Basic package workflow"

In steppedwedge: Analyze Data from Stepped Wedge Cluster Randomized Trials

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(steppedwedge)

# Including this twice (once here with include=F) avoids a bug having to do with dplyr and pkgdown
data(sw_data_example)
dat <- load_data(
  time = "period",
  cluster_id = "cluster",
  individual_id = NULL,
  treatment = "trt",
  outcome = "outcome_bin",
  data = sw_data_example
)

The load_data function takes in raw data and creates a data object that can be accepted by the plot_design and analyze functions. We use the made-up dataframe sw_data_example to demonstrate the workflow.

data(sw_data_example)
head(sw_data_example)

dat <- load_data(
  time = "period",
  cluster_id = "cluster",
  individual_id = NULL,
  treatment = "trt",
  outcome = "outcome_bin",
  data = sw_data_example
)

The plot_design function produces a diagram of the stepped wedge design and a summary of the variables.

plot_dat <- plot_design(dat)

print(plot_dat)

The analyze function analyzes the stepped wedge data. First, we analyze the data using a mixed effects model, with the Time Average Treament Effect (TATE) as the estimand, assuming an Immediate Treatment (IT) effect, passing the family = "binomial" and link = "logit" arguments to glmer.

analysis_1 <- analyze(
  dat = dat,
  method = "mixed",
  estimand_type = "TATE", 
  exp_time = "IT",
  family = binomial,
  re = c("clust", "time")
)

print(analysis_1)

Repeat the analysis, but including a random effect for cluster only, not for cluster-time interaction.

analysis_1b <- analyze(
  dat = dat,
  method = "mixed",
  estimand_type = "TATE", 
  exp_time = "IT",
  family = binomial,
  re = "clust"
)

print(analysis_1b)

Repeat the analysis, but using GEE rather than a mixed model.

analysis_2 <- analyze(
  dat = dat,
  method = "GEE",
  estimand_type = "TATE", 
  exp_time = "IT",
  family = binomial,
  corstr = "exchangeable"
)

print(analysis_2)

Mixed model, with Time Average Treament Effect (TATE) as the estimand, using an Exposure Time Indicator (ETI) model.

analysis_3 <- analyze(
  dat = dat,
  method = "mixed",
  estimand_type = "TATE", 
  exp_time = "ETI",
  family = binomial
)

print(analysis_3)

Mixed model, with Time Average Treatment Effect (TATE) as the estimand, using a Natural Cubic Splines (NCS) model.

analysis_4 <- analyze(
  dat = dat,
  method = "mixed",
  estimand_type = "TATE", 
  exp_time = "NCS",
  family = binomial
)

print(analysis_4)

Mixed model, with Time Average Treament Effect (TATE) as the estimand, using a Treatment Effect Heterogeneity over exposure time (TEH) model.

analysis_5 <- analyze(
  dat = dat,
  method = "mixed",
  estimand_type = "TATE", 
  exp_time = "TEH",
  family = binomial
)

print(analysis_5)

Continuous outcome

Mixed model, with Time Average Treament Effect (TATE) as the estimand, using a Natural Cubic Splines (NCS) model.

analysis_6 <- analyze(
  dat = dat,
  method = "mixed",
  estimand_type = "TATE", 
  exp_time = "NCS",
  family = gaussian
)

print(analysis_6)

steppedwedge documentation built on April 3, 2025, 9:57 p.m.