tests/testthat/_snaps/estimate.md

In uo-cmor/cea: Functions for Trial-Based Cost-Effectiveness Analysis

cea_estimate print methods work

===============================================
=== Cost-Effectiveness Regression Estimates ===
===============================================

Multivariate Covariate Generalized Linear Model

Call:
 estimate.data.frame(QALYs = "QALYs", costs = "Cost", treatment = "booster", 
    covars = c("age", "sex"), data = moa2_ex)

------------------
Univariate Models:

  QALYs: QALYs ~ booster + age + sex
    * Link function: identity 
    * Variance function: constant 
    * Covariance function: identity

  Costs: Cost ~ booster + age + sex
    * Link function: log 
    * Variance function: tweedie 
    * Covariance function: identity

------------------
Incremental Treatment Effects:
  QALYs:     +0.069 
  Costs:      +2421 
  ICER:       35012

===============================================

===============================================
=== Cost-Effectiveness Regression Estimates ===
===============================================

Multivariate Generalized Linear Mixed-Effects Model

Call:
 estimate.data.frame(QALYs = "QALYs", costs = "Cost", treatment = "booster", 
    covars = c("age", "sex"), data = moa2_ex, method = "mglmmPQL")

------------------
Univariate Models:

  QALYs: QALYs ~ booster + age + sex
    * Family: gaussian 
    * Link: identity 
  Costs: Cost ~ booster + age + sex
    * Family: Gamma 
    * Link: log 
------------------
Incremental Treatment Effects:
  QALYs:     +0.069 
  Costs:      +2421 
  ICER:       35012

===============================================

===============================================
=== Cost-Effectiveness Regression Estimates ===
===============================================

Multivariate Covariate Generalized Linear Model

Call:
 estimate.data.frame(QALYs = "QALYs", costs = "Cost", treatment = "tx", 
    covars = c("age", "sex"), data = moa2)

------------------
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

===============================================

===============================================
=== Cost-Effectiveness Regression Estimates ===
===============================================

Multivariate Generalized Linear Mixed-Effects Model

Call:
 estimate.data.frame(QALYs = "QALYs", costs = "Cost", treatment = "tx", 
    covars = c("age", "sex"), data = moa2, method = "mglmmPQL")

------------------
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

===============================================

================================================================
=== Multiply-Imputed 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:
(From first imputed dataset; use `pool_cea()` to compute pooled estimates)
                ExB         MT   MT + ExB 
  QALYs:     +0.089     +0.171     +0.007 
  Costs:      +2238      +2084      +1252 
  ICER:       25266      12197     167702

===============================================

uo-cmor/cea documentation built on Dec. 23, 2021, 2:01 p.m.