Term
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Definition
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Description
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X
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–
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Predictor matrix for the true outcome.
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Z(1)
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–
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Predictor matrix for the first-stage observed outcome, conditional on
the true outcome.
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Z(2)
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–
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Predictor matrix for the second-stage observed outcome, conditional on
the true outcome and first-stage observed outcome.
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Y
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Y ∈ {1, 2}
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True binary outcome. Reference category is 2.
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yij
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𝕀{Yi = j}
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Indicator for the true binary outcome.
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Y*(1)
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Y*(1) ∈ {1, 2}
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First-stage observed binary outcome. Reference category is 2.
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yik*(1)
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𝕀{Yi*(1) = k}
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Indicator for the first-stage observed binary outcome.
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Y*(2)
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Y*(2) ∈ {1, 2}
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Second-stage observed binary outcome. Reference category is 2.
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yiℓ*(2)
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𝕀{Yi*(2) = ℓ}
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Indicator for the second-stage observed binary outcome.
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True Outcome Mechanism
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logit{P(Y = j|X; β)} = βj0 + βjXX
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Relationship between X and the
true outcome, Y.
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First-Stage Observation Mechanism
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logit{P(Y*(1) = k|Y = j, Z(1); γ(1))} = γkj0(1) + γkjZ(1)(1)Z(1)
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Relationship between Z(1) and the first-stage
observed outcome, Y*(1), given the true
outcome Y.
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Second-Stage Observation Mechanism
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logit{P(Y*(2) = ℓ|Y*(1) = k, Y = j, Z(2); γ(2))} = γℓkj0(2) + γℓkjZ(2)(2)Z(2)
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Relationship between Z(2) and the second-stage
observed outcome, Y*(2), given the
first-stage observed outcome, Y*(1), and the true
outcome Y.
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πij
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$P(Y_i = j | X ; \beta) =
\frac{\text{exp}\{\beta_{j0} + \beta_{jX} X_i\}}{1 +
\text{exp}\{\beta_{j0} + \beta_{jX} X_i\}}$
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Response probability for individual i’s true outcome category.
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πikj*(1)
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$P(Y^{*(1)}_i = k | Y = j, Z^{(1)} ;
\gamma^{(1)}) = \frac{\text{exp}\{\gamma^{(1)}_{kj0} +
\gamma^{(1)}_{kjZ^{(1)}} Z_i^{(1)}\}}{1 + \text{exp}\{\gamma^{(1)}_{kj0}
+ \gamma^{(1)}_{kjZ^{(1)}} Z_i^{(1)}\}}$
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Response probability for individual i’s first-stage observed outcome
category, conditional on the true outcome.
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πiℓkj*(2)
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$P(Y^{*(2)}_i = \ell | Y^{*(1)} = k, Y = j,
Z^{(2)} ; \gamma^{(2)}) = \frac{\text{exp}\{\gamma^{(2)}_{\ell kj0} +
\gamma^{(2)}_{\ell kjZ^{(2)}} Z_i^{(2)}\}}{1 +
\text{exp}\{\gamma^{(2)}_{\ell kj0} + \gamma^{(2)}_{\ell kjZ^{(2)}}
Z_i^{(2)}\}}$
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Response probability for individual i’s second-stage observed outcome
category, conditional on the first-stage observed outcome and the true
outcome.
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πik*(1)
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$P(Y^{*(1)}_i = k | X, Z^{(1)} ; \gamma^{(1)})
= \sum_{j = 1}^2 \pi^{*(1)}_{ikj} \pi_{ij}$
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Response probability for individual i’s first-stage observed outcome
cateogry.
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πjj*(1)
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$P(Y^{*(1)} = j | Y = j, Z^{(1)} ;
\gamma^{(1)}) = \sum_{i = 1}^N \pi^{*(1)}_{ijj}$
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Average probability of first-stage correct classification for category
j.
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πjjj*(2)
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$P(Y^{*(2)} = j | Y^{*(1)}_i = j, Y = j,
Z^{(2)} ; \gamma^{(2)}) = \sum_{i = 1}^N \pi^{*(2)}_{ijjj}$
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Average probability of first-stage and second-stage correct
classification for category j.
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First-Stage Sensitivity
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$P(Y^{*(1)} = 1 | Y = 1, Z^{(1)} ;
\gamma^{(1)}) = \sum_{i = 1}^N \pi^{*(1)}_{i11}$
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True positive rate. Average probability of observing first-stage outcome
k = 1, given the true outcome
j = 1.
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First-Stage Specificity
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$P(Y^{*(1)} = 2 | Y = 2, Z^{(1)} ;
\gamma^{(1)}) = \sum_{i = 1}^N \pi^{*(1)}_{i22}$
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True negative rate. Average probability of observing first-stage outcome
k = 2, given the true outcome
j = 2.
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βX
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–
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Association parameter of interest in the true outcome mechanism.
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γ11Z(1)(1)
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–
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Association parameter of interest in the first-stage observation
mechanism, given j = 1.
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γ12Z(1)(1)
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–
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Association parameter of interest in the first-stage observation
mechanism, given j = 2.
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γ111Z(2)(2)
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–
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Association parameter of interest in the second-stage observation
mechanism, given k = 1 and
j = 1.
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γ121Z(2)(2)
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–
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Association parameter of interest in the second-stage observation
mechanism, given k = 2 and
j = 1.
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γ112Z(2)(2)
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–
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Association parameter of interest in the second-stage observation
mechanism, given k = 1 and
j = 2.
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γ122Z(2)(2)
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–
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Association parameter of interest in the second-stage observation
mechanism, given k = 2 and
j = 2.
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