======================================================
=== Pooled Cost-Effectiveness Regression Estimates ===
======================================================
Based on 2 imputed datasets.
Call:
estimate.mids(QALYs = "QALYs", costs = "Cost", treatment = "tx",
covars = c("age", "sex"), data = moa2_mi)
Data:
mice(data = data, m = m, where = where, maxit = 0, remove.collinear = FALSE,
allow.na = TRUE)
------------------
Univariate Models:
QALYs: QALYs ~ tx + age + sex
* Link function: identity
* Variance function: constant
* Covariance function: identity
Costs: Cost ~ tx + age + sex
* Link function: log
* Variance function: tweedie
* Covariance function: identity
------------------
Incremental Treatment Effects:
ExB MT MT + ExB
QALYs: +0.089 +0.171 +0.007
Costs: +2238 +2084 +1252
ICER: 25266 12197 167702
===============================================
======================================================
=== Pooled Cost-Effectiveness Regression Estimates ===
======================================================
Based on 2 imputed datasets.
Call:
estimate.mids(QALYs = "QALYs", costs = "Cost", treatment = "tx",
covars = c("age", "sex"), data = moa2_mi, method = "mglmmPQL")
Data:
mice(data = data, m = m, where = where, maxit = 0, remove.collinear = FALSE,
allow.na = TRUE)
------------------
Univariate Models:
QALYs: QALYs ~ tx + age + sex
* Family: gaussian
* Link: identity
Costs: Cost ~ tx + age + sex
* Family: Gamma
* Link: log
------------------
Incremental Treatment Effects:
ExB MT MT + ExB
QALYs: +0.089 +0.171 +0.007
Costs: +2238 +2084 +1252
ICER: 25266 12197 167702
===============================================
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