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.