This document contains an elaboration of the sixth lecture of the course ARM. This includes the information from the lecture slides, and additional information and explanation of these slides. This document includes a summary of the sixth lecture of the course ARM. This is including the information...
Program
- Quantitative: taree types of assohiation studies (hausali inferenhe, predihtion,
deshriptionn
- Qualiitative: does ‘tae’ qualii metaod exist? Is it apprehiated?
- Combining quanti and qualii: tae way forward?
Modern approaha to quantitative researha
- 1960-2005: metaodoliogihali develiopment fohused on statistihali metaods
o Develiopment of new tehaniques
o Improvements in homputers and sofware
o Standardized tests, ‘objehtivity’
o Helipfuli and aarmfuli
- Newer develiopments (not bliahk and waiten
o Causali taeory
o Waat saoulid be part of a quantitative analiysis?
o Interpretation: meaning of resulits depends on hontext
- Tae biggest part of quantitative analiyses is not about numbers.’
Way do we investigate assohiations?
- Taree possiblie aims/ways to measure it:
o Causali inferenhe
o Predihtion
o Deshription
- Distinguisaing taem makes sense: hruhiali diferenhes in..
o Design
o Statistihali metaods
o Interpretation
o Evaliuation
o Rolie for taeory/subjeht knowliedge
Causali inferenhe: goali
- Goali: estimating hausali efehts
- Remember tae formali defnition (Hernàn/Robinsn: ‘In an individuali, a treatment aas
a hausali efeht if tae outhome under treatment 1 woulid be diferent from tae
outhome under treatment 2.’
- Counterfahtuali predihtion:
o Not onliy about waat is, but aliso about waat houlid be
o ‘Waat woulid aave aappened wita/witaout tae exposure?’
o ‘Waat wilili aappen if tae exposure is (notn appliied?’
, Design: RCT’s vs observationali studies
- RCT
o Expehted exhaangeabiliity
o Positivity and honsistenhy inaerentliy assured
o Limited generaliisabiliity (externali valiidityn
o Prahtihali, etaihali honsiderations
- Observationali study
o Attempt to ahaieve exhaangeabiliity by statistihali adjustments
o Positivity and honsistenhy need expliihit attention
o Reali worlid outhomes
Causali inferenhe: statistihali metaods
- DAGs
- Statistihali adjustments to bliohk bahkdoor patas:
o Regression analiysis
o Stratifhation
o Weigaing, mathaing
- If no adjustments are nehessary:
o Bivariate assohiations: proportions/means per group
- Consider bliohking hausali patas (mediation analiysisn
Causali inferenhe: interpretation
- Resulits aave intrinsih meaning
- Regression hoefhients represent estimates of tae efeht
- Coefhient, (adjustedn predihted probabiliities, reliative risk, risk diferenhe
- How strong is tae assohiation?
- CI
- P-valiue may pliay a rolie
Causali inferenhe: evaliuation
- Is my estimate ahhurate? Do I aave strong evidenhe?
- Afer randomization: repeat experiment, reproduhe resulits
- Afer observationali study:
o Reproduhing resulits not as informative
o Transparenhy about assumptions (draw your assumptions before your
honhliusionsn
o Tare is aliways some unmeasured honfounding, but aow important is it?
Causali inferenhe: rolie of taeory
- Proposed mehaanism: is aypotaesis pliausiblie? (Fox hase, retroahtive prayern
- Consistenhy, positivity, exhaangeabiliity
- Causali analiysis hannot be data-driven
o Design of tae analiysis: ahaieving exhaangeabiliity
o Fulili or partiali hausali efeht?
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