Chapter 10
Data Interpretation Issues
Learning Objectives
• Distinguish between random and
systematic errors
• State and describe sources of bias
• Identify techniques to reduce bias at the
design and analysis phases of a study
• Define what is meant by the term
confounding and provide three examples
• Describe methods to control confounding
Validity of Study Designs
• The degree to which the inference drawn
from a study, is warranted when account it
taken of the study, methods, the
representativeness of the study sample,
and the nature of the population from
which it is drawn.
Validity of Study Designs
• Two components of validity:
– Internal validity
– External validity
Internal Validity
• A study is said to have internal validity
when there have been proper selection of
study groups and a lack of error in
measurement.
• Concerned with the appropriate
measurement of exposure, outcome, and
association between exposure and
disease.
External Validity
• External validity implies the ability to
generalize beyond a set of observations to
some universal statement.
• A study is externally valid, or
generalizable, if it allows unbiased
inferences regarding some other target
population beyond the subjects in the
study.
Sources of Error in
Epidemiologic Research
• Random errors
• Systematic errors (bias)
Random Errors
• Reflect fluctuations around a true value of
a parameter because of sampling
variability.
Factors That Contribute to
Random Error
• Poor precision
• Sampling error
• Variability in measurement
Poor Precision
• Occurs when the factor being measured is
not measured sharply.
• Analogous to aiming a rifle at a target that
is not in focus.
• Precision can be increased by increasing
sample size or the number of
measurements.
• Example: Bogalusa Heart Study
Sampling Error
• Arises when obtained sample values
(statistics) differ from the values
(parameters) of the parent population.
• Although there is no way to prevent a
non-representative sample from
occurring, increasing the sample size
can reduce the likelihood of its
happening.
Variability in Measurement
• The lack of agreement in results from
time to time reflects random error
inherent in the type of measurement
procedure employed.
Bias (Systematic Errors)
• “Deviation of results or inferences
from the truth, or processes leading to
such deviation. Any trend in the
collection, analysis, interpretation,
publication, or review of data that can
lead to conclusions that are
systematically different from the
truth.”
Factors That Contribute to
Systematic Errors
• Selection bias
• Information bias
• Confounding
Selection Bias
• Refers to distortions that result from procedures
used to select subjects and from factors that
influence participation in the study.
• Arises when the relation between exposure and
disease is different for th ...
EpidemiologyUnit 3Bias, Error, Confounding and Effect Modification4hrs
Radha Maharjan
MN(WHD)
Contents
3.1 Bias and Error in Epidemiology
3.1.1 Bias (Researcher and Respondent)
Recall Bias
Information Bias ( sponsor bias, social desirability bias, acquiescence Bias)
Selection Bias
Confirmation Bias
The halo effect.
Contents
3.1.2 Error
Systematic Error
Random Error
Confounding & Effect Modification
Definition of Error
A measure of the estimated difference between the observed or calculated value of a quantity and its true value.
Random error or Chance
It is the by-chance error
It makes observed value different from the true value
May occur through sampling variability or random fluctuation of the event of interest due to
biological variability, sampling error and measurement error (not due to machine)
lack of precision in the measurement of an association
Biological variability:
The natural variability in a lab parameter due to physiologic differences among subjects and within the same subject over time.
Differences between subjects due to differences in diet, genetics or immune status.
Sampling error:
Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data.
Measurement error:
Measurement Error (also called Observational Error) is the difference between a measured quantity and its true value.
Random error or Chance
Random error can never be completely eliminated since we can study only a sample of the population.
Random error can be reduced by
careful measurement of exposure and outcome
Proper selection of study
Taking larger sample- increase the size of the study.
Systematic error or Bias
Systematic error (or bias) occurs in epidemiology when results differ in a systematic manner from the true values.
Bias is any difference between the true value and observed value due to all causes other than random fluctuation and sampling variability.
This type of error is generally more insidious and hard to detect.
Systematic error or Bias
For example over-estimate of blood sugar of every subject by 0.05 mmol/l resulted from using inaccurate analyser.
The possible sources of systematic error are many and varied but the important biases are selection bias, measurement bias, confounding, information bias, recall (respondent) bias, etc..
Sources of error in epidemiological study
Common sources of error are
selection bias
absence or inadequacy of controls
unwarranted conclusions
improper interpretation of associations
mixing of non-comparable records
errors of measurement (intra-observer variation, inter-observer variation), etc.
The error can be minimised through
study design (by randomisation, restriction & matching) and
during analysis of the results (by stratification and statistical modelling) ..
Selection bias
Chapter 2
Study Designs
Learning Objectives
• List and define the components of a good
study design
• Compare and contrast observational and
experimental study designs
• Summarize the advantages and disadvantages
of alternative study designs
Learning Objectives
• Describe the key features of a randomized
controlled trial
• Identify the study designs used in public health
and medical studies
Study Designs
• Observational Studies
– Case-series study
– Cross-sectional (prevalence) survey
– Case-control study
– Cohort study
• Experimental Studies
– Randomized Controlled (Clinical) Trial
Inferences
• Observational studies – inferences limited to descriptions
and associations; with carefully designed analysis can
make stronger inferences (statistical adjustment)
• Experimental studies – cause and effect
In ALL studies – need careful definition of disease
(outcome) and exposure (risk factor)
Which Design is Best
• Depends on the study question
• What is current knowledge on topic
• How common is disease (and risk factors)
• How long would study take, what are costs
• Ethical issues
Case Report/Case Series
• Observational study
• Case report: Detailed report of specific
features of case
• Case series: Systematic review of common
features of a small number of cases
• Advantage: Cost-efficient
• Disadvantages: No comparison group, no
specific research question
Case-Series
• Simplest design – description of interesting
observations in a small number of individuals
• Usually case-series do not involve control patients
(i.e., patients free of disease)
• Usually lead to generation of hypotheses for more
formal testing
• Criticisms: not planned – no research hypotheses
Case-Series
• Gottleib (1981) studied 5 young homosexual
men with rare form of pneumonia and other
unusual infections
• Initial report was followed by more series (26
cases in NY and CA; “cluster” in southern CA;
34 cases among Haitians, etc.)
• Condition termed AIDS in 1982
Cross-Sectional Survey
• Observational study conducted at a point in
time
• Advantages: Cost-efficient, easy to implement,
ethical
• Disadvantages: No temporal information, non-
response bias
Cross-Sectional Survey
• Is there an association between diabetes and
cardiovascular disease (CVD)?
Patients
with
Diabetes
Patients without
Diabetes
Patients with
CVD
Prospective Cohort Study
• Observational study involving a group (cohort)
of individuals who meet inclusion criteria
followed prospectively in time for risk factor
and outcome information
• Advantages: Can assess temporal relationships
• Disadvantages: Need large numbers for rare
outcomes, confounding
Cohort Study
• Is there an association between hypertension and
cardiovascular disease?
CVD
Hypertension
No CVD
Cohort
CVD
No Hypertension
No CVD
Study Start Time
Cohort Studies
• Identify a group of individuals that meet
inclusion crit ...
EpidemiologyUnit 3Bias, Error, Confounding and Effect Modification4hrs
Radha Maharjan
MN(WHD)
Contents
3.1 Bias and Error in Epidemiology
3.1.1 Bias (Researcher and Respondent)
Recall Bias
Information Bias ( sponsor bias, social desirability bias, acquiescence Bias)
Selection Bias
Confirmation Bias
The halo effect.
Contents
3.1.2 Error
Systematic Error
Random Error
Confounding & Effect Modification
Definition of Error
A measure of the estimated difference between the observed or calculated value of a quantity and its true value.
Random error or Chance
It is the by-chance error
It makes observed value different from the true value
May occur through sampling variability or random fluctuation of the event of interest due to
biological variability, sampling error and measurement error (not due to machine)
lack of precision in the measurement of an association
Biological variability:
The natural variability in a lab parameter due to physiologic differences among subjects and within the same subject over time.
Differences between subjects due to differences in diet, genetics or immune status.
Sampling error:
Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data.
Measurement error:
Measurement Error (also called Observational Error) is the difference between a measured quantity and its true value.
Random error or Chance
Random error can never be completely eliminated since we can study only a sample of the population.
Random error can be reduced by
careful measurement of exposure and outcome
Proper selection of study
Taking larger sample- increase the size of the study.
Systematic error or Bias
Systematic error (or bias) occurs in epidemiology when results differ in a systematic manner from the true values.
Bias is any difference between the true value and observed value due to all causes other than random fluctuation and sampling variability.
This type of error is generally more insidious and hard to detect.
Systematic error or Bias
For example over-estimate of blood sugar of every subject by 0.05 mmol/l resulted from using inaccurate analyser.
The possible sources of systematic error are many and varied but the important biases are selection bias, measurement bias, confounding, information bias, recall (respondent) bias, etc..
Sources of error in epidemiological study
Common sources of error are
selection bias
absence or inadequacy of controls
unwarranted conclusions
improper interpretation of associations
mixing of non-comparable records
errors of measurement (intra-observer variation, inter-observer variation), etc.
The error can be minimised through
study design (by randomisation, restriction & matching) and
during analysis of the results (by stratification and statistical modelling) ..
Selection bias
Chapter 2
Study Designs
Learning Objectives
• List and define the components of a good
study design
• Compare and contrast observational and
experimental study designs
• Summarize the advantages and disadvantages
of alternative study designs
Learning Objectives
• Describe the key features of a randomized
controlled trial
• Identify the study designs used in public health
and medical studies
Study Designs
• Observational Studies
– Case-series study
– Cross-sectional (prevalence) survey
– Case-control study
– Cohort study
• Experimental Studies
– Randomized Controlled (Clinical) Trial
Inferences
• Observational studies – inferences limited to descriptions
and associations; with carefully designed analysis can
make stronger inferences (statistical adjustment)
• Experimental studies – cause and effect
In ALL studies – need careful definition of disease
(outcome) and exposure (risk factor)
Which Design is Best
• Depends on the study question
• What is current knowledge on topic
• How common is disease (and risk factors)
• How long would study take, what are costs
• Ethical issues
Case Report/Case Series
• Observational study
• Case report: Detailed report of specific
features of case
• Case series: Systematic review of common
features of a small number of cases
• Advantage: Cost-efficient
• Disadvantages: No comparison group, no
specific research question
Case-Series
• Simplest design – description of interesting
observations in a small number of individuals
• Usually case-series do not involve control patients
(i.e., patients free of disease)
• Usually lead to generation of hypotheses for more
formal testing
• Criticisms: not planned – no research hypotheses
Case-Series
• Gottleib (1981) studied 5 young homosexual
men with rare form of pneumonia and other
unusual infections
• Initial report was followed by more series (26
cases in NY and CA; “cluster” in southern CA;
34 cases among Haitians, etc.)
• Condition termed AIDS in 1982
Cross-Sectional Survey
• Observational study conducted at a point in
time
• Advantages: Cost-efficient, easy to implement,
ethical
• Disadvantages: No temporal information, non-
response bias
Cross-Sectional Survey
• Is there an association between diabetes and
cardiovascular disease (CVD)?
Patients
with
Diabetes
Patients without
Diabetes
Patients with
CVD
Prospective Cohort Study
• Observational study involving a group (cohort)
of individuals who meet inclusion criteria
followed prospectively in time for risk factor
and outcome information
• Advantages: Can assess temporal relationships
• Disadvantages: Need large numbers for rare
outcomes, confounding
Cohort Study
• Is there an association between hypertension and
cardiovascular disease?
CVD
Hypertension
No CVD
Cohort
CVD
No Hypertension
No CVD
Study Start Time
Cohort Studies
• Identify a group of individuals that meet
inclusion crit ...
The most ambitious definition of health is that proposed by WHO in 1948: “health is a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” but,
Practical definitions of health and disease are needed in epidemiology, which concentrates on aspects of health that are easily measurable and amenable to improvement.
Definitions of health states used by epidemiologists tend to be simple.
1. The ALIVE status of each SEX. (SEX needs to be integrated into th.docxketurahhazelhurst
1. The ALIVE status of each SEX. (SEX needs to be integrated into the only Male, Female, ND, and Other) (bar comparison chart, pie comparison chart)
2. How many Male, Female, ND, and Other are there in each ALIGN. (Bar comparison chart)
3. How many red-haired heroes do Marvel and DC have?
.
1. Some potentially pathogenic bacteria and fungi, including strains.docxketurahhazelhurst
1. Some potentially pathogenic bacteria and fungi, including strains of Enterococcus, Staphylococcus, Candida, and Aspergillus, can survive for one to three months on a variety of materials found in hospitals, including scrub suits, lab coats, plastic aprons, and computer keyboards. What can hospital personnel do to reduce the spread of these pathogens?
2. Human immunodeficiency virus (HIV) preferentially destroys CD4+ cells. Specifically, what effect does this have on antibody and cell-mediated immunity?
**Provide APA references for each
.
More Related Content
Similar to Chapter 10Data Interpretation IssuesLearning Objec.docx
The most ambitious definition of health is that proposed by WHO in 1948: “health is a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” but,
Practical definitions of health and disease are needed in epidemiology, which concentrates on aspects of health that are easily measurable and amenable to improvement.
Definitions of health states used by epidemiologists tend to be simple.
1. The ALIVE status of each SEX. (SEX needs to be integrated into th.docxketurahhazelhurst
1. The ALIVE status of each SEX. (SEX needs to be integrated into the only Male, Female, ND, and Other) (bar comparison chart, pie comparison chart)
2. How many Male, Female, ND, and Other are there in each ALIGN. (Bar comparison chart)
3. How many red-haired heroes do Marvel and DC have?
.
1. Some potentially pathogenic bacteria and fungi, including strains.docxketurahhazelhurst
1. Some potentially pathogenic bacteria and fungi, including strains of Enterococcus, Staphylococcus, Candida, and Aspergillus, can survive for one to three months on a variety of materials found in hospitals, including scrub suits, lab coats, plastic aprons, and computer keyboards. What can hospital personnel do to reduce the spread of these pathogens?
2. Human immunodeficiency virus (HIV) preferentially destroys CD4+ cells. Specifically, what effect does this have on antibody and cell-mediated immunity?
**Provide APA references for each
.
1. Taking turns to listen to other students is not always easy f.docxketurahhazelhurst
1. Taking turns to listen to other students is not always easy for young children. What does the research show about promoting good listeners in the classroom setting?
2. How would you help the shyest student to become a confident speaker? How would you help the overly confident speaker to have self-control? Why are these skills important to instill in children at this age? How can becoming a confident speaker encourage stronger advocacy skills for themselves? Likewise, how does maintaining self-control encourage better listening?
.
1. The main characters names in The Shape of Things are Adam and E.docxketurahhazelhurst
1. The main characters names in "The Shape of Things" are Adam and Evelyn, suggesting the play is a retelling of the original creation myth. Compare the original “Adam and Eve” and characters in the Judea-Christian creation account to Adam and Evelyn. How is The Shape of Things similar or different from the traditional Judea-Xian account? (Keep in mind the main difference being art and artistic versus theistic creation).
2. The “garden” is the museum, and roped off sculpture with the fig leaf is, like the tree of good and evil, what you’re not supposed to touch. Why does the author present the museum as a creation space? How is the sculpture like the tree of good and evil? What happens when they cross the line and touch (or photograph) it?
3. Compare Evelyn and Pygmalion as creators. How does their gender effect their position in history and creation? How do both their creations critique the culture in which they exist? Describe the "changes" to society that Evelyn and Pygmalion aspire to in their art.
4. How much are the creators (Evelyn and Pygmalion) in control of creation and their art work? Where does their control break down? What is the difference between creator and creature; or is the creature reducible to its creator?
5. When does Adam assert his own mind, (if at all) or veer towards independence by not relying on the tools to achieve superficial beauty that Evelyn imparts?
.
1. Select one movie from the list belowShutter Island (2010; My.docxketurahhazelhurst
1. Select one movie from the list below:
Shutter Island (2010; Mystery, Thriller; Leonardo DiCaprio, Mark Ruffalo
2. Watch the film you have selected as a psychology student and not merely as an ordinary film viewer (it is suggested that you watch the selected film multiple times).
3. Provide your own summary of the film, using psychological terms and concepts that you have learned in class and from your textbook. State clearly the psychological disorder you have seen portrayed in the film you have chosen, using DSM criteria/language. You should explain the psychological disorder portrayed in the movie. Determine and evaluate if the disorder identified in the film is accurate according to your textbook and other resource materials. Provide evidence using actual behaviors seen in the film. Is the depiction of the psychological disorder in the film accurate or not? Give evidence to support your claims using observable behaviors from the movie.
4. Based on the information from the film, determine what clinical diagnosis (or diagnoses) a character from the movie most likely has/have (can be the main character or supporting characters). Use criteria provided by the DSM-5 and provide an evidence-based diagnosis/diagnoses of the person. You will need to justify their diagnoses by demonstrating how the character’s symptoms meet some or all the criteria outlined in the DSM-5 as evidence of your diagnosis/diagnoses. Everything that you assert should be supported by evidence.
7. Be sure to use APA format using the latest edition of the APA Manual (7th edition).
.
1. Select a system of your choice and describe the system life-cycle.docxketurahhazelhurst
1. Select a system of your choice and describe the system life-cycle. Construct a detailed flow diagram tailored to your situation
2. What characteristics of an airplane would you attribute to the system as a whole rather than to a collection of its parts? Explain why.
.
1. Sensation refers to an actual event; perception refers to how we .docxketurahhazelhurst
1. Sensation refers to an actual event; perception refers to how we interpret the event. What are some cultural differences that might affect responses to particular stimuli, particularly in taste and pain?
2. Most of us feel like we never get enough sleep. What are the stages of sleep and what is the importance of sleep? What are some common sleep disorders and treatments?
.
1. The Institute of Medicine (now a renamed as a part of the N.docxketurahhazelhurst
1. The Institute of Medicine (now a renamed as a part of the
National Academies of Sciences, Engineering, and Medicine
) defined patient-centered care as: "Providing care that is respectful of and responsive to individual patient preferences, needs, and values, and ensuring that patient values guide all clinical decisions.”[1] While this definition clearly emphasizes the importance of a patient’s perspective in the context of clinical care delivery, it does not allow managers to focus on the actual “person” inside the institutional role of the patient.
In the same sense that a person who is incarcerated in a prison may receive extremely humane treatment, the “person” is still defined into the role of an “inmate,” and as such cannot, by definition, be granted the same rights and privileges as a non-institutionalized member of the civil order enjoys. In other words, I may be placed in a cell with great empathy and understanding of my preferences, needs, and values, but I am still being locked-up in jail.
No one is suggesting that being admitted into a jail cell is the same as being admitted into a hospital bed. There are many obvious differences between the two, including the basic purpose of the two institutions.
But while much is different, what is the same is how a pre-existing set of structured behaviors and processes are used to firmly, and without asking or negotiating, radically transform a “regular” person into a defined role of a “patient” that then can be diagnosed, treated, and discharged back into the world once the patient has finished their “time” in the “system.”
While patient-centered care emphasizes the value of increased sensitivity to a patient’s preferences, needs, and values, what we want to focus on is how decisions made by healthcare leaders affect the actual experience of a person receiving that care.
So with the "real person" in mind, this week's question is:
What can healthcare leaders do in improve the actual personal experience that "real people" go through as our "patients?"
(Be sure to develop your answers AFTER you review the definition and roles of "Leadership" in the readings for this week).
[1] Institute on Medicine, Crossing the Quality Chasm: A New Health System for the 21st Century, March, 2001
2. Health Information Technonogy - PPP Discussion
The board has created an innovation fund designed to foster improved quality, increased access, or reduced costs in healthcare delivery. Select a health information technology related to genomics, precision medicine, or diagnostics that you would propose to be funded for implementation. Prepare a PowerPoint presentation that describes the selected health information technology, what it does, why it would be beneficial, and what risks may be involved. Please note, this activity is weighted 5% toward the final grade. The PowerPoint should be no more than 5-6 slides with the presenter's notes. Follow the APA format.
.
1. The Documentary Hypothesis holds that the Pentateuch has a number.docxketurahhazelhurst
1. The Documentary Hypothesis holds that the Pentateuch has a number of underlying documents (alt., sources) that were ultimately gathered and sewn into the Pentateuch as we now have it. The method of separating those underlying documents is called source criticism. Please perform a source-critical analysis of Gen 1-3. In so doing, please identify the significant features that distinguish each underlying document. Note: There are many such features.
2. Why are covenants important in the Bible? What do they accomplish? Are they all the same, whether in structure or outlook? Do the different writers view them differently? What does the ancient Near Eastern background to the biblical covenant contribute to our understanding?
3. Dt 6:4 used to be translated
“Hear, O Israel: The LORD [YHWH] our God, the LORD [YHWH] is one.”
Currently, we translate
“Hear, O Israel: The LORD [YHWH] is our God, the LORD [YHWH] alone.”
In all likelihood, the second translation is grammatically preferable. What is the interpretive difference between “one” and “alone”? Is it significant? How, if at all, does this verse relate to the First Commandment? How does this verse relate to Gen 1:26, 3:22, and 11:7? How does this verse relate to the variant non-MT variant in Dt 32:8-9 (as reproduced in HarperCollins)? Why is any of this important?
Be sure to provide a careful, well-written essay which gives ample biblical examples (proof texts) to support the point(s) you wish to make.
.
1. Search the internet and learn about the cases of nurses Julie.docxketurahhazelhurst
1. Search the internet and learn about the cases of nurses Julie Thao and Kimberly Hiatt.
2. List and discuss lessons that you and all healthcare professionals can learn from these two cases.
3. Describe how the principle of beneficence and the virtue of benevolence could be applied to these cases. Do you think the hospital adminstrators handled the situations legally and ethically?
4. In addition to benevolence, which other virtues exhibited by their colleagues might have helped Thao and Hiatt?
5. Discuss personal virtues that might be helpful to second victims themselves to navigate the grieving process.
Scholarly article, APA format, and no grammar error
.
1. Search the internet and learn about the cases of nurses Julie Tha.docxketurahhazelhurst
1. Search the internet and learn about the cases of nurses Julie Thao and Kimberly Hiatt.
2. List and discuss lessons that you and all healthcare professionals can learn from these two cases.
3. Describe how the principle of beneficence and the virtue of benevolence could be applied to these cases. Do you think the hospital adminstrators handled the situations legally and ethically?
4. In addition to benevolence, which other virtues exhibited by their colleagues might have helped Thao and Hiatt?
5. Discuss personal virtues that might be helpful to second victims themselves to navigate the grieving process.
use reference and scholarly nursing article.
.
1. Review the three articles about Inflation that are found below th.docxketurahhazelhurst
1. Review the three articles about Inflation that are found below this.
Globalization and Inflatio
n
Drivers of Inflation
Inflation
and Unemploymen
t
2. Locate two JOURNAL articles which discuss this topic further. You need to focus on the Abstract, Introduction, Results, and Conclusion. For our purposes, you are not expected to fully understand the Data and Methodology.
3. Summarize these journal articles. Please use your own words. No copy-and-paste. Cite your sources.
4.The replies are due by the deadline specified in the Course Schedule.
Please post (in APA format) your article citation.
.
1. Review the following request from a customerWe have a ne.docxketurahhazelhurst
1. Review the following request from a customer:
We have a need to replace the aging Signage Application. This application is housed in District 4 and serves the district as well as two other districts. We would like a new application that can be used statewide to track all information related to road signs.
The current system is old and doesn’t do most of what we need it to.
The current system has a whole bunch of reports, but no way for the user to update them by themselves without getting IT involved.
We also can’t create our own reports, on-demand, when we need to. Currently, data is entered into the application manually by Administrative Staff, but in the future, we would like to be able to take a picture of the road sign using a phone app, and have it automagically populate the database with geospatial location and other information. We thought about having a Smart Watch interface, but we don’t need that. Also, the current method does not have any way to manage the quality of the data that is entered, so there is a lot of garbage information there. There is no way to centrally manage security access, with the existing application. We want to get real time alerts when a sign gets knocked over in an accident and have a dashboard that shows where signs have been knocked over across the state. This is kind of important, but not super-critical. We need to store location information, types of signs, when a new sign is installed, who installed it, etc. We plan to provide the phone app to drivers in each district who will drive around, take pictures of the signs, and upload them to the database at the end of each day, or in realtime, if a data connection is available.
Back in Central Office, reviewers will review the sign information and validate it. A report will be printed every month with the results and a map. There are probably other things, but we can’t think of anything else right now.
2. List the main goal(s) of this request
3. Write all the user stories you see (include value statements and acceptance criteria, if possible)
4. Prioritize the user stories as
a. Critical
b. Important
c. Useful
d. Out of Scope
5. Are the user stories sufficiently detailed? If not, what steps would you take to split them/further define them?
6. What are the known Data Entities?
7. Is there an implied business process? Draw an activity diagram or a flow chart of it
8. Who are the actors/roles?
9. What questions would you ask of the stakeholders to get more information?
10. What technology should be used to implement the solution?
11. What would you do next as the assigned Business Analyst working on an Agile team?
.
1. Research risk assessment approaches.2. Create an outline .docxketurahhazelhurst
1. Research risk assessment approaches.
2. Create an outline for a basic qualitative risk assessment plan.
3. Write an introduction to the plan explaining its purpose and importance.
4. Define the scope and boundaries for the risk assessment.
5. Identify data center assets and activities to be assessed.
6. Identify relevant threats and vulnerabilities. Include those listed in the scenario and add to the list if needed.
7. Identify relevant types of controls to be assessed.
8. Identify the key roles and responsibilities of individuals and departments within the organization as they pertain to risk assessments.
9. Develop a proposed schedule for the risk assessment process.
10. Complete the draft risk assessment plan detailing the information above. Risk assessment plans often include tables, but you choose the best format to present the material. Format the bulk of the plan similar to a professional business report and cite any sources you used.
.
1. Research has narrowed the thousands of leadership behaviors into .docxketurahhazelhurst
1. Research has narrowed the thousands of leadership behaviors into two primary dimensions. Please list and discuss these two behaviors.
2. Distinguish between charismatic, transformational, and authentic leadership. Could an individual display all three types of leadership?
.
1. Research Topic Super Computer Data MiningThe aim of this.docxketurahhazelhurst
1. Research Topic: Super Computer Data Mining
The aim of this project is to produce a super-computing data mining resource for use by the UK academic community which utilizes a number of advanced machine learning and statistical algorithms for large datasets. In particular, a number of evolutionary computing-based algorithms and the ensemble machine approach will be used to exploit the large-scale parallelism possible in super-computing. This purpose is embodied in the following objectives:
1. to develop a massively parallel approach for commonly used statistical and machine learning techniques for exploratory data analysis
1. to develop a massively parallel approach to the use of evolutionary computing techniques for feature creation and selection
1. to develop a massively parallel approach to the use of evolutionary computing techniques for data modelling
1. to develop a massively parallel approach to the use of ensemble machines for data modelling consisting of many well-known machine learning algorithms;
1. to develop an appropriate super-computing infra-structure to support the use of such advanced machine learning techniques with large datasets.
Research Needs:
Problem definition – In the first phase problem definition is listed i.e. business aims and objectives are determined taking into consideration certain factors like the current background and future prospective.
Data exploration – Required data is collected and explored using various statistical methods along with identification of underlying problems.
Data preparation – The data is prepared for modeling by cleansing and formatting the raw data in the desired way. The meaning of data is not changed while preparing.
Modeling – In this phase the data model is created by applying certain mathematical functions and modeling techniques. After the model is created it goes through validation and verification.
Evaluation – After the model is created, it is evaluated by a team of experts to check whether it satisfies business objectives or not.
Deployment – After evaluation, the model is deployed and further plans are made for its maintenance. A properly organized report is prepared with the summary of the work done.
Research paper Policy
· APA format
. https://apastyle.apa.org/
. https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/general_format.html
· Min number of pages are 15 pages
· Must have
. Contents with page numbers
. Abstract
. Introduction
. The problem
4. Are there any sub-problems?
4. Is there any issue need to be present concerning the problem?
. The solutions
5. Steps of the solutions
. Compare the solution to other solution
. Any suggestion to improve the solution
. Conclusion
. References
· Missing one of the above will result -5/30 of the research paper
· Paper does not stick to the APA will result in 0 in the research paper
· Submission
. you have multiple submission to check you safe assignments
. The percentage accepted is 1%.
1. Research and then describe about The Coca-Cola Company primary bu.docxketurahhazelhurst
1. Research and then describe about The Coca-Cola Company primary business activities. Include: Minimum 7 Pages. Excluding reference page
2.
A. A brief historical summary,
B. A list of competitors,
C. The company's position within the industry,
D. Recent developments within the company/industry,
E. Future direction, and
F. Other items of significance to your corporation.
3. Include information from a variety of resources. For example:
A. Consult the Form 10-K filed with the SEC.
B. Review the Annual Report and especially the Letter to Shareholders
C. Explore the corporate website.
D. Select at least two significant news items from recent business periodicals
The report should be well written with cover page, introduction, the body of the paper (with appropriate subheadings), conclusion, and reference page.
.
1. Prepare a risk management plan for the project of finding a job a.docxketurahhazelhurst
1. Prepare a risk management plan for the project of finding a job after graduation.
and
2. Develop a reward system for motivating IPT members to do their jobs more conscientiously and to take on more responsibility.
[The assignment should be at least 400 words minimum and in APA format (including Times New Roman with font size 12 and double spaced), and attached as a WORD file.]
Plagiarism free
.
1. Please define the term social class. How is it usually measured .docxketurahhazelhurst
1. Please define the term social class. How is it usually measured? What are some ways that social class is affecting health outcomes for people who become ill with COVID-19?
2. What is the CARES Act? Has it been enough? What has happened to people's ability to pay their bills since it expired?
3. As things stand now, data is showing higher COVID-19 related mortality rates for African Americans. Given what you know from the textbook and from the attached articles, what are some explanations for the disparity?
4. What is environmental racism (injustice)? How does environmental racism put some populations at higher risk for severe medical complications than others? (Vice article)
https://www.theatlantic.com/ideas/archive/2020/07/600-week-buys-freedom-fear/613972/
https://www.vox.com/2020/4/10/21207520/coronavirus-deaths-economy-layoffs-inequality-covid-pandemic
https://www.vice.com/en_us/article/pke94n/cancer-alley-has-some-of-the-highest-coronavirus-death-rates-in-the-country
https://www.theguardian.com/us-news/2020/apr/12/coronavirus-us-deep-south-poverty-race-perfect-storm
.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
1. Chapter 10
Data Interpretation Issues
Learning Objectives
• Distinguish between random and
systematic errors
• State and describe sources of bias
• Identify techniques to reduce bias at the
design and analysis phases of a study
• Define what is meant by the term
confounding and provide three examples
• Describe methods to control confounding
Validity of Study Designs
• The degree to which the inference drawn
from a study, is warranted when account it
2. taken of the study, methods, the
representativeness of the study sample,
and the nature of the population from
which it is drawn.
Validity of Study Designs
• Two components of validity:
– Internal validity
– External validity
Internal Validity
• A study is said to have internal validity
when there have been proper selection of
study groups and a lack of error in
measurement.
• Concerned with the appropriate
measurement of exposure, outcome, and
association between exposure and
3. disease.
External Validity
• External validity implies the ability to
generalize beyond a set of observations to
some universal statement.
• A study is externally valid, or
generalizable, if it allows unbiased
inferences regarding some other target
population beyond the subjects in the
study.
Sources of Error in
Epidemiologic Research
• Random errors
• Systematic errors (bias)
Random Errors
4. • Reflect fluctuations around a true value of
a parameter because of sampling
variability.
Factors That Contribute to
Random Error
• Poor precision
• Sampling error
• Variability in measurement
Poor Precision
• Occurs when the factor being measured is
not measured sharply.
• Analogous to aiming a rifle at a target that
is not in focus.
• Precision can be increased by increasing
sample size or the number of
measurements.
5. • Example: Bogalusa Heart Study
Sampling Error
• Arises when obtained sample values
(statistics) differ from the values
(parameters) of the parent population.
• Although there is no way to prevent a
non-representative sample from
occurring, increasing the sample size
can reduce the likelihood of its
happening.
Variability in Measurement
• The lack of agreement in results from
time to time reflects random error
inherent in the type of measurement
procedure employed.
6. Bias (Systematic Errors)
• “Deviation of results or inferences
from the truth, or processes leading to
such deviation. Any trend in the
collection, analysis, interpretation,
publication, or review of data that can
lead to conclusions that are
systematically different from the
truth.”
Factors That Contribute to
Systematic Errors
• Selection bias
• Information bias
• Confounding
Selection Bias
• Refers to distortions that result from procedures
used to select subjects and from factors that
influence participation in the study.
• Arises when the relation between exposure and
disease is different for those who participate and
those who theoretically would be eligible for study
but do not participate.
7. • Example: Respondents to the Iowa Women’s
Health Study were younger, weighed less, and were
more likely to live in rural, less affluent counties than
nonrespondents.
Information Bias
• Can be introduced as a result of
measurement error in assessment of
both exposure and disease.
• Types of information bias:
– Recall bias: better recall among cases
than among controls.
• Example: Family recall bias
Information Bias (cont’d)
– Interviewer/abstractor bias--occurs
when interviewers probe more
thoroughly for an exposure in a case
than in a control.
– Prevarication (lying) bias--occurs when
8. participants have ulterior motives for
answering a question and thus may
underestimate or exaggerate an
exposure.
Confounding
• The distortion of the estimate of the
effect of an exposure of interest
because it is mixed with the effect of
an extraneous factor.
• Occurs when the crude and
adjusted measures of effect are not
equal (difference of at least 10%).
• Can be controlled for in the data
analysis.
Criteria of Confounders
• To be a confounder, an extraneous
factor must satisfy the following
criteria:
– Be a risk factor for the disease.
9. – Be associated with the exposure.
– Not be an intermediate step in the
causal path between exposure and
disease.
Simpson’s Paradox as an
Example of Confounding
• Simpson’s paradox means that an
association in observed subgroups of a
population may be reversed in the entire
population.
• Illustrated by examining the data (% of
black and gray hats) first according to two
individual tables and then by combining all
the hats on a single table.
Simpson’s Paradox (cont’d)
• When the hats are on separate tables, a
greater proportion of black hats than gray
10. hats on each table fit.
– On table 1:
• 90% of black hats fit
• 85% of gray hats fit
– On table 2:
• 15% of black hats fit
• 10% of gray hats fit
Simpson’s Paradox (cont’d)
Simpson’s Paradox (cont’d)
• When the man returns the next day
and all of the hats are on one table:
– 60% of gray hats fit (18 of 30)
– 40% of black hats fit (12 of 30)
Note that combining all of the hats on
one table is analogous to
confounding.
11. Examples of Confounding
• Air pollution and bronchitis are positively
associated. Both are influenced by
crowding, a confounding variable.
• The association between high altitude and
lower heart disease mortality also may be
linked to the ethnic composition of the
people in these regions.
Techniques to Reduce
Selection Bias
• Develop an explicit (objective) case
definition.
• Enroll all cases in a defined time and
region.
• Strive for high participation rates.
• Take precautions to ensure
12. representativeness.
Reducing Selection Bias Among
Cases
• Ensure that all medical facilities are thoroughly
canvassed.
• Develop an effective system for case
ascertainment.
• Consider whether all cases require medical
attention; consider possible strategies to
identify where else the cases might be
ascertained.
Reducing Selection Bias
Among Controls
• Compare the prevalence of the exposure
with other sources to evaluate credibility.
• Attempt to draw controls from a variety of
13. sources.
Techniques to Reduce
Information Bias
• Use memory aids; validate exposures.
• Blind interviewers as to subjects’ study status.
• Provide standardized training sessions and
protocols.
• Use standardized data collection forms.
• Blind participants as to study goals and
classification status.
• Try to ensure that questions are clearly
understood through careful wording and
pretesting.
Methods to Control
Confounding
• Prevention strategies--attempt to control confounding
through the study design itself.
• Three types of prevention strategies:
– Randomization
14. – Restriction
– Matching
• Two types of analysis strategies:
– Stratification
– Multivariate techniques
Randomization
• Attempts to ensure equal distributions of the
confounding variable in each exposure
category.
• Advantages:
– Convenient, inexpensive; permits straightforward
data analysis.
• Disadvantages:
– Need control over the exposure and the ability to
assign subjects to study groups.
– Need large sample sizes.
Restriction
• May prohibit variation of the confounder in the
15. study groups.
– For example, restricting participants to a
narrow age category can eliminate age as a
confounder.
• Provides complete control of known
confounders.
• Unlike randomization, cannot control for
unknown confounders.
Matching
• Matches subjects in the study groups according
to the value of the suspected or known
confounding variable to ensure equal
distributions.
• Frequency matching--the number of cases with
particular match characteristics is tabulated.
• Individual matching--the pairing of one or more
controls to each case based on similarity in sex,
race, or other variables.
Matching (cont’d)
• Advantages:
16. – Fewer subjects are required than in
unmatched studies of the same hypothesis.
– May enhance the validity of a follow-up study.
• Disadvantages:
– Costly because extensive searching and
recordkeeping are required to find matches.
Two Analysis Strategies to
Control Confounding
• Stratification--analyses performed to evaluate
the effect of an exposure within strata (levels) of
the confounder.
• Multivariate techniques--use computers to
construct mathematical models that describe
simultaneously the influence of exposure and
other factors that may be confounding the
effect.
17. Advantages of Stratification
• Performing analyses within strata is a
direct and logical strategy.
• Minimum assumptions must be
satisfied for the analysis to be
appropriate.
• The computational procedure is
straightforward.
Disadvantages of Stratification
• Small numbers of observations in some
strata.
• A variety of ways to form strata with
continuous variables.
• Difficulty in interpretation when several
confounding factors must be evaluated.
• Categorization results in loss of
information.
18. Multivariate Techniques
• Advantages:
– Continuous variables do not need to be
converted to categorical variables.
– Allow for simultaneous control of several
exposure variables in a single analysis.
• Disadvantages:
– Potential for misuse.
Publication Bias
• Occurs because of the influence of
study results on the chance of
publication.
– Studies with positive results are more
likely to be published than studies with
negative results.
62. • Apply Hill’s criteria for evaluation of
epidemiologic associations
Effect Measure
• A quantity that measures the effect of
a factor on the frequency or risk of a
health outcome
Three Effect Measures
• Attributable Fractions
– Measure the fraction of cases due to a
factor.
• Risk and Rate Differences
– Measure the amount a factor adds to
the risk or rate of a disease.
• Risk and Rate Ratio
– Measure the amount by which a factor
multiplies the risk or rate of disease.
63. Absolute vs. Relative Effects
• Absolute
– Attributable risk is also known as a rate
difference or risk difference.
– Population risk difference
• Relative
– Relative risk
– Etiologic fraction
– Population etiologic fraction
Risk Difference (Attributable
Risk)
• Risk difference--the difference
between the incidence rate of
disease in the exposed group (Ie)
and the incidence rate of disease in
the nonexposed group (Ine).
64. • Risk difference = Ie - Ine
Calculation of Risk Difference
• For women younger than age 75, the
incidence (Ie) of hip fractures per 100,000
person-days was highest in the winter
(0.41), and the incidence (Ine) was lowest
in the summer (0.29). The risk difference
between the two seasons (Ie - Ine) was 0.41
- 0.29, or 0.12 per 100,000 person-days.
Population Risk Difference
• Measures the benefit to the
population derived by modifying a
risk factor.
Etiologic Fraction
• Defined as the proportion of the rate
in the exposed group that is due to
the exposure.
• Also termed attributable proportion or
65. attributable fraction.
Population Etiologic Fraction
• Provides an indication of the effect of
removing a particular exposure on the
burden of disease in the population.
• Also termed attributable fraction in the
population.
Statistical Measures of Effect
• Significance tests
• The P value
• Confidence interval
Null Hypothesis
• Underlying all statistical tests is a null
hypothesis, which states that there is
no difference among the groups being
66. compared.
• The parameters may consist of the
prevalence or incidence of disease in
the population.
Significance Tests
• Used to decide whether to reject or fail to reject
a null hypothesis.
• Involves computation of a test statistic, which is
compared with a critical value obtained from
statistical tables.
• The critical value is set by the significance level
of the test.
• The significance level is the chance of rejecting
the null hypothesis when, in fact, it is true.
The P Value
• Indicates the probability that the
67. findings observed could have
occurred by chance alone.
• However, a nonsignificant difference
is not necessarily attributable to
chance alone.
The P Value (cont’d)
• Possible meaning of nonsignificant
differences: For studies with a small
sample size the sampling error may
be large, which can lead to a
nonsignificant test even if the
observed difference is caused by a
real effect.
Confidence Interval (CI)
• A computed interval of values that, with a
given probability, contains the true value
68. of the population parameter.
• The degree of confidence is usually stated
as a percentage; commonly the 95% CI is
used.
• Influenced by variability of the data and
sample size.
Clinical vs. Statistical
Significance
• While small differences in disease frequency or
low magnitudes of relative risk (RR) may be
significant, they may have no clinical
significance.
• Conversely, with small sample sizes, large
differences or measures of effect may be
clinically important and worthy of additional
study.
69. Statistical Power
• The ability of a study to demonstrate
an association if one exists.
• Determined by:
– Frequency of the condition under study.
– Magnitude of the effect.
– Study design.
– Sample size.
Evaluating Epidemiologic
Associations
• Five key questions to be asked:
– Could the association have been observed by
chance?
• Determined through the use of statistical tests.
– Could the association be due to bias?
• Bias refers to systematic errors, i.e., how samples
were selected or how data was analyzed.
70. Evaluating Epidemiologic
Associations (cont’d)
• Could other confounding variables have
accounted for the observed relationship?
• To whom does this association apply?
– Representativeness of sample
– Participation rates
• Does the association represent a cause-
and-effect relationship?
– Considers criteria of causality.
Types of Associations between
Factors and Outcomes
• Not statistically associated
(independent)
• Statistically associated
71. Statistical Association
• When a factor and outcome are
statistically associated, the
relationship can be:
– Non-causal
– Causal
• Indirect
• Direct
Multiple Causality
• Also referred to as multifactorial
etiology.
• “…requirement that more than one
factor be present for disease to
develop…”
Models of Multiple Causality
• Epidemiologic triangle
• Web of causation, e.g., in avian
72. influenza
• Wheel model, e.g., childhood lead
poisoning
• Pie model, e.g., lung cancer
Chapter 8
Experimental Study
Designs
Learning Objectives (abridged)
• State how study designs compare with respect
to validity of causal inference
• Distinguish between a controlled experiment
and a quasi-experiment
• Describe the scope of intervention studies
• Define the term controlled clinical trials and give
examples
73. • Explain the phases in testing a new drug or
vaccine
Learning Objectives (abridged)
• Discuss blinding and crossover in
clinical trials.
• Define what is meant by community
trials.
• Discuss ethical aspects of
experimentation with human subjects.
True Experimental Studies
• Most convincing for conferring
evidence of associations between
risk factors and outcomes
• Manipulation of study factor and
randomization of subjects
• An example is a randomized clinical
74. trial.
Women’s Health Initiative
• Hormone Replacement Therapy (HRT)
– Epidemiologic studies had shown that HRT
use had significant benefits against coronary
heart disease.
– Clinical trials had failed to demonstrate any
benefit.
– Large body of epidemiologic research had
observed that women who took HRT had
elevated risks of breast cancer.
Women’s Health Initiative
• Hormone Replacement Therapy (HRT)
– To resolve the question of risks versus benefits of
HRT, a clinical trial was conducted.
– Demonstrated that:
75. • the epidemiologic findings on cancer were
generally accurate
• the benefits on cardiovascular disease had
been overestimated
– Results
• Use of HRT decreased 40%-80% after the trial
was stopped
Quasi-
Experiment/Community Trial
• Ranked immediately below
controlled experiments in rigor
• Investigator is unable to randomly
allocate subjects to the conditions.
• There may be contamination across
the conditions of the study.
76. Intervention Studies
• An investigation involving intentional
change in some aspect of the status
of subjects
• Used to test efficacy of preventive or
therapeutic measures
• Manipulation of the study factor and
randomization of study subjects
Intervention Studies
• Two categories:
– Clinical trials (focus on the individual)
– Community trial or community
intervention (focus on the group or
community.
• NOTE: Controlled clinical trials may
be conducted both at the individual
and community levels.
77. Clinical Trials: Definition
• A research activity that involves the
administration of a test regimen to
humans to evaluate its efficacy and safety
• Wide variation in usage:
– The first use of the term was for studies in
humans without any control treatment
– Now denotes a rigorously designed and
executed experiment involving RANDOM
ALLOCATION of test and control treatments
Characteristics of Clinical Trials
• Carefully designed and rigidly enforced
protocol
• Tightly controlled in terms of eligibility,
delivery of the intervention, and monitoring
out outcomes
78. • Duration ranges from days to years
• Participation is generally restricted to a
highly selected group of individuals.
Characteristics of Clinical Trials
• Once subjects agree to participate,
they are randomly assigned to one of
the study groups, e.g., intervention or
control (placebo)
History of Clinical Trials
• In 1537, Ambroise Paré applied
experimental treatment for battlefield
wounds.
• East India Shipping Company (1600)
found that lemon juice protected against
scurvy.
• James Lind (1747) used the concurrently
79. treated control group method.
History of Clinical Trials
• Edward Jenner’s efforts to develop a
smallpox vaccine in the late 18th century
• Most recent historical developments
include the use of multicenter trials.
– Instrumental in the development of
treatments for infectious diseases and
recently in chronic diseases that are of
noninfectious origin
Prophylactic and Therapeutic
Trials
• A prophylactic trial evaluates the
effectiveness of a substance that is used
to prevent disease; it can also involve a
prevention program.
80. • A therapeutic trial involves the study of
curative drugs or a new surgical procedure
to improve the patient’s health.
Outcomes of Clinical Trials
• Referred to as clinical end points
• May include rates of disease, death, or
recovery
• The outcome of interest is measured in
the intervention and control arms of the
trial to evaluate efficacy--these must be
measured in a comparable manner.
Examples of Clinical Trials
• Medical Research Council Vitamin
Study—studied role of folic acid in
preventing neural tube defects.
• South Bronx, NY, STD Program—
81. evaluated effectiveness of education
efforts to prevent spread of sexually
transmitted diseases (STDs).
Blinding (Masking)
• To maintain the integrity of a study and
reduce the potential for bias, the
investigator may utilize one of two popular
approaches:
–Single-blind design: subject unaware of
group assignment
–Double-blind design: Neither subject nor
experimenter is aware of group
assignment
Phases of Clinical Trials
• Before a vaccine, drug, or treatment can
be licensed for general use, it must go
82. through several stages of development.
• This lengthy process requires balance to:
– protect the public from a potentially
deleterious vaccine
– satisfy the urgent needs for new vaccines
Stages in the Development of A
Vaccination Program
• Pre-licensing evaluation of vaccine
– Phase I trials: Safety of adult volunteers
– Phase II trials: Immunogenicity and reactogenicity in
the target population.
– Phase III trials: protective efficacy
• Post-licensing evaluation
– Safety and efficacy of vaccine
– Disease surveillance
– Serologic surveillance
– Measurement of vaccine coverage
83. Phase IV Trials
• There can be more than three phases in a
clinical trial.
• Phase IV trials involve post-marketing
research to gather more information about
risks and benefits of a drug.
Randomization
• Method of choice for assigning subjects to
the treatment or control conditions of a
clinical trial.
• Non-random assignment may cause
mixing of the effects of the intervention
with differences (e.g., demographic)
among the participants of the trial.
Crossover Designs
• Any change of treatment for a patient in a
clinical trial involving a switch of study
84. treatments
• In planned crossovers a protocol is
developed in advance, and the patient
may serve as his or her own control.
• Unplanned crossovers exist for various
reasons, such as patient’s request to
change treatment.
Ethical Aspects of Human
Experimentation
• Benefits must outweigh risks.
• Ethical issues:
– Informed consent
– Withholding treatment known to be effective
– Protective the interests of the individual
patient
– Monitoring for side effects
– Deciding when to withdraw a patient
Reporting the Results of
85. Clinical Trials
• The CONSORT Statement is a
protocol that guides the reporting of
randomized trials by providing a 22-
item checklist and a flowchart.
Summary of Clinical Trials
• Strengths:
– Provide the greatest control over:
• the amount of exposure
• the timing and frequency of exposure
• the period of observation
– Ability to randomize reduces the likelihood
that groups will differ significantly.
Summary of Clinical Trials
(cont’d)
• Limitations:
86. – Artificial setting
– Limited scope of potential impact
– Adherence to protocol is difficult to
enforce
– Ethical dilemmas
Community Trials
• Community intervention trials determine the
potential benefit of new policies and programs
• Intervention: Any program or other planned
effort designed to produce changes in a target
population
• Community refers to a defined unit, e.g., a
county, state, or school district
Community Trials (cont’d)
• Start by determining eligible communities and
their willingness to participate
87. • Collect baseline measures of the problem to be
addressed in the intervention and control
communities
• Use a variety of measures, e.g., disease rates,
knowledge, attitudes, and practices
Community Trials (cont’d)
• Communities are randomized and followed over
time
• Outcomes of interest are measured
Examples of Community Trials
• North Karelia Project
• Minnesota Heart Health Program
• Stanford Five-City Project
• Pawtucket Heart Health Program
• Community Intervention Trial for Smoking
Cessation (COMMIT)
88. • Project Respect
Summary of Community Trials:
Advantages
• They represent the only way to estimate
directly the impact of change in behavior
or modifiable exposure on the incidence of
disease.
Summary of Community Trials:
Disadvantages
• They are inferior to clinical trials with respect to
ability to control entrance into study, delivery of
the intervention, and monitoring of outcomes.
• Fewer study units are capable of being
randomized, which affects comparability.
• They are affected by population dynamics,
secular trends, and nonintervention influences.
Four Stages of Evaluation
89. • Formative: Will all plans and procedures
work as conceived?
• Process: Is the program serving the
target group as planned?
• Impact: Has the program produced any
changes among the target group?
• Outcome: Did the program accomplish
its ultimate goal?
Overview of Quasi-Experimental
Study Designs
Type of Study Design Group(s) Pretest Intervention Posttest
Posttest only Intervention O X X
(has only one group)
Pretest/Posttest Intervention X X X
(has only one group)
Pretest/Posttest/Control Intervention X X X
(has two groups) Control X O X
90. Solomon Four-Group Intervention 1 X X X
(has four groups) Intervention 2 O X X
Control 1 X O X
Control 2 O O X
Note. O = not used; X = used.
Quasi-Experimental Designs
• Posttest only--observations are made only
after the program has been delivered.
• Pretest/Posttest--baseline and follow-up
observations are made.
• Pretest/Postest/Control--observations are
made in both intervention and control
groups before and after the program.
Quasi-Experimental Designs
(cont’d)
• Solomon Four-Group assignment:
91. – Used to overcome the Hawthorne Effect.
– Uses four equivalent groups, two
intervention and two control:
• Two are observed before and after intervention.
• Two are observed only after intervention.