LO: Formulate answerable research questions to address clinical and population health problems

PICO (T)

LO: Describe and compare the design, strengths, limitations and appropriate use of common quantitative research methodologies (incl: RCTs, quasi-experimental studies, cohort studies, case-control studies, systematic reviews and meta-analyses) and explain how these relate to the hierarchy of evidence

Cross-sectional study

• Measurement of exposed and outcome at same time in a population at a single point in time - snapshot
• Estimating prevalence/behaviours
• Health surveys
• Generating hypotheses
• Describing population health

Strengths

• Quick and low cost
• Practical with large numbers
• Practical to consider multiple outcomes

Limitations

• Evidence only supports association (not causation)
• Hard to robustly establish cause and effect, reverse causality

Cohort studies

• Longitudinal study design
• Observe a group of people who share a common characteristic or experience
• Identify and analyse relationships between exposures and outcomes
• Participants do not have outcome when recruited

flowchart LR
	Time -----> F[ ]:::hidden
	Y[Direction of enquiry] -----> G[ ]:::hidden
	Population --> A[People without the disease]
	A --> Exposed
	A --> B[Not exposed]
	Exposed --> Disease
	Exposed --> C[No disease]
	B --> D[Disease]
	B --> E[No disease]
	
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1. Prospective cohort study - recruit exposed group and follow up
2. Retrospective cohort study - identify exposed people from the past and measure outcomes

Strengths

• Establishes temporal sequence
• Shows exposure/cause happened before effect/outcome - stronger causality than cross-sectional
• Address multiple research questions within same population
• Good for rare exposures

Limitations

• Expensive
• Lost to follow up bias
• Retrospective data collection quality can be lower and limited
• Evidence for causality weakened by confounders
• Not good for rare outcomes

Case-control

• Select based on an outcome
• Look back in time to see who was exposed
• Compare individuals with outcome to without outcome to identify factors associated with outcome

flowchart LR
	F[ ]:::hidden <----- Time
	Y[Direction of enquiry] -----> G[ ]:::hidden
	Population --> A[People without the disease]
	A --> Exposed
	A --> B[Not exposed]
	Exposed --> Disease
	Exposed --> C[No disease]
	B --> D[Disease]
	B --> E[No disease]
	
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Selecting controls

• Draw from same underlying population
• Inclusion/exclusion match cases
• Matching
• Ratio of controls:cases

Strengths

• Good for rare outcomes
• Uses smaller numbers - quick and cheaper
• Explore multiple exposures
• Good for outbreak investigations
• Hypothesis generation

Limitations

• Bias - recall, selection
• Robustness of evidence for causality heavily impacted by choice of controls
• Confounding

Nested case-control studies

• Select cases and matched with controls from an established cohort study
• Compare exposures
• Less selection bias
• Clear on causality
• Less recall bias
• Cheaper

Ecological studies

• Compare populations rather than individuals
• Aggregate data - not individual data
• Measurement of exposure and outcome not necessarily in same person
• Develop hypotheses and research questions
• Low cost and convenient
• Cannot establish causality
• Ecological fallac

Case studies and case series

• Study - single
• Series - several
  

LO: Interpret measures of disease frequency, association and effect, including prevalence, incidence, risk ratios, odds ratios, number needed to treat or harm

Incidence

• Number of new cases of a disease per unit population at risk per unit time
• Incidence rate = $\frac{\text{New cases}}{\text{Total person-time at risk}}$

Prevalence

• Proportion of individuals with a given disease at a given point in time
• Point prevalence - fixed point
• Period prevalence - over a period, existing and incident cases

Effect size

Continuous outcomes

• Categorical exposure - mean difference
• Difference in the mean outcome between exposure groups
• Continuous exposure - regression coefficient ($\beta$)
• Change in outcome associated with a one-unit increase in exposure

Binary outcomes

Relative risk

• Relative risk = $\frac{\text{Risk in exposed}}{\text{Risk in unexposed}}$

Absolute risk

• Risk difference = Risk in exposed - Risk in unexposed

Numbers needed to treat

• NNT = $\frac{1}{\text{Risk difference}}$

Probability (risk) and odds

• Probability = $\frac{\text{Chance of event}}{\text{Total outcomes}}$
• Odds = $\frac{\text{Chance of event}}{\text{Chance of event not happening}}$

Odds ratio

• Odds ratio = $\frac{\text{Odds in exposed}}{\text{Odds in unexposed}}$

Risk ratios v Odds ratios

• Odds ratio for case-control and cohort
• Relative risk for only cohort
• Case-control studies cannot calculative relative risk as true risk of disease is unknown in both populations