Week 4 Lecture - Causation and Correlation, RCTs to upload

Week 4 Overview

Outline and Announcements

• Topics:

  • Correlation and Causation

  • Why we do RCTs

• Announcements:

  • HW2 Assigned (Due Wednesday at 11:59 PM)

• Optional Reading:

  • Intuitive Biostatistics Chapter 32

Correlation

• Definition: Indicates an association between two variables.

• Types of variables:

  • Categorical/Binary

    • Example: Men are more likely to have kidney stones.

  • One binary and one continuous

    • Example: 11th graders score higher in statistics than 10th graders.

  • Both continuous

    • Example: Older adults have more compliant achilles tendons.

• Knowing one variable provides information on the likely values of another, but correlation does not imply causation.

Causation

Key Concepts

• Definition: Causation means changes in one variable directly lead to changes in another.

• Temporal Precedence: The cause must precede the effect.

• Causation is:

  • Typically probabilistic in nature.

  • Example: Relationship between height (y) and weight (x).

• Importance of Causation: Provides explanations of how systems work and guides interventions.

Causation Example

• Study by Straus et al (1997):

  • Spanking and antisocial behavior correlation observed over 2 years.

  • Observational studies don't prove causation due to lack of control over grouping.

  • Example: Link between smoking and lung cancer shows correlation rather than direct causation.

Understanding Correlation vs. Causation

• Mistake in science: inferring causation from mere correlation.

• Only true experiments (RCTs) can consolidate causation proof.

• Examples:

  • Rehabilitation experiments must consider confounding factors.

  • Treatment comparisons need careful structuring to avoid bias.

Confounding Variables

• Example: Treatment choice may be influenced by stone size in kidney stone treatment.

• Common confounding factors include:

  • Baseline condition severity

  • Socioeconomic status

  • Comorbidities

• Addressing confounds is crucial for establishing causal relationships.

Randomized Controlled Trials (RCTs)

Components

• Intervention: One variable must be manipulated.

• Randomization: Participants are randomly assigned to groups to ensure comparability.

• Controlling: Maintaining a control group helps mitigate biases and ensures valid comparisons.

Randomization Techniques

• Importance of Randomization: Eliminates selection bias and balances confounders.

• Block Randomization: Ensures equal representation across treatment groups based on identified confounders.

Challenges in Using RCTs

• Situations where RCTs are not feasible:

  • Ethical considerations (e.g., smoking)

  • Long-term interventions (e.g., diet)

  • Rare disease incidence studies

Causal Directed Acyclic Graphs (DAGs)

• Definition: Graphical representation of causal assumptions.

• Uses: Helps conceptualize relationships between variables and potential confounders.

• Example Pathways:

  • Direct and backdoor pathways affect the perceived causality.

Summary of Key Learnings

• Causation vs. correlation: critical differentiation.

• RCTs provide robust evidence for causality through intervention and randomization.

• Causal DAGs are useful tools for representing and understanding research relationships.

Hill's Criteria for Causation

• Criteria for evaluating causation in observational studies include:

  • Strength, Consistency, Specificity, Temporality, Biological Gradient, Plausibility, Coherence, Experiment, and Analogy.