DepressionDemo

0th

Percentile

Artificial depression treatment dataset

Simulated dataset of a randomized clinical trial (N = 150) to illustrate fitting of (G)LMM trees.

Keywords
datasets
Usage
data("DepressionDemo")
Details

The data were generated such that the duration and anxiety covariates characterized three subgroups with differences in treatment effects. The cluster variable was used to introduce a random intercept that should be accounted for. The treatment outcome is an index of depressive symptomatology.

Format

A data frame containing 150 observations on 6 variables:

depression

numeric. Continuous treatment outcome variable (range: 3-16, M = 9.12, SD = 2.66).

treatment

factor. Binary treatment variable.

cluster

factor. Indicator for cluster with 10 levels.

age

numeric. Continuous partitioning variable (range: 18-69, M = 45, SD = 9.56).

anxiety

numeric. Continuous partitioning variable (range: 3-18, M = 10.26, SD = 3.05).

duration

numeric. Continuous partitioning variable (range: 1-17, M = 6.97, SD = 2.90).

depression_bin

factor. Binarized treatment outcome variable (0 = recovered, 1 = not recovered).

See Also

lmertree, glmertree

Aliases
  • DepressionDemo
Examples
# NOT RUN {
data("DepressionDemo", package = "glmertree")
summary(DepressionDemo)
lt <- lmertree(depression ~ treatment | cluster | anxiety + duration + age, 
        data = DepressionDemo)
plot(lt)
gt <- glmertree(depression_bin ~ treatment | cluster | anxiety + duration + age, 
        data = DepressionDemo)
plot(gt)
# }
Documentation reproduced from package glmertree, version 0.2-0, License: GPL-2 | GPL-3

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