(4) designing a study ii

7/30/2019 (4) Designing a Study II

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Now we continuo the topic of designing a study, and this is part 2, last timewe covered two different categories related to the research design, The

first category is the observational study and the second is randomized

study, and we gave many example of how they differ from each other.

Today we will cover the different types of observational studies and the

different type of randomized study. soooooo:

Slide (2): Types of randomized control trails

1. Simple randomized design

2. Cross over design

3. Factorial studies

Slide (3): Simple randomized design

Randomized to two or more groups, we want to make a study and we just

divide the group or sample to two or three or four or more groups.

This is very simple and powerful

We do it when enough subjects are available and can be recruited, to

recruit means to invite somebody to participate in your research.

So when we have enough subjects we can easily divide these subjects to groups, the

groups will be different; for example one group is called the treatment group and the

other is called the placebo group, the first one is given the medication, and the other

one is given a placebo, and then we study the difference between these two groups,

this is simple randomized design.

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*Wikipedia: A placebo is a sham or simulated medical intervention that can produce a (perceivedor actual) improvement, called a placebo effect. /

Slide (4, 5): cross over design

Cross over design; when the subjects are randomized to one study groupand after a specific period of time the same subjects are switched to the

other group.

We want study the effect of a medication on the liver, you bring one group and give

them a medication, and The other group you dont give them the medication or you give

them the placebo, and after you finish the first stage you switch the groups or cross

over the groups, The first group that was given the medication is now given the

placebo, and The second group that was given the placebo is now given the actual

medication.

Advantages of this design:

Two subjects for the price of one

It seems that we have four groups but actually we have only two

groups, because we crossed over the groups. Or It seems that we

examined two subjects, But actually we have examined one subject

twice, zay ka2anno darabet mareeden b 7ajar(WTH O_o!)

Less variability and more powerful

In general, when the variability is less you will not have results far

from the average, but when you add more subjects you will increase

the variability.

Each subject serves as his/her own control

The subject will know that at a specific stage he will be given the

actual medication, not just the placebo, so thats why he is his own

control, this assures best results.

Increase subject motivation

All subjects will be in treatment group at some stage;

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http://en.wiktionary.org/wiki/shamhttp://en.wiktionary.org/wiki/sham


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if we are doing a research for creating a medication to treat AIDS,

all subjects are suffering from ending diseases, they eventually will

die, so these people will accept any research that may benefit them,

thats why they prefer to be in the treatment group rather than the

placebo group, so in such cases in this type of research all subjectswill be in the treatment group, which is motivating for the patient.

Good when you cannot recruit enough subjects

We only use the cross over when we have a very small sample

Disadvantages :

Bias due to carryover effects leading to failure to ascribe successes

or failures to correct group; this is the main disadvantage of cross

over design:

Carryover effects are due to the 1st treatment but occur during

the 2nd treatment

E.g. subject receives antibiotic A for 3 month then Antibiotic B

for the next 3 months. One of the subjects developed an

Infection in month 4. Is it because antibiotic B failed? Or

infection appeared in the period of antibiotic A but did not

manifest itself until the period of antibiotic B??

To overcome carryover effect:

o Washout period: a time with NO treatment, we give

antibiotic A for three month, then we wait for threemonth, then we give antibiotic B.

Slide (6): Factorial studies

Factorial study: Designed to answer more than one question by

randomizing each subject to more than one condition.

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Its not like the cross over where we just switch groups, here we have two

researches, each subject will be in the first research, and in the second

research.

We can examine the role of the lack of oral hygiene on caries development, and

lack of oral hygiene in periodontal diseases, so we can answer two research

questions by examining one subject.

Advantages :

Get two studies in the price of one

In factorial studies we are answering more than one research

question.

Cost effective

Its less expensive, because we are using one patient in two studies.

Disadvantages :

Different conditions may affect one another (interact)

When we examine the lack of oral hygiene on both caries and periodontal disease, we

have to take in mind that they may affect each other, because the Lack of oral

hygiene causes caries, but caries itself makes a cavity, and this cavity is filled with

bacteria and food debris, which will increase the amount of periodontal disease, so

thats why different condition may affect one another, so we have to make sure

that we dont have interaction between the two outcomes that we will examine.

Slide (7): Methods of allocating subjects within a randomized design

How to categorize the patients in different groups in a randomized

study?

1. Randomization with equal allocation

2. Blocked randomization

3. Randomization with unequal allocation

4. Stratified randomization

Slide (8): Randomization with equal allocation

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Equal number of person to each treatment group .

If I want to examine a drug, I put thirty people in the treatment group and also

thirty people in the placebo group, so the two groups have the same number of

participants.

This type is the Standard method with highest power, high power always

means less bias.

But it Needs enough number of subjects in each group (>20) to minimize

the effect of chance in having 2 equal groups in the sample when they are

not equal in the population. The ideal size of a group should be something

like 30 subjects.

If you have two groups, and in each group there are five subjects, you will not be sure

that these five will represent all the population, because its very small.

When you toss a coin six times can you make sure that the head will appear three times!,

but if you toss it hundred times you will make sure that the head or tail will be close to

fifty.

Conditions of subjects have to be similar as well. Thus equal allocation in

number is not by itself sufficient .

If I select a forty years old man in the treatment group, I have to select a forty year

old man in the placebo group, but when the age is not an important factor it doesnt

matter.

Slide (9, 10): Blocked randomization

Only used :

When exact number in each group is needed but the study is too

small (4 or 6 subjects per block), we dont have an enough number

of participants, we categorize the subjects in blocks.

In larger studies when temporal changes affecting study enrollment

are expected, if you have temporal changes, any change with time

may affect the participation or registration of your subjects in the

study.

Enrollment at different times of people with changing

conditions.

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Temporal changes: I want to examine the effect of lack of oral hygiene on caries,

and I dont have a big sample at the beginning of the research, or I am not sure that I

will have a big sample at the beginning of the research so I start inviting people, and

start my research on a small sample and then include more patients in the study, this

is not similar to the case when you have all the participants from the beginning.

In this case when we have temporal changes affecting the study enrollment, we do

blocked randomization.

In large multicenter studies;

I want to do a study in the university of JUST and the same study in Jordan

University and the same study in Mu2ta University, the subject that areenrolled in my study are different from the subject there, thats why the best

way here is to do a blocked randomization.

Assignment of subjects is randomized

Disadvantages :

Staff may figure out the assignment of a subject prior to enrollment

(in unblinded studies), speciallywhen all but the last subject of a

block have been enrolled.

Four subject block in two groups A, B. three have been randomized as ABB, thus the last

one is to be assigned (not randomized) to A for the two groups to be equal. So the investigator

will know to which the last subject belongs to, and this is not good in the research design, I

have to be blind (I shouldnt know to which group the subjects belong to).

This can be overcome by randomly choosing among different size

blocks so that staff does not know the size of the block within which

the subjects are being randomized.

The Dr said that its very important to read the BO_oK !, because there are many good

examples, which is important to understand research methodology. (Ma 3aleek Dr. Of

course we will )

Slide (11): Randomization with unequal allocation

In this case you have two groups, but these groups are unequal in size

for example; two-to-one randomization.

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Advantages :

Subjects of serious diseases may benefit from unequal allocation;

because we can include more people in the treatment group, and

less people in the control group.

>50% chance of receiving the new treatment when allocated to the

larger group

If we are examining a drug to treat AIDS we can put twenty people in the treatment

group and only ten people in the placebo group, and in this will increase the motivation

of the participants because they will know that the chance of being in the treatment

group is not 50/50, its more than fifty.

More knowledge about side effect when allocating >50% of subjects

to the treatment group; we increased the size of the treatment

group, and this means that we can study the side effect more

properly.

Disadvantages :

Losing power; because we dont have two equal sizes.

Harder to reject the false Null hypothesis; because there is no equal

number of subjects in each group, I will explain the null hypothesis

later.

Inconsistency with equipoise principles;

Investigators beliefs that the two groups are equal, we cannot

confirm the belief of the investigator that the two groups are

equal.

Investigator may believe that one group is superior to the

other, because we have two groups of unequal size, the

investigator may think that one group is more important that

the other, which is not good in research design.

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Slide (12): Stratified randomization

Preferred when an equal distribution of baseline prognostic factors is

needed.

Example of Baseline prognostic factors are Sex and age.

I want to examine the lack of oral hygiene on incidence of periodontitis, but I cannot

put someone who is twenty years old and someone who is sixty years old in the same

group, in this situation I divide the same group into subcategories, so I divide the main

group into many other subgroups according to their ages, so this is called stratified

randomization, when we have important factors like sex and age.

Unequal distribution of baseline factors may lead to confounding .

Avoided by randomizing persons within groups of important baseline

factors .

Advantage: variability is decreased, thus power increased.

When I include many people with different ages in one group, the

variability will be high. Therefore the Variability decreases because we are

decreasing the size of the group into subgroups.

Disadvantage: only possible with one or two associated baseline factors.

If I have more than two factors and I want to divide all the subjects in the

group according to these factors, there will be a lot of subgroups, with very

small sizes in each group.

Slide (13): Types of observational studies

Now we are done with the types of randomized study and shift to the

observational study:

1. Cross-sectional studies

2. Prospective cohort studies

3. Case-control studies

4. Nested case-control studies

5. Ecologic studies

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Slides (14, 15): Cross-sectional studies

Easy and fast

Information is collected from subject at a single point of time

In my research timing of tooth eruption I went to the school and by only one visit for

each school I examined the sequence of eruption for the students, so I did the

research on a single point of time.

It is different from the longitudinal (prospective cohort) study when I bring

the subject and observe it over a long period of time; actually longitudinalstudy is more effective.

Used to answer descriptive questions

What is the prevalence of a disease?

Prevalence: is the proportion of individuals in a population

who have a specific disease or condition at a particular

moment of time.

Used to determine frequency of risk behavior .

Useful in estimating sample size; the cross sectional study is the main

study that is used to study the sample size and risk factors.

The last two will be explained later on.

Not good in answering analytic questions ;

An association found may go in either direction

Risk factor may cause the outcome or vice versa

Effect-cause or reverse causality

Two directions: When I study alcohol and depression, does the alcohol consumption

cause depression, or does the depressed people drink more alcohol? In cross sectional

study, analytic question cant be answered.

One direction: smoking can cause facial wrinkles, but not all people who have facial

wrinkles smoke! So this is a one direction study.

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Cross sectional study is not good when we have two directional pathways.

Slide (16, 17, and 18): Prospective cohort studies

Its also called the longitudinal study, its the opposite of the

cross sectional study.

Sample is assembledpriorto development of the outcome and followed

over time.

Subjects are evaluated to make sure that they do not already have theoutcome being studied .

Provide much stronger evidence in support of a causal relationship .

Reduce the possibility of reverse causality.

In the longitudinal study, we can distinguish between the risk factor

and the outcome, but in cross sectional study its hard to distinguish

between them.

Minimizing recall bias:

Information about risk factor is collected ahead of

disease development.

Recall bias is a problem with case control studies,

developing disease make subject remember an

exposure. (will be explained later on )

Can be used to calculate incidence rate .

Incidence rate: is the number of new cases of a particular condition in

an at-risk population per unit time.

We want to see the incidenceof cancer among a population that is at risk of

developing cancer first of all we examine these people and we have to make sure that

they dont have cancer at the beginning of the research, and then we follow up these

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people for ten years for example, and we see the incidence of cancer during this

period of time.

Also called a longitudinal study.

Length of follow up time is based on how long it takes to develop the

disease. Some studies may take fifty years, and other studies can be

inherited from one generation to another!

Disadvantages :

Take a long time to perform especially when the disease develops

slowly.

Costly and inefficient for studying uncommon diseases (fewer

persons will develop the disease)

If you want to see the development of some types of cancer whish are very

rare, it may not occur, so its useless that you observe a sample for ten years

and finally you will have a result with no incidence, so its better used forcommon diseases.

Bias due to loss of subjects to follow up, some subjects may become

bored due to the long period of time, and they just drop off the

research.

Period of temporal changes may influence results.

I am doing a research about composite that may take thirty years, but after

ten years a new generation of filling material develops better than composite,so its useless to carry on the research.

Introduction of new instruments.

Change in clinical practice.

Answer of research question may become less relevant when the

study is complete.

When we finish a study after thirty years for example, the outcomes

may not be that important anymore.

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The End

DONE BY:

AMMAR ANAGREH

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(4) designing a study ii

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