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COMMA Notation Guide

Kimberly Webb

2024-04-23

This guide is designed to summarize key notation and quantities used the COMMA R Package and associated publications.

Term Definition Description
$X$ -- Predictor matrix for the true mediator and outcome.
$C$ -- Covariate matrix for the true mediator and outcome.
$Z$ -- Predictor matrix for the observed mediator, conditional on the true mediator
$Y$ -- Outcome variable.
M $M \in \{1, 2\}$ True binary mediator. Reference category is 2.
$m_{ij}$ $\mathbb{I}\{M_i = j\}$ Indicator for the true binary mediator.
$M^*$ $M^* \in \{1, 2\}$ Observed binary mediator. Reference category is 2.
$m^*_{i \ell}$ $\mathbb{I}\{M^*_i = \ell \}$ Indicator for the observed binary mediator.
True Mediator Mechanism $\text{logit} \{ P(M = 1 | X, C ; \beta) \} = \beta_{0} + \beta_{X} X + \beta_{C} C$ Relationship between $X$ and $C$ and the true mediator, $M$.
Observed Mediator Mechanism $\text{logit}\{ P(M^* = 1 | M = m, Z ; \gamma) \} = \gamma_{1m0} + \gamma_{1mZ} Z$ Relationship between $Z$ and the observed mediator, $M^*$, given the true mediator $M$.
Outcome Mechanism $E(Y| X, C, M ; \theta) \} = \theta{0} + \theta_{X} X + \theta_{C} C \theta_{M}M + \theta_{XM}XM$ Relationship between $X$, $C$, and $M$ and the outcome of interest $Y$.
$\pi_{ij}$ $P(M_i = j | X, C ; \beta) = \frac{\text{exp}\{\beta_{j0} + \beta_{jX} X_i + \beta_{jC} C_i\}}{1 + \text{exp}\{\beta_{j0} + \beta_{jX} X_i + \beta_{jC} C_i\}}$ Response probability for individual $i$'s true mediator category.
$\pi^*_{i \ell j}$ $P(M^*_i = \ell | M_i = j, Z ; \gamma) = \frac{\text{exp}\{\gamma_{\ell j 0} + \gamma_{ \ell jZ} Z_i\}}{1 + \text{exp}\{\gamma_{\ell j0} + \gamma_{kjZ} Z_i\}}$ Response probability for individual $i$'s observed mediator category, conditional on the true mediator.
$\pi^*_{i \ell}$ $P(M^*_i = \ell | M_i, X, Z ; \gamma) = \sum_{j = 1}^2 \pi^*_{i \ell j} \pi_{ij}$ Response probability for individual $i$'s observed mediator cateogry.
$\pi^*_{jj}$ $P(M^* = j | M = j, Z ; \gamma) = \sum_{i = 1}^N \pi^*_{ijj}$ Average probability of correct classification for category $j$.
Sensitivity $P(M^* = 1 | M = 1, Z ; \gamma) = \sum_{i = 1}^N \pi^*_{i11}$ True positive rate. Average probability of observing mediator $k = 1$, given the true mediator $j = 1$.
Specificity $P(M^* = 2 | M = 2, Z ; \gamma) = \sum_{i = 1}^N \pi^*_{i22}$ True negative rate. Average probability of observing mediator $k = 2$, given the true mediator $j = 2$.
$\beta_X$ -- Association parameter of interest in the true mediator mechanism.
$\gamma_{11Z}$ -- Association parameter of interest in the observed mediator mechanism, given $j=1$.
$\gamma_{12Z}$ -- Association parameter of interest in the observed mediator mechanism, given $j=2$.
$\theta_X$ -- Association parameter of interest in the outcome mechanism.
$\theta_M$ -- Association parameter relating the true mediator to the outcome.
$\theta_{XM}$ -- Association parameter for the interaction between $X$ and $M$ in the outcome mechanism.

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They may not be fully stable and should be used with caution. We make no claims about them.
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