target trial emulation入門

観察データをRCTのように分析するフレームワークの意義について

Transcript

1. Introduction to the Target Trial Emulation
   Framework
   KRSK (@koro485)
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2. Randomized Trial and Observational Study
   • Ideally, we want to conduct an RCT to answer our questions
   • Not always feasible
   • Alternatively, use observational data
   • Results can differ substantially(!)
   Example: Postmenopausal hormone therapy and heart disease?
   • Preventive!
   • e.g., HR = 0.68
   • (Grodstein et al. 2006)
   Observational Study
   • Harmful!
   • e.g., HR = 1.24
   • (Manson et al. 2003)
   RCT
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3. 1. Lack of randomization (i.e., confounding)
   Sources of Discrepancies
   2. Other sources of bias
   (selection, immortal time, measurement error)
   3. Different, and often ambiguous, causal estimand
   (answering different questions)
   Target trial emulation
   3
   Study design, regression, propensity scores
   

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4. On Confounding Adjustment
   • ”Adjusting for”, “controlling for”,…
   • Assumption: 𝒀𝒂∐𝑨|𝑳
   A Y
   L
   U
   • Unmeasured confounder(s) U?
   • Almost always the case in observational epi
   • Estimates are biased, but perhaps not terribly…
   • Magnitude of bias by U needs to be strong conditional on L
   • Rationale behind the E-value analysis
   References:
   Ø VanderWeele, Tyler J., and Peng Ding. "Sensitivity analysis in observational research: introducing the
   E-value." Annals of internal medicine 167.4 (2017): 268-274.
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5. 1. Temporal ordering (require at least three time points!)
   2. Covariate selection
   1SFFYQPTVSF
   XBWF
   #BTFMJOF
   XBWF
   'PMMPXŠVQ
   XBWF
   Covariates Exposure Outcomes
   References:
   Ø VanderWeele, T. J. (2019). Principles of confounder selection. European Journal of Epidemiology, 34:211-219.
   Ø Ideally, choose based on a DAG and backdoor criterion (i.e., use theory)
   Ø More pragmatic approach:
   Ø Control for a cause of the exposure, the outcome, or both
   Ø Key covariates: pre-baseline values of outcomes and exposure
   Ø Require a large sample size
   5
   On Confounding Adjustment
   

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6. 1. Lack of randomization (i.e., confounding)
   Sources of Discrepancies
   2. Other sources of bias (selection, immortal time)
   3. Different, and often ambiguous, causal estimand
   (answering different questions)
   Target trial emulation
   6
   Too much
   focus on this!
   

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7. 1. Imagine a protocol of an ideal trial that answers your question
   2. Emulate it by using observational data
   • Only pragmatic trial (no blinding, no placebo)
   Target Trial Emulation
   Protocol Component Description
   Eligibility Who’s in the study?
   Assignment Randomly. When?
   Follow-up period From randomization
   Treatment strategies How do you treat patients?
   Causal contrast of interest ITT vs Per-protocol?
   Aligning key time points
   prevents some form of bias
   Clarify causal question of
   interest
   References:
   Ø Hernán, Miguel A., and James M. Robins. "Using big data to emulate a target trial when a randomized trial is
   not available." American journal of epidemiology 183.8 (2016): 758-764.
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8. Why Emulate?
   • Target trial emulation is a framework
   • Forces you to ponder the key elements of a strong study design
   • Causal inference is sometimes feasible without emulation
   But, if we obtain comparable results from RCTs
   & emulated trials using observational data…
   • More confidence in the conditional exchangeability assumption
   (i.e., emulation of randomization)
   • Can expand the emulated trial to other types of questions that
   RCTs cannot address:
   • Rare events, long-term effects, etc
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9. Benefit of Emulation 1: Clarifying Causal Estimand
   1. Well-defined interventions
   • Dose? Duration? Initiation? Discontinuation?
   • Consistency assumption (a component of SUTVA)
   • 𝐸[𝑌" 𝐴 = 𝑎 = 𝐸[𝑌|𝐴 = 𝑎]
   2. Causal contrast
   • Intention-to-Treat effect or per-protocol effect?
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10. Treatment Incidence vs Prevalent Treatment
    Randomized trial
    Observational study NOT
    emulating a target trial
    Problems?
    1. Unclear “effect” estimand
    • Implicit assumption: Equivalence of initiation/discontinuation
    2. Prevalent user bias
    • Type of selection bias
    Non-users Control group
    Treatment group Current users
    Current non-users
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11. Prevalent user bias
    1. Consider current exposure (A1
    =0 vs A1
    =1)
    2. Exposure might have started much earlier (e.g, A0
    )
    3. A0
    might affect probabilities of attrition/censoring C
    • e.g., Death before the current exposure assessment
    4. An unmeasured common cause of C and Y (U)
    • Comparing A1
    status among those who managed to survive up to time 1
    and participate in the survey (C=0) induces selection bias
    A1
    Y
    L
    A0
    C
    U
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12. Treatment Incidence vs Prevalent Treatment
    Randomized trial
    Observational study emulating a
    target trial
    1. Analysis is conditional on prior exposure (A0
    )
    2. Estimating the effect of treatment initiation
    Non-users before
    assignment Control group
    Treatment group Current users
    Current non-users
    Non-users at baseline
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13. 0 months 6 months 12 months
    ×
    ×
    Observational Data
    Taking aspirin
    Not taking aspirin
    Event
    ×
    ID A Y
    1 1 1
    2 0 1
    3 0 0
    4 1 0
    5 ? 1
    ×
    Estimating Effects of Treatment “Duration”
    Aspirin use of <6 months (A=0) vs ≥6 months (A=1)
    • Two possible scenarios for ID=5:
    1. Assigned to A=0
    2. Assigned to A= 1 but died
    before 6 months
    References:
    Ø Hernán, Miguel A. "How to estimate the effect of treatment duration on survival outcomes using
    observational data." bmj 360 (2018).
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14. 0 months 6 months 12 months
    ×
    ×
    Observational Data
    Taking aspirin
    Not taking aspirin
    Event
    ×
    ID A Y
    1 1 1
    2 0 1
    3 0 0
    4 1 0
    5 0 1
    ×
    Estimating Effects of Treatment “Duration”
    Incorrect Approach 1: Use observed duration
    Problems:
    • Immortal time bias
    • Those with A=1 by definition
    survive longer
    • A=1 seems protective even
    under the null
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15. Estimating Effects of Treatment “Duration”
    Incorrect Approach 2: Analyze who were alive at 6months
    Problems:
    • Selection bias
    • Conditioning on being alive up
    to 6 months, post-treatment
    eligibility criteria
    • Solution? See the BMJ paper by
    Hernan (2018)
    0 months 6 months 12 months
    ×
    ×
    Observational Data
    Taking aspirin
    Not taking aspirin
    Event
    ×
    ID A Y
    1 1 1
    2 0 1
    3 0 0
    4 1 0
    5 ? 1
    ×
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16. Causal Contrast
    • RCTs:
    • Intention-to-treat (ITT) effect
    • Effect of assignment X
    • Per-protocol effect
    • Effect of treatment ̅
    𝐴
    • Observational studies:
    • Intention-to-treat (ITT) effect
    • Effect of baseline treatment A0
    • Per-protocol effect
    • Effect of treatment ̅
    𝐴
    A0
    Y
    X A1
    U0
    U1
    A0
    Y
    A1
    L
    U1
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17. Causal Contrast
    • Baseline exposure analysis
    • ITT effect
    • e.g., 𝐸[𝑌#!$% − 𝑌#!$&]
    • Time-varying exposure analysis
    • Per-protocol effect
    • e.g., 𝐸[𝑌#!$%,#"$% − 𝑌#!$&,#"$&]
    A0
    Y
    A1
    L0
    L1
    • When exposure varies substantially over time, the estimated ITT effect
    may be small (as in RCT with low adherence)
    • Example
    • Social participation and depressive symptoms among older adults
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18. ●
    ●
    Point Exposure
    Time−varying Exposure
    0.80 0.85 0.90 0.95 1.00
    Estimated Prevalence Ratio
    Type of Exposure
    Joint intervention in social participation in two time points
    (i.e., per-protocol effect of sustained social participation)
    Single point intervention, in which people may
    deviate from the baseline exposure over time
    (i.e., ITT effect of baseline exposure)
    Social participation is more beneficial
    References:
    Ø Shiba, Koichiro, et al. "Estimating the impact of sustained social participation on depressive symptoms in
    older adults." Epidemiology 32.6 (2021): 886-895. 18
    

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19. Three key time points in a target trial
    • Eligibility (E)
    • Treatment assignment (A)
    • “Time-zero” (t0
    )
    Benefit of Emulation 2: Align key time points
    E
    Follow-up
    A
    t0
    • In an ideal RCT
    • E before A and t0
    • A and t0
    coincide
    • In an observational study
    • Often misaligned
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20. Misalignment 1: Eligibility After Assignment
    E
    Follow-up
    A
    t0
    • Selection bias
    • Eligibility criteria affected by
    treatment (assignment)
    • Example: Assessing individuals
    whose outcomes were observed
    Misalignment 2: Time Zero After Assignment
    E
    Follow-up
    A
    t0
    • Selection bias
    • Conditioning on no loss-to-
    follow-up until t0
    • Example: Prevalent user analysis
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21. Misalignment 3: Assignment After Time Zero
    • Immortal time bias
    • Treatment assignment is
    determined sometime after the
    time zero
    • Example: Analysis of treatment
    duration
    E
    Follow-up
    A
    t0
    References:
    Ø Hernán, Miguel A., et al. "Specifying a target trial prevents immortal time bias and other self-inflicted injuries
    in observational analyses." Journal of clinical epidemiology 79 (2016): 70-75.
    Emulating a target trial forces you to think through
    how to align E, A, and t0
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22. 1. Lack of randomization (i.e., confounding)
    Sources of Discrepancies (Revisited)
    2. Other sources of bias (selection, immortal time)
    3. Different, and often ambiguous, causal estimand
    (answering different questions)
    Easy to fix by target trial emulation
    Hard to fix
    The low-hanging fruit for causal inference!
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23. Successful Cases of Target Trial Emulation
    1. Dickerman, B. A., Gerlovin, H., Madenci, A. L., Kurgansky, K. E., Ferolito, B. R., Figueroa Muñiz,
    M. J., ... & Hernán, M. A. (2022). Comparative effectiveness of BNT162b2 and mRNA-1273
    vaccines in US veterans. New England Journal of Medicine, 386(2), 105-115.
    1. Emilsson L, García-Albéniz X, Logan RW, Caniglia EC, Kalager M, Hernán MA. Examining Bias in
    Studies of Statin Treatment and Survival in Patients With Cancer. JAMA Oncol. 2018 Jan
    1;4(1):63-70. doi: 10.1001/jamaoncol.2017.2752. Erratum in: JAMA Oncol. 2018 Jan 1;4(1):133.
    PMID: 28822996; PMCID: PMC5790310.
    2. Dickerman, B.A., García-Albéniz, X., Logan, R.W. et al. Avoidable flaws in observational
    analyses: an application to statins and cancer. Nat Med 25, 1601–1606 (2019).
    3. García-Albéniz X, Hsu J, Bretthauer M, Hernán MA. Effectiveness of Screening Colonoscopy to
    Prevent Colorectal Cancer Among Medicare Beneficiaries Aged 70 to 79 Years: A Prospective
    Observational Study. Ann Intern Med. 2017 Jan 3;166(1):18-26.
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