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Selection bias

What If: Chapter 8

Elena Dudukina

2021-02-18

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Selection bias

Classic definition

  • The magnitude of the association is different for participants and non-participants

    Structural definition

  • Occurs when conditioning on the common child or its descendants of two variables

    Encompasses various biases

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8.1 The structure of selection bias

DAG:

  • A: exposure (folic acid supplements)
  • Y: outcome, binary (cardiac malformation)
  • C: common effect (death before birth) observed only among live-born children (C=0)
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Under the null

  • The biasing path A ➡️ [C] ⬅️ Y

  • The associational risk ratio does not equal causal risk ratio

    • Pr[Y=1|A=1,C=0]Pr[Y=1|A=0,C=0] is not Pr[Ya=1]Pr[Ya=0]

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Selection bias

DAG:

  • A: exposure (folic acid supplements)
  • Y: outcome, binary (cardiac malformation)
  • C: common effect (death before birth) observed only among live-born children (C=0)
  • S: parental grief (restricted to S=0)

Under null we would still see an association between A and Y due to the collider stratification bias

  • The biasing path A ➡️ [C] ⬅️ Y

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Selection bias

  • DAG 8.3:
    • A: anti-retroviral treatment
    • Y: 3-year risk of death
    • U: unmeasured, immunosuppression (U=1 are under greater risk of death)
    • C: censored (C=1)
    • L: unmeasured, immunosuppression symptoms

Biasing path:

  • A ➡️ [C] ⬅️ L ⬅️ U ➡️ Y

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Selection bias

  • DAG 8.4:
    • A: anti-retroviral treatment
    • Y: 3-year risk of death
    • U: unmeasured, immunosuppression (U=1 are under greater risk of death)
    • C: censored (C=1)
    • L: unmeasured, immunosuppression symptoms

Biasing path:

  • A ➡️ [L] ⬅️ U ➡️ Y

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Selection bias

  • DAG 8.5 (M-bias):
    • A: anti-retroviral treatment
    • Y: 3-year risk of death
    • U: unmeasured, immunosuppression (U=1 are under greater risk of death)
    • C: censored (C=1)
    • L: unmeasured, immunosuppression symptoms
    • W: unmeasured, lifestyle

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Selection bias

  • DAG 8.6 (M-bias):
    • A: anti-retroviral treatment
    • Y: 3-year risk of death
    • U: unmeasured, immunosuppression (U=1 are under greater risk of death)
    • C: censored (C=1)
    • L: unmeasured, immunosuppression symptoms
    • W: unmeasured, lifestyle

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8.2 Examples of selection bias

  • Differential loss to follow-up/informative censoring (8.3-8.6)
  • Missing data bias, non-response bias
    • Missing data on the outcome for any reason (8.3-8.6)
  • Healthy worker bias
    • U: unmeasured, underlying health
    • C: at work or not (C=0 being at work)
  • Self-selection bias, volunteer bias
  • Selection affected by treatment received before study entry

Selection bias can occur in any follow-up study (observational or RCT)

  • Those who stayed in the study (uncensored, C=0) are not exchangeable with those who did not stay in the study (censored, C=1)
    • Can only compute observational Pr[Y=1|A=1,C=0]Pr[Y=1|A=0,C=0], but not counterfactual Pr[YA=1,C=c]Pr[YA=0,C=c] under both levels of C={0, 1}
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8.3 Selection bias and confounding

Sources of lack of exchangeability:

  • Confounding
  • Selection bias

DAG 8.7:

  • A: physical activity
  • Y: heart disease
  • L: family SES
  • C: becoming a firefighter (restricted to C=1)
  • U: unmeasured, personal preference for physical activity professions

There is no confounding between A and Y Pr[Y=1|A=1,C=0]Pr[Y=1|A=0,C=0] is Pr[Ya=1]Pr[Ya=0]

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8.4 Selection bias and censoring

  • Censoring as "treatment"
  • Identifiability conditions of exchangeability, positivity, and consistency hold for both A and C
  • Analytical methods
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8.5 How to adjust for selection bias

  • IP weighting (or standardization)
    • Assigning a weight to each selected individual (C = 0) that accounts for the individuals with same A and L, but C=1
    • Weights constructed from the probability of selection model: Pr[C=0|L,A]
  • Effect measure in the population had no one been censored
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8.6 Selection without bias

  • A: surgery
  • Y: death
  • E: haplotype

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  • "Collider stratification is not always a source of selection bias"
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References

Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC (v. 31jan21)

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Selection bias

Classic definition

  • The magnitude of the association is different for participants and non-participants

    Structural definition

  • Occurs when conditioning on the common child or its descendants of two variables

    Encompasses various biases

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