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RESEARCH DESIGN AND
DATA COLLECTION
Instructor: Ahmed taha Abdelwahed
Course: Statistics
Information
Study/ trials
• definition
• types
 observational
 interventional
design
• aim
• Data collection
• Data analysis
• report
 Statistical tests
 Tables/figures
introduction
• What is the study question or aim? •
• What design is appropriate to answer the question(s)?
• What statistics are appropriate for the study?
Research questions
• criteria:
• Specific with respect to time/place/subjects/condition as
appropriate
• Answerable collected
• Novel such that the relevant data are available or able to be in
some sense so that the study either makes a contribution to
knowledge or extends existing knowledge
• Relevant to current medicine
Types of question
• Descriptive , e.g. incidence/prevalence; trends/patterns;
opinion/ knowledge; life history of disease
• Evaluative , e.g. effi cacy/safety of treatments or preventive
programmes; may be comparative
• Explanatory , e.g. causes of disease; mechanisms for observed
processes or actions or events
• Interventional vs observational
• Time-course : prospective; retrospective; cross-sectional
• Source of data : new data; routine data; patient notes; existing
data, e.g. secondary data analysis, meta-analysis
RCT
• def.: an intervention study in which subjects are randomly
allocated to treatment options.
• gold standard + pooled in a meta-analysis
• Parallel group / crossover trials
• Aim : superiority , equivalence -> sample size large enough to
detect the difference or at least to be in the limit of equivalence.
• Non-Inferiority (equivalence) should be one-sided significance
testing.
• ITT (intention to treat analysis): direct comparison between 2
groups.
Observational studies
Case-Control:
• Study investigate the cause or factor associated.
• Retrospective data collection and analysis.
• Limitations: choice of control group. / exposure to risk data
collection / (ascertainment bias)
Cohort study:
Prospective longitudinal.
Cross-sectional study (survey).
The Bradford-Hill criteria for causation
• Strength of association
• • Consistency in different studies, settings, etc.
• Specifi city of association of risk factor with a particular disease
• Temporal relationship – exposure precedes disease
• Dose–response relationship
• • Biological plausibility for causality
• Coherence – association is consistent with current knowledge
• Experimental evidence for causality
• Existence of analogous evidence between a similar exposure
and disease
outcomes
• 1ry outcome
• 2ry outcome
• Composite outcome
• Surrogate outcome
Sample size
• Sample size calculations determine the number of subjects
needed to
give a sufficiently narrow confidence interval.
MEAN-comparison:
Proportion-comparison:
Standard
deviation
Confidence
interval
Expected
proportion
Confidence
interval
Sample size
• K:multiplier that depends on the significance
level α(5%) and power 1-β(80-90%)
Confidence
interval
Standard
deviation
Variables
• Qualitative vs. Quantitative
• Discrete vs. Continuous
Tests used according to variable type
Data summary representation

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Statistics design

  • 1. RESEARCH DESIGN AND DATA COLLECTION Instructor: Ahmed taha Abdelwahed Course: Statistics
  • 2. Information Study/ trials • definition • types  observational  interventional design • aim • Data collection • Data analysis • report  Statistical tests  Tables/figures
  • 3. introduction • What is the study question or aim? • • What design is appropriate to answer the question(s)? • What statistics are appropriate for the study?
  • 4. Research questions • criteria: • Specific with respect to time/place/subjects/condition as appropriate • Answerable collected • Novel such that the relevant data are available or able to be in some sense so that the study either makes a contribution to knowledge or extends existing knowledge • Relevant to current medicine
  • 5. Types of question • Descriptive , e.g. incidence/prevalence; trends/patterns; opinion/ knowledge; life history of disease • Evaluative , e.g. effi cacy/safety of treatments or preventive programmes; may be comparative • Explanatory , e.g. causes of disease; mechanisms for observed processes or actions or events
  • 6. • Interventional vs observational • Time-course : prospective; retrospective; cross-sectional • Source of data : new data; routine data; patient notes; existing data, e.g. secondary data analysis, meta-analysis
  • 7. RCT • def.: an intervention study in which subjects are randomly allocated to treatment options. • gold standard + pooled in a meta-analysis • Parallel group / crossover trials • Aim : superiority , equivalence -> sample size large enough to detect the difference or at least to be in the limit of equivalence. • Non-Inferiority (equivalence) should be one-sided significance testing. • ITT (intention to treat analysis): direct comparison between 2 groups.
  • 8. Observational studies Case-Control: • Study investigate the cause or factor associated. • Retrospective data collection and analysis. • Limitations: choice of control group. / exposure to risk data collection / (ascertainment bias) Cohort study: Prospective longitudinal. Cross-sectional study (survey).
  • 9. The Bradford-Hill criteria for causation • Strength of association • • Consistency in different studies, settings, etc. • Specifi city of association of risk factor with a particular disease • Temporal relationship – exposure precedes disease • Dose–response relationship • • Biological plausibility for causality • Coherence – association is consistent with current knowledge • Experimental evidence for causality • Existence of analogous evidence between a similar exposure and disease
  • 10. outcomes • 1ry outcome • 2ry outcome • Composite outcome • Surrogate outcome
  • 11. Sample size • Sample size calculations determine the number of subjects needed to give a sufficiently narrow confidence interval. MEAN-comparison: Proportion-comparison: Standard deviation Confidence interval Expected proportion Confidence interval
  • 12. Sample size • K:multiplier that depends on the significance level α(5%) and power 1-β(80-90%) Confidence interval Standard deviation
  • 13. Variables • Qualitative vs. Quantitative • Discrete vs. Continuous
  • 14. Tests used according to variable type