PHC215

By Dr. Khaled Ouanes Ph.D.

E-mail: k.ouanes@seu.edu.sa

Twitter: @khaled_ouanes

INTRODUCTION TO

HEALTHCARE RESEARCH

METHODS

Primary Studies:Selecting a Sample Population

Types of Research Populations

At least 4 different types of populations must be

considered when preparing to collect data:

1. The results of the study should be applicable to the target

population

2. The source population is a well-defined subset of individuals

from the target population

3. The sample population is the individuals from the source

population who are asked to participate

4. The study population is the members of the sample population

who actually participate in the study

Types of Research Populations

Target Populations

A well-defined study question identifies a target population to which the results of the study should apply.

A target population might be quite narrow (like one wing of a long-term acute care hospital) or relatively large (like a whole country).

Unless the target population is very small, measuring the entire target population or even randomly sampling from it may be impossible.

Source Populations

A source population (AKA sampling frame) consists of an enumerated list of population members.

Example:

All students of the same faculty/College

All women with a breast cancer diagnosis in the past 5 years who are indexed in a particular cancer registry

All members of a professional sports league

All households within 2 miles of a particular nuclear power plant

Sample Populations

A source population is often much larger than

the sample size required for a study. In this

situation, only a portion of the source population

is selected to serve as a sample population.

A variety of probability-based samplingtechniques can be used to select a samplepopulation.

Examples of Types of

Probability Sampling

Sometimes a non-probability-based convenience

population can be selected based on the ease of access

to those individuals, schools, or communities.

However, convenience sampling must always be used

with caution. Convenient sample populations are often

systematically different than the communities they are

intended to represent.

Sample Populations

Study Populations

The study population will consist of the members of

the sample population who can be located, who

consent to participation, and who meet all eligibility

criteria.

A 100% participation rate is extremely rare.

A low response rate may result in nonresponse bias

if the members of the sample population who agree

to be in the study are systematically different from

nonparticipants.

Study Populations

A less than 100% participation rate is usually not a

problem as long as the researcher:

Uses suitable and carefully explained sampling

methods

Takes appropriate steps to maximize the participation

rate

Recruits an adequately large sample size

Cross-Sectional Surveys

The goal of most cross-sectional surveys is to

describe a specific target population accurately.

Convenience samples rarely result in a study

population that is representative of the target

population.

Ideally, the researcher needs some way to confirm

that the source population is similar to the target

population and that the sample population is similar

to the source population.

Population

Example for

a Cross-

Sectional

Survey

Case-Control Studies

All cases must have the same disease,

disability, or other health-related condition.

The controls must be similar to the cases in

every way except for their disease status, so

cases and controls should be drawn from

populations with similar demographics.

Population

Example for

a Case-

Control

Study

Cohort Studies

Longitudinal cohort studies: the participants should berepresentative of the source and target populations Therequirements for longitudinal studies are similar to those forcross-sectional studies, since both study designs recruitpopulation-based samples.

Prospective / retrospective cohort studies: the exposed andunexposed should be drawn from similar populations Therecruitment of exposed and unexposed for cohort studies is likethe recruitment of the cases and controls for case-controlstudies.

Population

Example for a

Cohort Study

Experimental Studies

Experimental studies require a source population

that is reasonably representative of the target

population.

Safety is always the top priority in designing an

experimental study. The risk of harm to participants

can be reduced by selecting an appropriate source

population and defining strict inclusion and

exclusion criteria.

Population

Example

for an

Experiment

al Study

Vulnerable Populations

Vulnerable populations in health research include some

people with poor health, some people with limited decision-

making capacity, and members of some socially

marginalized groups, among others.

Despite the potential risks of including members of these

populations in research studies, including them is the only

way to study health issues in these groups.Example: The health of prisoners can only be studied by conducting research in

prisons.

Research conducted with members of vulnerable

populations requires extra consideration of the

potential risks of research to participants.

The ability of every participant to provide informed

consent free from coercion must be assured.

Concerns about the increased risks of adverse

effects from study participation must be addressed.

Vulnerable Populations

Community Involvement

Some studies benefit from or require the participation and/or support of whole geographic, cultural, or social communities and their leaders.

Community-based studies often work best when they use methods such as those developed for Community-Based Participatory Research.

Primary Studies:Estimating Sample Size

Importance of Sample Size

An adequate number of study

participants is required to achieve valid

and significant results

Importance of Sample Size

Importance of Sample Size

What you saw in the previous slide are 2 distributions of possible sample means

for 20 people (n=20) and 40 people (n=40), both drawn from the same

population. On each we have superimposed a sample mean weight change of

3kg. The curves are both centered on zero to indicate a null hypothesis of "no

difference" (ie. that the diet has no effect). It is more likely to be significant when

n=40 because the distribution curve is narrower and 3kg is more extreme in

relation to it than it is in the n=20 scenario, which points to how you can increase

the power of your experiment. The reason the n=40 curve is spikier is because of

something called the standard error of the mean.

Essentially, the larger the sample sizes, the more accurately the

sample will reflect the population it was drawn from, so it is

distributed more closely around the population mean. (Except

for some genetics studies)

Sample Size and Means

Bigger Samples Are Better

Large samples from a population are usually better than small ones at yielding a sample mean close to the true population value.

When the sample size is small, the sample mean may be quite far from the mean in the total population from which the sample was drawn. This is represented by a wide confidence interval that reaches far from the sample mean.

When the sample size is large, the sample mean is expected to be close to the population mean, and the confidence interval will be narrower.

Bigger Samples Are Better

Larger Samples from a Population Have a

Narrower 95% Confidence Interval Than Smaller

Samples

So, the goal is to recruit just the right number ofparticipants based on statistical estimations of howmany people are required to answer the studyquestion with a specified level of certainty.

If more participants are recruited than arestatistically required, resources are wasted.

If too few participants are recruited, the whole studywill be almost worthless because there will not beenough statistical power to answer the studyquestion.

Importance of Sample Size

Sample Size Estimation

A sample size calculator – more accurately called a sample size estimator – should be used to identify an appropriate sample size goal.

Sample size estimators suggest an appropriate minimum sample size based on a series of “best guesses” the researcher makes about the expectedcharacteristics of the sample population.

When in doubt, err on the size of a larger sample!

Examples of

Sample Size

Calculation

Power Estimation

Another way to check for sample size requirements is

to work backward from the number of participants

likely to be recruited to see whether that sample size

provides adequate statistical power for the study

design.

Statistical power is the ability of a statistical test to

detect significant differences in a population when

differences really do exist.

Sometimes a sample population does not capture the

true experience of the population:

Type 1 errors (α) occur when a study population

yields a significant statistical test result when one

does not exist in the source population.

Type 2 errors (β) occur when a statistical test of data

from the study population finds no significant result

when one actually exists in the source population.

Power = 1 – β

Power Estimation

Power

and

Errors

Examples of Power Calculation

Refining the Study Approach

Be prepared to rethink the study question, study approach, and/or target and source populations if the power for the estimated number of participants is

not sufficient.

PHC215

By Dr. Khaled Ouanes Ph.D.

E-mail: k.ouanes@seu.edu.sa

Twitter: @khaled_ouanes

HEALTHCARE RESEARCH METHODS

Based on the textbook of introduction to health research methods – K.H. Jacobsen