COMBO Notation Guide - Two-stage Misclassification Model

Notation

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