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# Chapter 7 - PowerPoint PPT Presentation

Chapter 7. Obtaining Subjects. Learning Objectives. Determine the valid connection between the study sample, study population, and theoretical population Identify and assemble appropriate sampling frames Select samples using appropriate sampling methods Recruit and retain study participants

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### Chapter 7

Obtaining Subjects

• Determine the valid connection between the study sample, study population, and theoretical population

• Identify and assemble appropriate sampling frames

• Select samples using appropriate sampling methods

• Recruit and retain study participants

• Maximize internal and external validity

• Calculate the required sample sizes

• It is extremely rare that a study can include every last individual in a population of interest

• Costly and time consuming

• No fool-proof method for identifying everyone in the population

• For this reason, most studies select a sample of individuals from the population of interest

• Ideally, the sample will be a good “representation” of the population

• Target or theoretical population – entire group to which the study results pertain

• Study population – members of the target population that are accessible by the researcher

• Sampling frame – concrete list of members of the study population

• Sample – Subgroup of individuals selected from the sampling frame

Target Population

Study Population

Sampling Frame

Sample

• Representative – degree to which the sample includes individuals with similar or identical characteristics to those of the target population

• Generalizable - generalizability is the degree to which study results are valid for members of the study population not included in the sample

• The degree of both of these depends on the accuracy and rigor in which the sample was selected

Representative

Generalizable

• Probability-based methods – ability to assign a numeric value to the probability of any one person being chosen for the study

• Ideally use a random method in which all individuals have an equal probability of being selected

• Simple random

• Systematic random

• Stratified random

• Ideal but rarely achievable

• Every individual in the sampling frame has the same probability of being selected for the sample

• If we have a population size of N and want a sample size of n, then each individual should have a (n/N) probability of being selected

• Say N=1000 and we want n=100

• p = 100/1000 = 0.10 or 10% chance of being selected

• If each person has the same chance of being selected, there should be no systematic difference between those selected and those not selected – the sample would NOT be biased

• Assign everyone in the sampling frame a number from 1 to N

• Generate numbers randomly

• Random numbers tables (found in old stats. books)

• Computerized number generators

• Mechanical method like lottery ball selector

• Select the individual with the corresponding number generated

• Appropriate when it is not possible or practical to assign numbers to each individual in the frame

• Appropriate when random numbers cannot be generated

• Choose every kth individual where

k = N/n

• If N=1000 and n=100 then k = 1000/100

• Organize individuals on a list or physically, such as boxes of biological specimens

• Calculate k

• Randomly select a number between 1 and k

• Start with the unit with the randomly selected number on the list, then keep counting down the list and choose every kthindividual or specimen

• Appropriate strategy to ensure that individuals with specific characteristics will be selected

• Sampling frame units are first stratified or grouped according to the characteristic(s) of interest for inclusion (e.g., racial/ethnic groups), then individuals are selected randomly from each group or strata

• Proportionate – individuals are selected to be the same proportion of the strata relative to the whole study population

• Disproportionate – individuals are oversampled (at a greater proportion) or undersampled (at a lesser proportion) relative to the study population depending on the needs of the study

• Make separate lists of individuals from the sampling frame for each strata of interest

• For example, a list of females and a separate list for males

• Assign numbers to each individual or record their original numbers separately for each strata

• Generate random numbers and select that assigned number from each strata

• Samples for which individuals have an unknown probability for being selected

• Appropriate when no sampling frame is available, for example:

• Homeless individuals who don’t visit shelters

• Victims of domestic violence who don’t contact the police or seek medical care

• Not possible to determine the representativeness of the sample, but this type of selection is the only one available for some types of target populations

• Include anyone who is eligible, no method of selection is used

• Students in school class

• Attendees at an event such as a health fair

• Sampling with a “purpose” – to include only individuals with relevant characteristics (e.g., injection drug users not in treatment)

• Still, no sampling frame is available

• Variations

• Expert sample – include only individuals with expertise relevant to the study topic

• Modal instance sample – include individuals who possess the “average” characteristics of the target population

• Heterogeneity sample – include those who represent the broad array of characteristics of the target population

• Quota sample – recruit a prescribed number of individuals with predetermined characteristics, then stop recruiting people with those characteristics when the desired number is achieved

• Snowball sample – locate the first few individuals with the desired characteristics, ask them to give contact information of others like them, contact and recruit their recommendations

• Conclusions cannot be drawn about the generalizability of sample results to the target population because the sample representativeness cannot be evaluated

• Such conclusions would be biased, by definition

• Process similar to “marketing”

• Study must be promoted to the potential participants

• Ideally use the same method of communication that will be used for the data collection

• in-person

• telephone

• internet

• Initial contact can also be made through referrals by professionals (e.g., physicians, pharmacists, therapists)

• Introductory letter, script for a phone call or in-person contact, and brochures

• Should be appropriate reading level and language for the target population

• Must be reviewed and approved by the IRB

Informing the Potential Participant(for the purpose of persuasion)

• Task necessary even before the informed consent process

• Who is conducting the study (e.g., funding agency, investigator’s institution, name and contact information)

• What is the purpose of the study and plan for disseminating results

• What, generally, will be asked of the subject

• When will the data collection take place

• Where will the data collection take place

• Significance

• Improving health for subject and/or society

• Potential subject will contribute to the improvement

• Legitimacy

• Funding agency

• Credentials of the investigator(s)

• Protection

• Confidentiality

• Monitoring by the IRB

• Cash/material incentives are not considered benefits in the IRB review

• BUT, they are very powerful persuaders for study participation

• Proper value of incentive

• Enough to be attractive

• Not so much to be coercive

• Often subject eligibility must be determined

• Evaluate the presence of inclusionary criteria and absence of exclusionary criteria

• Proper time to screen is after potential subjects have expressed willingness to participate

• Details of the screening depend on the research question

• Value of cohort and experimental studies rely on retention of subjects from baseline to follow-up(s)

• Incentive structure to maximize retention

• Perhaps increasing value with each subsequent follow-up

• Screening process can also include the collection of, sometimes extensive, contact information

• Contact information should be verified and possibly updated through the course of the study

• Internal validity – extent to which the sample is a good representation of the study population

• External validity – extent to which the sample is a good representation of the target or theoretical population

• Without internal validity it is impossible to have external validity

Theoretical Population

External Validity

Study Population

Internal Validity

Sample

• External threats

• Improperly defined target population

• Incomplete, inaccurate, or inappropriate sampling frame

• Internal threats

• Sampling errors

• High non-response

• High study attrition

• Selection bias - systematic error committed when sampling from the sampling frame; if a random procedure is planned and claimed but is not conducted properly, then selection bias may be the result

• Non-response bias - selected subjects who consent to participate in the study are systematically different in a way that may affect results from selected persons who do not participate

• Attrition bias - a systematic relevant difference between those retained and those lost to follow-up in cohort and experimental studies

• How many subjects are needed for the study?

• Statistical power – sample size is large enough to be able to detect hypothesized results that are statistically significant

• Too small – limited statistical power

• Too large – even weak and clinically insignificant results are found to be statistically significant

• Related to Type I and Type II errors

• Type I error - the error we make when we REJECT a TRUE null hypothesis (incorrectly concluding we HAVE support for our alternative hypothesis when we actually should NOT reject the null hypothesis)

• Can be seen as a false positive result

• Assign a maximum probability for making this error, called α, as usually 0.05 or no greater than a 5 percent chance of making this mistake

• Alternatively, we are 95 percent confident that our result is not due to chance and may be a real result

• Type II error - error we make when we do NOT reject a null hypothesis when the null hypothesis is really FALSE (incorrectly concluding we do NOT have support for our alternative hypothesis when we actually SHOULD reject the null hypothesis)

• Can be seen as a false negative

• Assign a maximum probability for making this error as usually 20 percent

• Probability is represented by β, and (1-β) is referred to the statistical power or a study.

• Want to have at least an 80 percent chance of rejecting a false null hypothesis

• Want to have 80 percent power to detect real results in our study

• Statistical power is largely dependent on sample size

• Example of role of n in a confidence interval formula

• The confidence interval around the mean estimate includes the standard value of Z (representing the alpha of 0.05) and sample size (n) in the denominator

• The larger the n, the smaller the interval will be. The smaller the interval, the more precise is the estimate

• Knowing or assuming the sample mean and standard deviation (σ) and using the Z value with 95 percent confidence, we can solve for n using the above formula

• Solving for n is a power analysis.

• Precise analysis depends on the study design and appropriate assumptions

• Examples follow

• Assumptions are educated guesses about values needed to solve for n

• Confidence level is typically 95%

• Desired power is usually 80%