Analyze a stepped wedge dataset

Usage

analyze(
  dat,
  method = "mixed",
  estimand_type = "TATE",
  estimand_time = c(1, attr(dat, "n_seq")),
  exp_time = "IT",
  cal_time = "categorical",
  family = stats::gaussian,
  re = c("clust", "time"),
  corstr = "exchangeable",
  offset = NULL
)

Arguments

dat: A dataframe containing the stepped wedge trial data.
method: A character string; either "mixed", for a mixed-effects model, or "GEE", for generalized estimating equations.
estimand_type: One of c("TATE", "PTE"); "TATE" represents the time-averaged treatment effect and "PTE" represents the point treatment effect.
estimand_time: An integer vector of length 1 or 2. When estimand_type="TATE", `estimand_time` must be a numeric vector of length 2, representing the start and end times of the exposure time period to average over. When estimand_type="PTE", `estimand_time` must be a numeric vector of length 1, representing the time period of interest. See examples.
exp_time: One of c("IT", "ETI", "NCS", "TEH"); model for exposure time. "IT" encodes an immediate treatment model with a single treatment effect parameter. "ETI" is an exposure time indicator model, including one indicator variable for each exposure time point. "NCS" uses a natural cubic spline model for the exposure time trend. "TEH" includes a random slope term in the model, allowing the treatment effect to vary by timepoint.
cal_time: One of c("categorical", "NCS", "linear", "none"); model for calendar time. "categorical" uses indicator variables for discrete time points, as in the Hussey and Hughes model. "NCS" uses a natural cubic spline, useful for datasets with continuous time. "linear" uses a single slope parameter. "none" assumes that there is no underlying calendar time trend.
family: A family object; see documentation for `glm`.
re: A character vector of random effects to include; only relevant if method="mixed" is used. Possible random effects include "clust" (random intercept for cluster), "time" (random intercept for cluster-time interaction), "ind" (random intercept for individuals; appropriate when a cohort design is used), "tx" (random treatment effect)
corstr: One of c("independence", "exchangeable", "ar1"); only relevant if method="GEE" is used. Defines the GEE working correlation structure; see the documentation for `geepack::geeglm`.
offset: A linear predictor offset term; see docs for `lme4::lmer`.

Value

A list with the model object, model type as a string, estimand type as a string, numeric treatment effect estimate, numeric treatment effect standard error, and treatment effect 95

Examples

# Load data
test_data <- load_data(time ="period", cluster_id = "cluster", individual_id = NULL,
treatment = "trt", outcome = "outcome_cont", data = sw_data_example)

# Analysis example 1: TATE estimand for exposure times 1 through 4
results_tate <- analyze(dat = test_data, method = "mixed", estimand_type = "TATE",
estimand_time = c(1, 4), exp_time = "ETI")

results_tate

# Analysis example 2: PTE estimand for exposure time 3
results_pte <- analyze(dat = test_data, method = "mixed", estimand_type = "PTE",
estimand_time = 3, exp_time = "ETI")

results_pte