Back Module: SBI222

Design and Analysis of Epidemiological Studies

Module information

Module details

Title
Design and Analysis of Epidemiological Studies
Type
Specialist
Module code
SBI222
Credits
20
Requirement
Compulsory

Aim of this module

Descriptive epidemiology helps with hypothesis generation; analytical epidemiology allows the testing of hypotheses and making inference of the results to the population with the use of analytical methods. The overall aim of this module is to provide trainees with the knowledge and skills to design, analyse and interpret observational epidemiological studies used in the public health setting to inform understanding and effect change. The aim of this module is to enable trainees to develop and apply their skills in the design and analysis of epidemiological studies from identifying the public health problem; designing a study to address the problem, and leading the study through to the presentation of the results and their implications for population health, patient care and patient outcomes.

Work-based content

Competencies

# Learning outcome Competency Action
# 1 Learning outcome 1 Competency

Identify the public health problem and justify the study, considering any ethical issues that may be relevant in order to protect patients and the public.

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# 2 Learning outcome 1 Competency

Conduct a literature review.

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# 3 Learning outcome 1 Competency

Define a research question and study objectives.

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# 4 Learning outcome 1 Competency

Define study hypotheses and translate to statistical hypotheses.

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# 5 Learning outcome 1 Competency

Determine the study population and study design.

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# 6 Learning outcome 1 Competency

Describe sampling strategy and calculate sample size.

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# 7 Learning outcome 1 Competency

Develop an analysis plan, including dummy tables and document the justification for modifications.

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# 8 Learning outcome 1 Competency

Define the strategy for communication of results taking into account diversity in patients and populations.

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# 9 Learning outcome 1 Competency

Consider ethical issues that might be relevant and consult the National Research Ethics Service and apply and gain ethical approval if necessary.

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# 10 Learning outcome 2 Competency

Identify data needed to answer the study question and design an appropriate quality-assured data collection instrument.

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# 11 Learning outcome 3 Competency

Conduct data collection and entry.

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# 12 Learning outcome 3 Competency

Conduct data handling and data management.

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# 13 Learning outcome 3 Competency

Check the data for inconsistencies and missing data and prepare data set for analysis.

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# 14 Learning outcome 3 Competency

Generate summary variables as necessary.

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# 15 Learning outcome 4 Competency

Undertake a descriptive analysis of the data variables.

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# 16 Learning outcome 4 Competency

Conduct bivariate analysis.

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# 17 Learning outcome 4 Competency

Conduct univariate analysis.

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# 18 Learning outcome 4 Competency

Conduct stratified analysis to identify effect modification and confounding.

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# 19 Learning outcome 4 Competency

Conduct multivariable analysis if required to adjust for multiple confounders and effect modifiers.

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# 20 Learning outcome 4 Competency

Archive data and analysis code as required.

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# 21 Learning outcome 5 Competency

Interpret significant and non-significant findings.

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# 22 Learning outcome 5 Competency

Identify limitations and make a final conclusion about the study results.

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# 23 Learning outcome 6 Competency

Draft a report summarising the study findings and their impact on patients and the population.

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# 24 Learning outcome 6 Competency

Share report with appropriate colleagues for comment, collate comments and amend draft following feedback.

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# 25 Learning outcome 6 Competency

Share report for final sign-off and disseminate through appropriate communications channels.

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# 26 Learning outcome 6 Competency

Summarise findings for a lay audience.

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Assessments

Information:

This module has no work-based assessments.

Learning outcomes

  1. Write a study protocol for an epidemiological study and consider ethical issues if required.
  2. Develop an appropriate quality-assured data collection instrument.
  3. Collate and manage the data and prepare the data set for analysis.
  4. Analyse data according to the data analysis plan; archive data and analysis code as required.
  5. Interpret study results, including a critique of study limitations.
  6. Write a report presenting the results of the analysis and their implications for population health, patient care and patient outcomes.

Academic content (MSc in Clinical Science)

Important information

The academic parts of this module will be detailed and communicated to you by your university. Please contact them if you have questions regarding this module and its assessments. The module titles in your MSc may not be exactly identical to the work-based modules shown in the e-portfolio. Your modules will be aligned, however, to ensure that your academic and work-based learning are complimentary.

Learning outcomes

  1. Identify and state the key considerations in the planning and design of epidemiological studies.
  2. Describe the principles and critically evaluate the relative merits of different study designs.
  3. Summarise the key concepts and impact of sampling error, bias and confounding in epidemiological studies and apply strategies to address these.
  4. Select appropriate sampling methods and calculate sample size and power using appropriate software.
  5. Describe the key elements of questionnaire design.
  6. Design data collection instruments.
  7. Design data collection procedures that protect patients, the public and other stakeholders, in line with data protection requirements, e.g. Caldicott requirements.
  8. Perform data cleaning and management on a dummy data set.
  9. Perform data analysis on a dummy data set using statistical software, e.g. R.
  10. Investigate effect modification (interaction) and confounding using both stratified and statistical modelling methods.
  11. Judge the adequacy of evidence supporting causal links between exposure and disease.
  12. Describe the elements and applications of time series analysis.
  13. Describe the applications of infectious disease modelling.

Indicative content

  • Identify and state the key considerations in the planning and design of epidemiological studies
    • Ethics
    • Cost
    • Human resource
    • Time
    • The underpinning evidence base to:
      • determind what study needs doing (what is the right uqestion)
      • inform how it migh tbest be conducted
    • The involvement of patient and the public
  • Describe the principles and critically evaluate the relative merits of different study designs
    • Cross-sectional studies
      • Prevalence and prevalence ratios
    • Prospective and retrospective cohort studies
      • Attack rates, risk ratios and rate ratios
      • Attributable fraction
    • Case-control studies
      • Odds and odds ratios
    • Case-case studies
    • Case-crossover studies
    • Case and control selection

 

  • Summarise the key concepts and impact of sampling error, bias and confounding in epidemiological studies and apply strategies to address these
    • Information bias
    • Selection bias
    • Prevention of bias in data collection
    • Correction of bias in data analysis
  • Select appropriate sampling methods and calculate sample size and power using appropriate software
    • Non-probability sampling: convenience sampling, judgement sampling, quota sampling and snowball sampling
    • Probability sampling: simple random, systemic, stratified, multi-stage, cluster
    • Selection of participants
      • Cohort
      • Cases selection
      • Control selection
      • Sampling techniques
    • Impact of response to the study
    • Sample size calculation
      • Open Epi, R
      • Methods for estimating the desired value of the measure of effect
      • Power, significance
  • Describe the key elements of questionnaire design
    • Information required
    • Define target respondents
    • Choose the method(s) of reaching target respondents
    • Question content: demographic information, establish rapport, information required for the study
    • Question wording: closed, open-ended and open response-option questions
    • Meaningful order and format
    • Length of the questionnaire
    • Layout
    • Piloting
  • Design data collection instruments
    • Web-based, e.g. SelectSurvey
    • EpiData Entry
    • Evolving technologies, e.g. social media, wearable technologies
  • Design data collection procedures which protect patients and the public, in line with data protection requirements e.g. Caldicott requirements
    • Questionnaire: paper or electronic, self-administered versus interviewer, mail v email v web
    • Confidentiality
    • Secure data storage
    • Data entry: double entry, data entry checks
  • Perform data cleaning and management on a dummy data set using statistical software, e.g. R
    • Software commands and syntax
    • Reading data
    • Data types
    • Generating and recoding variables
    • Sorting data
    • Saving the data
    • Automate analysis, e.g. by generating a ‘.R’ file
    • Missing data
      • Types of missing data
      • Missing-data mechanism
      • Imputation and sensitivity analyses
  • Perform data analysis on a dummy dataset using statistical software. e.g. R
    • Measurement error
      • Description
      • Monitoring measurement error
      • Measurement error in the outcome variable
      • Impact of measurement error
  • Compare the outcome of two or more groups using appropriate statistical techniques
    • Tests for comparing means
    • Tests for comparing proportions
    • Non-parametric methods
    • Stratified analysis
    • Regression models
    • Survival analysis
  • Describe the association between variables
    • Appropriate use of graphical techniques for displaying and describing univariate and multivariate data
    • Meaning of correlation and regression analysis
    • Meaning of scatter plots
    • Non-independent measurements
    • Clustered data: description, problems with analyses, methods of analysis including generalised estimating equations and multilevel models and examination of contextual effects
  • Investigate effect modification (interaction) and confounding using both stratified and statistical modelling methods
    • Causal diagrams
    • Stratified analysis
    • Regression methods for case-control studies: unconditional and conditional logistic regression
    • Regression methods for cohort studies and survival analysis: stratifying on time, poisson regression, cox regression
    • Non-linear relationships
  • Judge the adequacy of evidence supporting causal links between exposure and disease
    • Bradford-Hill criteria
  • Describe the elements and applications of time series analysis
    • Elements:
      • assessment of trend
      • periodicity
      • smoothing and differencing
      • outliers
    • Applications:
      • compact description of data
      • interpretation
      • forecasting
      • control
      • hypothesis testing
      • simulation
  • Describe the applications of infectious disease modelling
    • Describe a compartmental model
    • Describe course of outbreak (retrospective and real-time)
    • Predict impact of control strategies
    • Describe infectious disease dynamics (e.g. Ro)

Clinical experiences

Important information

Clinical experiential learning is the range of activities trainees may undertake in order to gain the experience and evidence to demonstrate their achievement of module competencies and assessments. The list is not definitive or mandatory, but training officers should ensure, as best training practice, that trainees gain as many of these clinical experiences as possible. They should be included in training plans, and once undertaken they should support the completion of module assessments and competencies within the e-portfolio.

Activities

  • Attend an ethics committee meeting as an observer and critically reflect on your learning from the experience.
  • Attend a research governance group meeting as an observer and critically reflect on your learning from the experience.
  • Visit an academic, clinical, or clinical trials unit, and discuss its role in public health research and the partnership with research volunteers.
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