Summary

Summary of all subject matter including mandatory articles Advances research methods

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Course
Advanced Research Methods

Institution
Erasmus Universiteit Rotterdam (EUR)

This document contains a summary of all the course material from the lectures as well as a summary of the mandatory articles. This course is taught in the Master Health Care Management at Erasmus University Rotterdam.

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Uploaded on October 11, 2023
Number of pages 62
Written in 2023/2024
Type Summary

quantitative
p value
research methods
design
identifiability conditions

Institution
Erasmus Universiteit Rotterdam (EUR)
Education
Master Healthcare Management
Course
Advanced Research Methods

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HC1: Advanced research methods

What prior knowledge do we expect?

Qualitative concepts and methods
- Interviews, observations, document
- Validity and reliability

Introduction to causal inference:

In causal inference
- We are not interested in the outcome per se.
- We are interested in the role of treatment in achieving this outcome (without true
match minerals powder, would there have been less skin imperfections).
- Conclusion
 We do not have the information.
 No causal claim can be made based on the L’Oréal study.

Causal effect
- Formal definition by Hernan and Robins
- In an individual, a treatment has a causal effect if the outcome under treatment 1
would be different from the outcome under treatment 2.
- To assess this, we need information on:

Potential outcome approach

Y= outcome
A= treatment
1= Yes (received treatment)
0= No treatment
I= Individual

=1 (improvement with treatment)

=0 (no improvement without treatment)

,Treatment effect for K: Delta Yk = 1-0=1 (positive effect)
Average treatment effect= average of Delta Yi

Not all potential outcomes are observed.
- Counterfactual outcome: potential outcome that is not observed because the subject
did not experience the treatment.
- Potential outcome Ya=1 is factual for some subjects, and counterfactual for others.

Fundamental problem
- Individual causal effect cannot be observed.
 Expect under extremely strong assumptions.
- Average causal effect cannot be determined based on individual estimates.
 Causal inference as a missing data problem.

Identifiability conditions:
Average causal effect can be determined if, and only if, three identifiability conditions are
met.
1. Positivity
2. Consistency
3. Exchangeability
- If all conditions are met the association between exposure and outcome is an
unbiased estimate of a causal effect.

Positivity
- This condition means that:
 Everyone must have a positive probability of being assigned to each of the
treatment arms.
- Cigarette lighter example: people with and people without one plastic cigarette
lighter.
- In comparison:
 100% was assigned to true mineral match.
 0% to comparator

Consistency
- The treatment has to be well-defined.
- Does water kill: Wat kind of water, how much etc?

Exchangeability
- The individual assigned to the different treatment arms must be similar.
- It does not matter who gets treatment A and who gets B.
- People with a lighter could also not have had a lighter.

,Meeting the exchangeability condition
Four ways are possible:
1. Randomized controlled trial (RCT)
 Individuals are randomly assigned to one of each treatment arms.
 Differences between individuals in the different treatment arms are cancelled out
on the sample level.
 Differences are independent from the treatment and outcome.
 Differences are random, not systematic.
Golden standard, because typically all identifiability conditions are met in RCTs.

2. Matching
 For each individual with characteristics x,y and z who gets treatment A. There is
an individual with characteristics x,y,z who gets treatment B.
 Statistical methods can be applied when perfect matching is not possible.

3. Stratification
 Randomly select individuals from different subsets of the larger population.
 Difficult to meet the positivity condition.
 Population -> strata -> random selection -> sample.

4. Adjustment
 Control for factors that influence the association between the treatment and
outcome in regression analysis.
 Individuals are assigned to all treatment arms within all levels of adjustment
factors.
 Can also be combined with an RCT, stratification, and matching.
 Complete and correct adjustment leads to exchangeability.

RCTs versus observational studies
- Golden standard however
 Limited external validity
 Ethical and practical considerations
- Observational studies
 Real world outcomes
 Availability of data
 Positivity and consistency need close attention.
 Internal validity threatened by lack of exchangeability.

People are often interested in causal effects, not just correlations
 What is the effect of nurses’ job satisfaction on the health of ICU patients

Correlation does not imply causation
- Correlation implies association.
 A statistical relationship between the treatment and outcome
 Knowing the value of one variable may provide information on the value of
another variable, but that does not mean that one caused the other.

,  Knowing that Zeus died after 5 days after a heart transplantation does not mean
the transplant caused Zeus’ death.
 Causation= difference between potential outcomes

Correlation does not imply causation.
- A statistical association equals the difference in potential outcomes if, and only if the
identifiability conditions are met.
- For this we need:
 Theory and subject knowledge
 Insight into the causal structure underlying the research question.
 To meet the positivity, consistency, and exchangeability conditions, and design the
study and analysis accordingly.

Design analysis
- Focus on adjustment in regression analysis.
 Complete and correct adjustment leads to exchangeability.
 But how do we know what to adjust for in the analysis.

Traditional selection strategies
- Correlation matrix: select variables with statistically significant association with the
outcome.
- Stepwise backward selection
 Insert all variables in regression models
 Remove the variable that is the least statistically significant
 Run regression, remove variable that is then the least.
 Or keep variable in regression model if removal leads to substantial change in the
effect estimate.
 Adjust for confounders, which are traditionally defined as being
 Associated with the exposure

Problems with traditional strategies
- These strategies are still applied
 These methods rely on the available data, rather than on theory/subject
knowledge
 Selected strategy may increase, rather than reduce bias
 Stepwise selection may result in false certainty
 These methods are increasingly considered as outdate

Cigarette lighter example in DAG terms

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