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- typically explores specific and clearly defined questions that examine the relationship between events/occurrences
- Data could be gathered through surveys and questionnaires that are carefully developed and structured to provide you with numerical data that can be explored statistically and yield a result that can be generalised to some larger population

Quantitative research


- Exploratory and seeks to explain the ‘how’ and ‘why’ of a particular phenomenon

- Ex: Investigation of the ff:
*local knowledge and understanding of a given issue or programme
*people’s experiences, meanings and relationships
*social processes and contextual factors

Qualitative Research


Type of knowledge: subjective
Aim: Exploratory and observational
Characteristics: Flexible, Contextual portrayal, Dynamic, continuous view of change
Sampling: Purposeful

Qualitative Research


Data collection: semi-stuctured or unstructured
Nature of Data: Narratives, quotations, descriptions, value of uniqueness, particularity
Analysis: Thematic

Qualitative Research


Type of knowledge: Objective
Aim: Generalizable and testing
- Fixed and Controlled
- Independent and dependent variable
- pre- and post- treatment of change
Sampling: Random

Quantitative research


Data collection: Structured
Nature of Data: Numbers and Statistics
Analysis: Statistical

Quantitative research


Data can be classified into two broad categories:

Qualitative Data: Characteristics or traits for which numerical value can not be assigned e.g. motivation, confidence, honesty integrity etc.

Quantitative Data: Characteristics or traits for which numerical value can be assigned, are called variables, e.g. Achievement Intelligence Aptitude, Height, Weight etc



1. They should be collected through standardized tests
*If self-made test is used it should be reliable and valid

2. They are highly reliable and valid
*Therefore, generalization an conclusions can be made easily with certain level of accuracy

3. They can be easily interpreted with scientific accuracy



- The level of significance can also be determined
- The scoring system of this type of data is highly objective
- Its use is always based upon the purpose of the study
- Inferential statistics can be used


Four different levels of measurement namely:

- Nominal scale
- Ordinal scale
- Interval scale
- Ratio scale


- Least precise or crude of the four basic scales of measurement
- These are qualitative data which are mutually exclusive and exhaustive and do not mean hierarchy
- Numerals represent category or classification labels only

Nominal scale


- There is no particular order assigned to them
- The frequency or numbers are used to give a name to something that may be used for determining percent, mode
- Example: sex (male does not mean being better than female); boys and girls; pass and fail; rural and urban

Nominal scale


- More precise scale than the nominal scale
- Allows the assignment of values by arranging the observations in relative rank order
- No value is assigned to the distances to the positions of ranking
- Example: Economic class (low, middle, upper), scale of severity (none, mild, moderate, severe, very severe).

Ordinal scale


- More precise and refined scale than nominal and ordinal scales
- Has all the characteristics and relationship of the ordinal scale, besides which distances between any two numbers on the scale are known

Interval Measures


- The intervals between numbers are equal but not related to true zero
- They do not represent true quantity
- Example temperature, calendar years

Interval Measures


- Has the properties of interval scale plus two additional characteristics:
*This scale has a true, rather than arbitrary ‘zero’. It is possible to indicate the complete absence of property
*Ex: The zero point on a centimeter scale indicates the absence of height

Ratio scale


- Has the properties of interval scale plus two additional characteristics:
*The ratio scale numerals have the qualities of real numbers, and can be added, subtracted, multiplied; or divided

Ratio scale


Reporting of Quantitative Data

- Units - Ex: number of staff that have been trained; number of children enrolled in school for the first time
- Prices - Ex: amount of money spent on a building, or the additional revenue of farmers following a seed distribution programme
- Proportions/percentages - Ex: proportion of the community that has access to a service


Reporting of Quantitative Data (2)

- Rates of change - Ex: percentage change in average household income over a reporting period
- Ratios - Ex: ratio of midwives or traditional birth attendants to families in a region
- Scoring and ranking - Ex: scores given out of ten by project participants to rate the quality of service they have received


Quantitative Data Collection

- Surveys and questionnaires
- Biophysical measurements (Ex: height and weight of a child)
- Project records (Ex: the number of training events held and the number of participants attending)
- Service provider of facility data


- Measurement instrument for assessing individual’s attitudes, beliefs, behavior or attributes
- Before constructing your questionnaire be clear about its purpose and the information you want to get



Design of the questionnaire can be split in to three elements:

- Determine the questions to be asked
- Select the question type for each question and specify the wording
- Design the question sequence and overall questionnaire layout


- A key link needs to be established between the research aims and the individual questions via the research issues
- Issues and questions can be determined through a combined process of exploring the literature and thinking creatively

Determine the Questions to be Asked


- limit the responses and the information to be collected to the available choices
- Advantage is that coding and analyses are often easy
- To avoid losing information it is best for close-ended questions to include all possible answers
*If this is not possible include “others and specify” as one of the choices

Close- Ended Questions


- Allow the researcher to obtain greater information especially on attitudes and opinions
- Coding and analyses are often difficult
- Ex: What do you usually use as guide for managing your patients?

Open- Ended Questions


The most appropriate questionnaire will probably be a combination of __

both open and close-ended questions


Some general rules can be stated on question wording:

- Be concise and unambiguous
- Avoid double questions
- Avoid questions involving negatives
- Ask for precise answers
- Avoid leading questions


- Avoid jargon
- Ex:
“Kayo po ba ay naniniwala na lahat ng bata ay nararapat na ma-immunize?”
“Kayo po ba ay naniniwala na lahat ng bata ay nararapat na mabakunahan?”

Be Concise and unambiguous


- Check for ambiguity and make sure that the answer may be competently answered
- Ex:
Kayo po ba ay nagkalagnat ng mga nakaraan araw?
Kayo po ba nagkalagnat ng mga nakaraang isang linggo?

Be Concise and unambiguous


- Ex:
Do you eat a balanced diet and exercise regulary__ Yes _NO
Do you eat a balanced diet? __ Yes _NO
Do you exercise regularly? __ Yes _NO

Avoid Double Questions


- Ex:
“Kayo po ba ay hindi naniniwala na lahat ng bata ay nararapat na mabakunahan?”
“Kayo po ba ay naniniwala na lahat ng bata ay nararapat na mabakunahan?”

Avoid questions involving negatives


- Leading questions such as “Do you agree with the majority of people that the health service is failing?” should be avoided for obvious reasons that any right-minded individual can see. Don’t you agree?

Avoid leading questions


- Designed to elicit the interviewee’s knowledge or perspective on a topic
- Individual interviews, which can include key informant interviews, are useful for exploring an individual’s beliefs, values, understandings, feelings, experiences and perspectives of an issue
- Allow the researcher to ask about a complex issue, learning more about the contextual factors that govern individual experiences.



Interviews: Advantages

- Usually yield richest data, details, new insights
- Permit face-to-face contact with respondents
- Provide opportunity to explore topics in depth
- Allow interviewer to experience the affective as well as cognitive aspects of responses
- Allow interviewer to explain or help clarify questions, increasing the likelihood of useful responses
- Allow interviewer to be flexible in administering interview toparticular individuals or in particular circumstances


Interviews: Disadvantages

- Expensive and time-consuming
- Need well-qualified, highly trained interviewers
- Interviewee may distort information through recall error, selective perceptions, desire to please interviewer
- Flexibility can result in inconsistencies across interviews
- Volume of information very large; may be difficult to transcribe and reduce data


- Organised discussion between 6 to 8 people
- Provide participants with a space to discuss a particular topic, in a context where people are allowed to agree or disagree with each other
- Allow you to explore how a group thinks about an issue, the range of opinions and ideas, and the inconsistencies and variations that exist in a particular community in terms of beliefs and their experiences and practices
- Recruit participants for whom the issue is relevant (purposive recruitment)

Focus Groups


- defined as a group of people with a common characteristic such as place of residence, religion, gender, age, use of hospital services, or life event (such as giving birth)



Types of Population based on permanency of membership

- Fixed population
- Dynamic or open population


- Population whose membership is permanent
- Membership is always defined by a life event
- Ex: the people who were in Hiroshima, Japan, when the atomic bomb exploded at the end of World War II are members of a fixed population.
*This population will never gain any new members because only people who were at this historical event can be members

Fixed population


- Membership is defined by a changeable state or condition, and so is transient
- A person is a member of a dynamic population only as long as he or she has the defining state or condition
- Ex: Population of the city of Manila is dynamic because people are members only while they reside within the city limits

Dynamic or open population


- Turnover is always occurring because people enter the city by moving in or by birth, and people leave the city by moving away or by death
- include groups defined by geographic and hospital catchment areas, religious groups, and occupations

Dynamic or open population


- describes a situation in which the number of people entering the population is equal to the number leaving

Steady state


- is usually based on a combination of physical and pathological examinations, diagnostic test results, and signs and symptoms
- Which and how many criteria are used to define a “case” (a person who meets the disease definition) has important implications for accurately determining who has the disease

definition of a disease


Measuring Disease Occurrence: Three factors to consider

- Number of people that are affected by the disease
- Size of the population from which the cases of disease arise
- Length of time that the population is followed

*Failure to consider all three components will give a false impression about the impact of the disease on a population.


Three types of calculations are used to describe and compare measures of disease occurrence:

- Ratios
- Proportions
- rates


- simply one number divided by another
- Entities represented by the two numbers are not required to be related to one another
- Individuals in the numerator can be different from those in the denominator.
- Ex: “sex ratio” - ratio of two unrelated numbers; the number of males divided by the number of females, usually expressed as the number of males per 100 female



- One number divided by another, but the entities represented by these numbers are related to one another
- The numerator of a proportion is always a subset of the denominator
- Also known as fractions, are often expressed as percentages and range from 0 to or 0% to 100%.
- Ex: the proportion of U.S. residents who are black is the number of black residents divided by the total number of U.S. residents of all races



- one number divided by another, but time is an integral part of the denominator
- Ex: speed rate



Two basic measures of disease frequency

- Incidence
- Prevalence


- defined as the occurrence of new cases of disease that develop in a candidate population (population at risk) over a specified time period
- deals with the transition from health to disease



Two types of Incidence

- Cumulative incidence
- Incidence rate


- May also be called as Incidence proportion
- the proportion of a candidate population that becomes diseased over a specified period of time
- Possible value ranges from 0 to 1 or, if expressed as a percentage, from 0 to 100%

Cumulative Incidence/ Cumulative risk


Number of new cases of disease / Number in candidate population over a period of time

Cumulative Incidence/ Cumulative risk


- Time is not an integral part of this proportion but rather is expressed by the words that accompany the numbers of the cumulative incidence measure
- Can be thought of as the average risk of getting a disease over a certain period of time
- Generally, the cumulative incidence over a long period of time (such as a lifetime) will be higher than that over a few years
- is usually reserved for fixed populations

Cumulative Incidence/Cumulative risk


- the occurrence of new cases of disease that arise during person-time of observation
- The incidence rate’s denominator integrates time (t), and so it is a true rate

Incidence Rate


Incidence Rate

Number of new cases of disease/Person-time of observation in candidate population


- its dimension is 1/t or t-1
- possible values range from zero to infinity
- Person-time is accrued only among candidates for the disease.
- A person contributes time to the denominator of an incidence rate only up until he or she is diagnosed with the disease of interest

Incidence Rate


- It is not based upon the assumption that everyone in the candidate population has been followed for a specified time period
- Person-time is accrued only while the candidate is being followed
- Accrual of person-time stops when the person dies or is lost to follow-up

Incidence Rate


Relationship Between Cumulative Incidence and Incidence Rate

CI = IRi x Ti
where CI is cumulative incidence, IRi is incidence rate, and Ti is the specified period of time.


- measures the frequency of existing disease
defined as the proportion of the total population that is diseased
- Two types
*point prevalence
*period prevalence



- refers to the proportion of the population that is diseased at a single point in time
- can be thought of as a single snapshot of the population
point can be either a particular calendar date such as December 1, 2005 or a point in someone’s life such as college graduation

Point Prevalence


Point Prevalence

Number of existing cases of disease/Number in total population at a point in time


- refers to the proportion of the population that is diseased during a specified duration of time, such as during the year 2005
- includes the number of cases that were present at the start of the period (for example, January 1, 2005) as well as the number that developed during the period (for example, January 2 through December 31, 2005).

Period Prevalence


Period Prevalence

Number of existing cases of disease/Number in total population during a period in time


- Unlike the numerator for the two incidence measures, the prevalence numerator includes all currently living cases regardless of when they first developed
- The denominator includes everyone in the population— sick, healthy, at risk, and not at risk
- Because prevalence is a proportion, it is dimensionless and its possible values range from zero to one, or 0 to 100%



This equation assumes that the population is in steady state (that is, inflow equals outflow) and that the incidence rate and duration do not change over time.

P (1-P) = IR x D


If the frequency of disease is rare (that is, less than 10%), the equation simplifies to:

P = IR x D


Uses of Incidence

- Evaluation of the effectiveness of programs that try to prevent disease from occurring in the first place
- Researchers who study the causes of disease prefer to study new cases over existing ones because they are usually interested in exposures that lead to developing the disease
- Many researchers prefer to use incidence because the timing of exposures in relation to disease occurrence can be determined more accurately


Uses of Prevalence

- useful for estimating the needs of medical facilities and for allocating resources for treating people who already have a disease
- Researchers who study diseases such as birth defects (where it is difficult to gather information on defects present in miscarried and aborted fetuses) and chronic conditions such as arthritis (whose beginnings are difficult to pinpoint) have no choice but to use prevalence


- Total number of deaths from all causes per 100,000 population per year

Crude mortality (or death) rate


- Number of deaths from a specific cause per 100,000 population per year

Cause-specific mortality (or death) rate


- Total number of deaths from all causes among individuals in a specific age category per 100,000 population per year in the age category

Age-specific mortality (or death) rate


- Number of existing or new cases of a particular disease or condition per 100 population

Morbidity rate


- Number of deaths per number of cases of disease.

Case fatality rate


Experimental Studies in Humans – Clinical Trials
(Purpose of Experiments)

- To draw conclusions about a procedure or treatment
- To determine whether there is a difference between the different groups


- Experimental drug or procedure is compared with another drug or procedure (placebo or another drug)

Controlled Trials


- Studies in which the investigator’s experience with a drug or treatment is described
- No comparison with another group

Uncontrolled Trials


- The direct comparison of two or more treatment modalities in human groups
- A method of evaluating treatment
- Uses the experimental design

Clinical Trials


- Subjects exposed to the treatment and to a comparator (placebo or standard treatment)
- Subjects and Researchers “blinded” to the actual exposure
- Specific outcomes are measures and compared using statistical analysis to determine significant effects

Clinical Trials


Risk of Disease With Drug A (Rt)

Rt = A / A+B


Risk of Disease w/out Drug A (Rc)

Rc = C / C+D


Relative Risk

Rt / Rc = Relative Risk

*Risk of Disease with tx relative to control


Absolute Risk Reduction

Absolute Risk Reduction = ARR = Rc – Rt


Relative Risk Reduction (RRR)

RRR = ARR / Rc

*Decreased Risk of Developing Disease due to Tx


Number Needed To Treat

NNT = Number Needed To Treat = 1/ARR


- The clinical decision making process is based on probabilities.
- The purpose is to increase the probability of disease towards 100%.
- may affect treatment plans or subsequent diagnostic tests.
- When the estimated likelihood of a disease is close to 100%, the disease is confirmed

Diagnostic Testing


Diagnostic Testing

1. History taking
2. Physical Examination
3. Tests are usually ordered to confirm a clinical impression
4. Tests may also be ordered to assess the severity of the illness, to predict disease outcome, or to monitor response to therapy.


The most definitive diagnostic method is referred to as the __.

“gold standard”


Characteristics of Diagnostic Tests

Screening Tests – must be sensitive
Confirmatory Tests – must be specific
Cutoff point – the point at which a test changes from negative to positive
Accuracy: Sensitivity, Specificity
Estimates of Probability: PPV, NPV, LR


- is defined as the percentage of persons with the disease of interest who have positive test results

Sensitivity of a test



Sensitivity = A / A+C x 100


- is defined as the percentage of persons without
the disease of interest who have negative test results




Specificity = D / B+D x 100


Tests with a high sensitivity are useful clinically to __.

rule out a disease


Tests with a high specificity are used to __.

confirm the presence of disease


- is defined as the percentage of persons with positive test results who actually have the disease of interest

Positive Predictive Value


Positive Predictive Value

+ Predictive Value = A / A+B x 100


- is defined as the percentage of patients with a negative
test result and do not have the disease of interest

Negative Predictive Value


Negative Predictive Value

- Predictive Value = D / C+D x 100


- is the probability of that test result in patients with disease………
- probability of that test result in patients without the disease

Likelihood Ratio of a positive test


Likelihood Ratio of a positive test

= sensit / 1 – specif
= A/A+C divided by B/B+D


Types of Clinical Studies

Case Series
Cross-sectional Survey

Case-Control Study Cohort Study

Experimental: Clinical Trials


- CLINICAL QUESTION: What happened?
- Rare diseases
- Illnesses with long latency periods
- Evaluation of a wide range of potential etiologic exposure

Case-Control Study


- means a group of subjects followed over a period of time
Main Objectives :
- To describe the incidence of certain outcomes over time (DESCRIPTIVE)
- To analyze associations between risk factors and those outcomes (ANALYTICAL)

Cohort Study


- CLINICAL QUESTION: What will happen?
- Sample of subjects without the outcome of interest
- Predictor variables measured
- Subjects followed over a period of time
- Most effective way to establish the temporal sequence of predictor and outcome variables

Prospective Cohort Study


- Sample of subjects with the outcome of interest
similar to the prospective cohort study except that baseline measurements, follow-up, and outcomes all happened in the past
- Only possible if there is adequate data on the risk factors and outcome

Retrospective Cohort Study


- Exposure status and Disease status are measured at one point in time
- Useful for chronic illnesses (gradual onset, long duration)
- Prevalence studies
- Less costly than cohort studies

Cross-Sectional Studies


Why do we order Laboratory Examinations?

- Plan an intervention
- Monitor response to therapy
- Estimate risk of future events
- Determine prognosis in patients with a known disease.
- To diagnose the presence or absence of a particular condition


In articles regarding diagnostic tests, the diagnostic modality o f interest is usually compared with a reference standard to check its accuracy

Reference Standard


- Yardstick with which the performance of the test is measured
- Ideally
*test that gives the information nearest to the “truth”
*test that can unequivocally establish presence or absence of the disease in question

Reference Standard


- proportion of persons with disease who correctly have a positive test, i.e. a/(a+c);

Sensitivity (sn)


- proportion of persons with no disease who correctly have a negative test, i.e. d/(b+d).

Specificity (sp)


- measure of how much the likelihood of disease changes given a test result
- Could be computed from sensitivity and specificity
LR (+) = Sn/1‐Sp
LR (-) = 1‐Sn/Sp

Likelihood ratio (LR)


Strength of test by likelihood ratio

LRs >10 or


- Have very few false negatives, therefore virtually all negative tests must occur in non-diseased people
- It is Negative in Health (NIH)
- A sensitive test rules out disease – and the mnemonic is SnOut (Sensitive = ruled Out)
- For screening especially for serious diseases that are easily treated, we want to do a sensitive test

Highly sensitive test


- Have very few false positives, therefore any positive tests must occur in diseased people
- It is Positive in Disease (PID)
- A specific test rules in disease – and the mnemonic is SpIn (Specificity = ruled In).
- For detection of disease not easily treated or for which the treatment is potentially dangerous, we want a highly specific test
- For confirmation of disease

Highly specific tests


- larger group to which the study results are to be generalized
- Ex: If one is studying a new drug for the treatment of hypertension, the target population is all the hypertensive patients in the world

Target Population


- representative group from the target population

Study or sample population


- Any inferences from a sample refer only to the defined population from which the sample has been properly selected
- Ex: If a sample of doctors in the Philippines was found to follow guidelines only 30 % of the time, can we say that all doctors all over the world are follow guidelines only 30 % of the time?

Study Population


For the study population to be clearly defined, we should be guided by the following questions:

- What or who should be the study population?
- When and where should the study population be recruited?
- How should the study population be selected?


- Exact characteristics of the study population must be defined
- Should correspond to the characteristics of the target population
- This is usually stated as an inclusion and exclusion criteria

What and who should be the study population


- A statement indicating when and where the study population will be recruited is oftentimes necessary
- Ex: “Patients consulting for low back pain at the Family Medicine Clinic of the Manila Doctors Hospital between the period January 1 to December 31, 2017 will be recruited for the study”.

When and where the study population be recruited


- method of selecting the study/sample population



Two categories of Sampling

- Probability sampling /Random Sampling
- Non-probability sampling


- Also called Random sampling
- When random sampling is used, each element in the population has an equal chance of being selected (simple random sampling) or a known probability of being selected (stratified random sampling)

Probability Sampling


- The sample is representative because the characteristics of a properly drawn sample represent the parent population in all ways
*Remember: To make accurate inferences, the sample has to be representative

Probability Sampling


- made by non-random methods
- target population is difficult to identify
- This may not be a limitation when generalization of results is not intended
*The results would be valid for the sample itself (internal validity)

Non-probability sampling


- Another limitation of non-random samples is that statistical inferences such as confidence intervals and tests of significance cannot be estimated from non-random samples

Non-probability sampling


Examples of Probability Sampling

- Simple random sampling
- Systematic sampling
- Cluster sampling
- Multi-stage sampling
- Stratified sampling
- Disproportional Sampling


- Every individual in the population being sampled has an equal likelihood of being include.
- Basis of all good sampling techniques
- Disallows any method of selection based on volunteering or the choice of groups of people known to be cooperative

Simple random sampling


- Identify all individuals from whom the selection will be made
- Assign a number to each individual in the list
- Select certain numbers by reference to random number tables which are published in standard statistical textbooks or generated by statistical software such as EPI INFO, Excel

Simple random sampling


- Identify all individuals from whom the selection will be made
- The total number in the list is divided by the sample size to get the sampling interval.
- Randomly select a starting point in the list and include the subject in the list at every sampling interval.

Systematic Random Sampling


This is equal to simple random sampling as long as the list is not arranged in a particular order

Systematic Random Sampling


- Studies may be carried out on large populations which may be geographically quite dispersed
- In such cases, clusters may be identified and random samples of clusters will be included in the study
- Every member of the cluster will also be part of the study.

Cluster Sampling


- This introduces two types of variations in the data – between clusters and within clusters – and this will have to be taken into account when analyzing data
- Cluster sampling may produce misleading results when the disease under study itself is distributed in a clustered fashion in an area.

Cluster Sampling


- Strict random sample may be difficult to obtain and it may be more feasible to draw the required number of subjects in a series of stages
- In this type of sampling primary sample units are inclusive groups and secondary units are sub-groups within these ultimate units to be selected which belong to one and only one group

Multi-stage Sampling


- The accessible population is first divided into nonoverlapping strata such as range of age, sex, economic status etc
- In each strata subjects are randomly selected either by simple or systematic random sampling

Stratified Random Sampling


- If different strata have different size, sampling can be done whose size is proportional to the size of the strata that may lead to different sizes between different strata.

Disproportional Sampling


Examples of Non-Probability Sampling

- Convenience Sampling
- Purposive Sampling
- Snowball Sampling
- Quota Sampling


- simply use participants who are available at the moment
- quick, inexpensive, and convenient
- Ex:
In shopping malls or airports, individuals are selected as they pass a certain location and interviewed concerning issues, candidates, or other matters.
Phone surveys may be based on anyone answering the phone between the hours of 9 A.M. and 5 P.M.

Convenience Sampling


- Researcher selects a certain subject because they fulfill some specific criteria
- Pick out the sample is picked out in relation to some criterion, which are considered important for the particular study.

Purposive sampling


- Subject who was already included are asked to identify others who also have the same requisite characteristics

Snowball Sampling


- This technique is often used by market researchers and those taking political polls
- The intent is to select a sample whose frequency distribution of characteristics reflects that of the population of interest

Quota Sampling


- Decide the group interest
*Dictated by the nature of the problem being investigated
For issues of national interest, frequently used subsets are age, race, sex, socioeconomic level, and religion.

- Know the percentage of individuals making up each subset of the population

- Match these percentages in the sample.
*For example, if you were interested in ethnic groups, and knew their population percentages, you would select your sample so as to obtain these percentages

- Choose within each subgroup
*Often individuals are selected in the sample on the basis of availability
*Quotas could be selected through knocking on doors, telephoning numbers, or sending mailings until the sample percen tages for subsets match those of the population

Quota Sampling


- Process in which each subject is given the same chance of being assigned to the different study groups.
- Purpose is to make the groups comparable with respect to known and unknown variables that might affect the outcome of the study



- One method to do this is through toss of a coin.
*If the coin turns up heads the subject is assigned to A, if the coin turns up tails the subject is assigned to B
- Another simple randomization procedure can be done using the table of random numbers

Simple Randomization


- Blocked randomization is used to avoid imbalance in the number of subjects assigned to each group
- If 20 subjects will be randomized to two groups using simple randomization might result to 12 subjects being assign to one group and 8 to the other

Block Randomization


- In blocked randomization each block, say 4, is designed to have equal number of A and B by enumerating the possible combinations of 2 A’s and 2 B’s (AABB, ABAB, ABBA, BAAB, BABA, BBAA).
- The combinations are then selected at random until all 20 subjects are randomized

Block Randomization


- prognostic factors that may affect the outcome such as age, severity of illness etc. are identified.
- They are then divided into different strata such as less than 20 as the first strata, 20-50 as the second strata, and greater than 50 as the last strata.
- The subjects in each strata are then randomized (simple or blocked) to their group assignment

stratified randomization


The formula to be used for estimating the sample size may depend on:

- Type of the study
- Outcome to be measured


The __ describes the purpose of the study, the importance of the research question, the methodology and justifies the feasibility of the project



Purposes of Research Proposal

- Represents the synthesis of the researcher’s critical thinking and the scientific literature to ensure that the research question is refined enough to be studied
- Serves as an application for review by peers, administrative committees or funding agencies
- Enhance communication among colleagues who may be co-investigators
- Serves as guide throughout the study to ensure that the researchers follow the outlined rules of conducting the study


General Rules (Research Proposal): Word of Choice

- Words chosen should be simple, precise, necessary and familiar
*Highly technical and scientific terms should be used less often and only when necessary
*Avoid jargon or inventing new words by adding suffixes or prefixes to familiar words.

- Use few abbreviatons as least as possible


General Rules (Research Proposal): Sentence Structure

- Use simple and direct sentences.
*This can be done if the core or the message is conveyed in a simple sentence structure

- Avoid piling of nouns into noun cluster

- Avoid putting too many ideas in one sentence.
*A sentence should only talk about one thing at a time.

- Aim for a mean sentence length of no more than 20 words per sentence.


General Rules (Research Proposal): Paragraph Structure

- Should convey an organized idea
- Continuity of these ideas must be clear.
*Paragraph should have a definite structure.
*It should be started with a topic sentence followed by a series of logically arranged supporting sentences


- Contains key words found in the study objectives or the research question
- Describes the main idea of the project
- Serves to orient the reviewer as to what he or she is about to read
- Should be not so brief that it says nothing or leaves some things hanging, but it should not be so long that a reviewer has to think hard to figure out what it means



- Name of investigators (main investigator first and not the most senior), their institution and addresses
- Limit authors who will actively participate in the study from planning, conduct of the study, analysis and writing the final paper.

List of investigators


- Should not exceed 300 words, single spaced and following standard structure:
- Objectives – state the main question of the study
- Study design – describe design as to the appropriate use of randomization, blinding and temporal direction
- Setting – indicate the study setting (hospital, clinic, community)
- Patients/participants – state selection procedures, entry criteria and numbers participants entering and completing the study

Protocol abstract


- Interventions – describe the essential features of any intervention including their method and duration of administration
- Main outcome measures – primary outcome measures should be indicated as planned before data collection begins
- Data analysis
*state sample size and considerations for its calculation and exact level of significance
*State specific statistical methods to test difference or association
- Conclusion – state expected primary conclusions according to study objective along with the clinical application

Protocol abstract


- Should awaken the interest of the reader and prepare him or her to understand the paper.
- Should be direct to the point, specific rather than vague and general.
- Its structure is like a “funnel”.
*The broader mouth of the funnel represents topics known about the subject This is followed by topics not yet known. What is not yet known usually leads to the research question




- Research design or strategy
*state the design (randomized controlled trial, cohort or case-control, etc.)

- Sample population
*Define target population, demographic area, and how they will be selected
*Describe randomization if it will be done



- Describe how intervention will be given
- Describe the process of blinding if it will be done, how to encourage compliance and other co-intervention

Experimental intervention



- Specify outcome attributes being measured, how they will be measured, and ways of minimizing bias in its measurement

Outcome measurement


Analysis – specify the statistical analysis to be undertaken
Pilot study – describe how and to whom it will be done
Ethics – emphasize informed consent, confidentiality, insurance policy if any



- Should be designed according to ethical standards (amended Helsinki declaration)
- The form should be written in understandable language

Informed Consent


Informed Consent contains the following: (1-5)

1. A statement of the purpose of the study
2. Description of procedures both experimental and routine
3. Duration of the subject’s involvement in the study
4. Whom to contact in terms of adverse events or additional questions
5. Risks and discomfort associated with participation in the study


Informed Consent contains the following: (6-10)

6. Alternative appropriate treatment available in place of experimental treatment
7. Benefits the subject may expect from participation in the study
8. A statement that participation is voluntary
9. A statement guaranteeing confidentiality
10. A statement regarding compensation due to adverse events


Risks, Burdens and Benefits

Medical research involving human subjects may only be conducted if the importance of the objective outweighs the risks and burdens to the research subjects


- Every precaution must be taken to protect the privacy of research subjects and the confidentiality of their personal information.

Privacy and Confidentiality


- Participation by individuals capable of giving informed consent as subjects in medical research must be voluntary

Informed Consent


- an attribute or agent that is suspected to be related to the occurrence of a particular disease

Risk Factor


- The probability of developing the outcome without the exposure

Baseline Risk


HARM Studies

Particular Exposure > Undesirable Outcome


Harmful Exposures:

Behaviours (tobacco, alcohol, coffee)
Treatments (medications, radiation)
Patient characteristics (obesity, lifestyle)


Clinical Questions on Harm:

P : patient population at risk
E : exposure
O : outcome


Risk Factors / HARM

Genetic susceptibility / Race
Environment, Geographic location
Age, Sex
Personal lifestyle (hygiene, diet)
Medical Conditions
Medications, Household Items
Food and Drinks


- Essential for understanding the different approaches to prevention and control
- Useful in the formulation of hypothesis regarding the etiology of a disease

Natural History of Disease


(Stages in the natural history of disease)
- The disease has not developed but the groundwork has been laid by the presence of factors which affect its occurrence

1. Stage of susceptibility


(Stages in the natural history of disease)
- No manifest disease but pathogenic changes have started to occur

2. Stage of presymptomatic disease


(Stages in the natural history of disease)

- Sufficient anatomic or functional changes have occurred so that there are recognizable signs and symptoms of disease

3. Stage of clinical disease


(Stages in the natural history of disease)

- Residual defects of short or long duration leave the patient disabled to a greater or less extent

4. Stage of disability


Study Designs for HARM

- Randomized Controlled Trials
- Cohort Study
- Case-control study


Studies designed to test the likelihood of a cause-and-effect relationship between a risk factor and a disease are termed __.

hypothesis-testing studies


RCT for Harm:

- Highest Validity
- Low feasibiliy
- May be unethical to randomize to harmful exposures


Cohort Studies on HARM:

- Midway in validity because baseline characteristics rarely similar
- Statistical adjustments needed
- Big sample, long follow-up


Case-control studies on HARM

- Highest feasibility
- Good for rare outcomes
- No need for follow-up
- Need to ascertain past exposures


Validity Criteria in Harm Studies:

- Similarity in baseline characteristics
- Statistical adjustment for differences
- Unbiased criteria to determine exposure (recall bias?)
- Unbiased outcome measurement


Case Exposure Probability

Case Exposure Probability = Exposed cases/All cases
= A / A+B


Odds of case exposure

= A/A+B / B/A+B or = A/B


Odds of control exposure

Odds of control exposure = C/D


Odds Ratio (OR)

Odds Ratio (OR) = A/B ÷ C/D = AD / CB


How precise is the estimate of risk? Use 95% Confidence Interval

- When interval ends are on the side of benefit (1), exposure increases the odds of the outcome
- When one end is 1, the study in inconclusive. Exposure has no effect on the outcome when ends show small unimportant reductions.


Issues affecting applicability

Biological Issues
- Sex
- Co-morbidities
- Race
- Age
- Pathology

Socio-economic Issues


Estimating impact of a harmful exposure on individual patients

Step 1: Estimate baseline risk or pre-exposure risk in percent
Step 2 : Convert the baseline risk to baseline odds (Assume 2% risk)
Step 3: Multiply baseline odds by the Odds Ratio to get the post-exposure odds
Step 4: Convert post-exposure odds to post-exposure risk in percent