

the chance effect increased as the sample is not representative of

the population that you are doing your research on.

Chance is the divergence of an observation on a sample from the

true population value, as the measurement goes away from the

true value that you want to measure. It is also called random

variation, an example of that is tossing a coin 100 times; you

have a coin and you want to toss it a 100 times, now the only

possibility for it is to be either head or tail, so if I toss the coin

100 times, do I guarantee that the result will be 50 heads and 50

tails?, that is not necessarily; it may be 55 heads and 45 tails,

now if I toss it 10 times only, is it necessarily for the result to be

5 heads and 5 tails? No, it may give for example 7 heads and 3

tails or 2 heads and 8 tails, so that as the number of tries


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increases as the results be more accurate and more close to the

correct value, thats why if I toss it a 1000 times, the results will

be close to 500 heads and 500 tails, and if I toss it 10000 times,

may be it will give around 5000 heads (a little greater forexample) and around 5000 tails (a little lesser for example), so

that the larger the sample size the lesser the chance, now what

are the effects of chance?, it will affect in a way that I will not

get the true value; now for this coin what is the percentage to

have head or tail if I toss it only one time? It is 50%; it will be

either head or tail, so that this is the true value or the actual

observation, now when you do the research you want yoursample to be representative of the true value, thats why you

dont want the effect of chance, now what is the effect of

chance?, it is that effect that will not give you the true value and

that will give you another value, lets take another example; you

want to do a research who is taller males or females?, now this

is your research which is very simple, but I always give it as it is

a simple example, now all of us know that males are taller thanfemales, but now I can take 5 groups randomly from you and

find that in 2 cases the females are taller than males, can I

generalize this? No, it is not a rule. Now if I take just 2 groups

(2 females and 2 males) and by chance the 2 females were taller

than the 2 males, now can I say that females are taller than

males just because of that? No, but if I take a sample of 100

person, the females who are taller than males in this sample maybe 5 or 3, and if I take a sample of 1000 person, the number of

the females who are taller than males will be lesser, so that the

larger the sample size the lesser the chance, the chance is when I

build my result on the chance, it is just a chance to find a female

taller than a male, but it is not the true, the true is that

males are taller than females and this is known, but by chance I

may find in my study a female who is taller than a male.


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*A student asked a question that it seems like how could I

know the number of subjects in my study to control the effect of

chance? and the doctor said that it is a very important question

and that we will deal with that later on, but usually we have arule that any sample should be representative at least if the

subjects are more than 30, as the sample is more than 30 we

consider the probability of the effect of chance to be minimized,

so that I will never do a study on a sample less than 30, and as

the number of subjects increases as it will be better, so the effect

of chance is important to identify it from bias.

Slide no.29

Chance verses bias, how to differentiate between the chance and

the bias, bias distorts the situation in one direction or another,

now in bias if the true value of some thing is for example 80, the

bias may make it 70 or it may make it 90, but it is always in one

direction either 70 or 90, but chance or random variation results

in an observation above the true value as likely as one below it;

the chance may make the value that you measure below or

above the true value in the same percentage, that means if you

have 50% of the probabilities above the true value you will also

have 50% of the probabilities below the true value. Now I want

to show you this figure - this figure is on Slide no.31 - :

Now this is an example, if we want to

measure the blood pressure by twoways; one way is by the means of

Sphygmomanometer which is the

device used to measure the blood

pressure and we all know it, in fact

this device does not give the true

value of the blood pressure accurately,

and the more accurate method of measuring blood pressure is byusing intraarterial canula by inserting the canula in the artery,


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and this give us the blood pressure correctly 100%, so that it is

more accurate to use the intraarterial canula than to use the

Sphygmomanometer, now lets assume that you want to

measure the blood pressure in a patient and the truemeasurement is 80, but when you use the Sphygmomanometer it

is 90, and because you did a distortion of the true value in one

direction we say that this is bias, we say that this is a

measurement bias particularly, now here what does the chance

do? the chance make the probability of taking the value to the

right equals to the probability of taking it to the left, but it does

not make a distortion in one direction only, while the bias makesa navigation of the sample to one side, but in chance we have

the chance of having a reading above the true reading or below

it, and the chance of having readings above and the chance of

having observations below should be the same, that means that

the chance always have something we call the random variation.

Now this is the end of the first part.

Done by: Raja Amin El-Haddad.

I dedicate this work to all my sweet friends, and of course I will

not forget to thank you my lovely sister >

Allah ye5aleli eyaki ya rab, o yeslamo kteer b3ref ene dayman

m3albetic ma3e.

Nadoooosh enti ya 3asal >>> thank you very much >>> you

have a great impact on others >>> Allah yes3edek sho ennek

btefhame !!!!


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Chance VS. Bias

& relationship between chance and bias

Slide #29+31If you want to measure BP in a patient and the true

measurement is 80 "the real value", and when use the

sphygmomanometer it is 90

Because you made distortion of the real value in one direction;

we said it is Bias "measurement bias"

Now chance makes the value at the right equal to the value on

left; but it doesn't make distortion in one direction.

So bias makes distortion of the sample in one side, but chance

may have readings above true reading or below the reading

and chance of having readings above and the chance of having

observation below should be the same!

Bias distorts the situation in one direction or another

chance always have the random variation, which results in an

observation above the true value as likely as one below it.

? Q: not heard

A:

" So the read of measurement from real value" 80" to the reading

of sphygmomanometer 90 this drift is calledBias"

"And the possibility of having below and above the 90; this is

calledchance"

You should know how to distinguish between bias and chance

~The mean of many unbiased observation of a sample approximates the true

observation of the population ~


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When you take these different readings, there are many

readings above the actual reading, or below the actual reading,

but when you take the average of all of these readings youshould have the actual reading,

That means in chance when you take the average of all

because you have some readings in the right side equal to other

readings in the left side, finally average should be close to actual

reading.

~ In small samples this may not be close to the true observation of thepopulation ~

When the sample is larger, the probability of chance above and

below the true observation would be equal

For example, when throwing a coin a thousand time it is

suppose to be 500 times head and 500 tail, (lama faradan nerme

el 3omleh 1000 marrah lazm faradan etkoon 500 rasmeh 500ketabeh) but it is possible to have an error of five times increase

or decrease (zeyadeh aw noqsaan) in a 1000 times it is okay to

have an error of 5 to 5 But when we shoot two or three times, it may be a possibility of

head 3 times and tail 3 times, meaning that the effect of chance

is big

This means that the impact of opportunity here will be large,that is why the distribution of measurement to the right side

and to the left side in the two values is the same; because you

have a large sample, But when you have a small sample let's

suppose that measures only twice, so the two values might be

on the right side.

But when you measure big samples, we have the probability of


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having the value in the right side equal to the value in the left

side.

Bias VS. Chance

Slide #30~ Bias can be prevented by proper conduction of clinical investigations ~

The bias can be prevented, but the chance cannot be prevented

- Chance cant be eliminated, but can be reduced

where it is possible to recognize the potential bias and therefore

can be totally excluded

But you cant cancel what you actually have (chance), forexample some females are taller than males, "This is indeed" so

the chance cant be eliminated but bias can be eliminated.

~ Bias can be corrected through proper data analysis ~

Sometimes especially confounding bias can be corrected

when I make analysis of the data I can make corrections of bias

"specially the confounding bias".~ Its influence (the effect of chance) can be reduced by proper research

"study" design ~, when you enlarge your sample for example the

effect of chance becomes very reduced

"y3ny 3nd e5teyar a huge sample the effect of chance will be

decreased"

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

variation ~,as we said before "statistics can calculate the probability of

chance " In any research always the probability of chance

must not be more than 5% , If it is more than 5%, it becomes the

reality.

So if you measure who is taller males or females, females who

are taller than males by chance shouldn't be more than 5%

"Doctor said eda ho 3ml ehsa2yeh 3la eltolab bel hall ; l2nh


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3ddna 250 taleb , lazem ykoon fqt aql mn 25 talebeh atwal mn el

male , otherwise he can't say females are taller than males"

Systematic error VS. Random error

Slide # 32in any search you read you may find the words systematic error

or random error

*Systematic error is the error that results because of bias

*Random error is the error that results because of chance.

Always pay attention Sys error >> bias Random error >>

chance.For example you did not control chance or did not reduce

effects of chance, because of that you did not take enough

number of subjects, for example when you study who is taller

females or males you didn't take enough (proper) sample y3ni

mathal bdl ma too5d 100 person a5dt fqt 5 and by chance kan 3

of females taller than males , so sample wasn't enough soooooo

we have random error"

"random error " the more the random error the less the validity of your research

But the systematic error is because of bias, for example if you

want to measure caries clinically by clinical examination of

patients, you also included the radiographic examination, so

you have an error; because in clinical examination of caries thereis cases we don't see very well, but in radiographic examination

caries would be clear, (as we are studying in cons) for example

in class 2 caries can be seen in radiographs but in clinic it is not

necessary to show.

So when you use two different methods of measurement

many different results comes out; this is called bias systematic

error


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~ Systematic error: Biases pushing the values of separate measurements

away from the true value ~

~ Random error: Even distribution about the true value ~

Always in random error the possibility of having higher or lower

values are equal

~ various bias tend to balance each other out ~, when you increase the

sample you will have increased probability of above or below

becomes more the same more equal, so when we take the

average to be very close to the true value (almost 100%)~ Systematic error: Remains systematically different no matter how many

times the measurement is repeated~

I mean even if we increase the sample the probability of bias

remains found versa is possible to increase

ValiditySlide # 33Validity is called truth

any research should be valid, the validity of the research is thetruth of research

~ Validity is correspondence to the true value measured or searched for ~;

Whenever the research gives results close to reality, we say it is

more valid.

~ For an observation to be valid, it must be neither biased nor incorrect due

to chance ~; the less of effect of bias and less the effect of chance >>>

greater the validity.


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So the enemies of any researcher are:1)Chance2)Bias

If you could reduce the effect of chance and eliminate the effect

of bias, then the values of your research are very valid and your

research has high validity.

We have two types of validity:

1- Internal validity

2- External validity

Internal validitySlide # 34

The internal validity ~ is the degree to which the result of a

study are correct for the patients being studied ~

The internal validity: the results of the research of a

sample must be true for the sample itself

~ Internal: means it Applies to the conditions of the particular

group of patients being observed and not to others ~

~ Is determined by how well the design, data collection andanalyses are conducted and threatened by bias and random

variation ~

always which determines the internal validity is the

biases and random variation or effect of chance .

~ Necessary but not sufficient by itself ~, that means we need

to have the external validity


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,

External validity"

For example: Dr. Ashraf did a study on the timing of

tooth eruption among Jordanian population, the data

that he share should be apply on the sample that hecollected this called internal validity, at the same time if

he study timing of tooth eruption on another sample,

then the result should be same, otherwise there is no

external validity

External validityGeneralizability

Slide # 35the external validity which is also called

"Generalizability"

Y3ni the results of your research should be valid on

other samples of the population and the whole

population.

~ Is the degree to which the result of an observation hold true in

other setting~

And the external validity means real results for a

sample of each other or all population

bm3na 25r: (in other words) if you take another samplefrom the same population, the values should be the

same, and if you take the population also the values

should be same, so this is called external validity

"

"

~The answer of :Assuming that the results are true in other

settings, do they apply to my patients as well?~)


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generalizability(

~ Generalizability assumes that patients in a study are similar to

other patients ~, that means if the Dr. does the study again,he will assume that the patients in his new research

should be the same as the patients in his previous

research.

~A study with high internal validity may be misleading if its

results are generalized to the wrong patients~

The internal validity not enough by itself, there should

be external validity as well.

- But always the starting point should be internal

validity.

We cannot have external validity if we did not have

internal validity; we cannot say that the results of the

research real if they were not true on the sample itself

A B

Conclusion

sampling

Selection

bias

samplesample

population

patients

chance

External validity

Generalizability

Internal validity

Measurement & confoundingbias

?

?

All patients with condition of interest


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This picture is a representation or a summary of what weve

been talking about for the validity

We have sample which is divided into A and Bif we have any bias at this stage of selection of sample we call it

selection bias

Then we want to measure the sample, so if we have any bias

during the measurement or during the analysis we call it

measurement or confounding bias

The chance plays the role after you measure your sample, it is

possible that we have the effect of chance and then we havethe conclusion.

If the sample you studied is representative to another sample

we call it is external validity.

That means if the conclusion that results from your sample can

be applied to another sample, or can be applied to another

group of patients, or can be applied to all the population; then

we say the research is of high validity externally or high

generalizability.

And thats it forgive me for any mistake Good luck to all ^_^

Lecture 3- Basic Principles of Research- part three :

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