Package 'steppedwedge'

Analyze a stepped wedge dataset

Description

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

---

Load and format data object

Description

Load and format data object

Usage

load_data(
  time,
  cluster_id,
  individual_id = NULL,
  treatment,
  outcome,
  time_type = "discrete",
  data
)

Arguments

time	A character string; the name of the numeric variable representing time. Time can be either discrete or continuous.
cluster_id	A character string; the name of the numeric variable identifying the cluster.
individual_id	A character string (optional); the name of the numeric variable identifying the individual.
treatment	A character string; the name of the binary variable indicating treatment. Values must be either integers (0/1) or Boolean (T/F).
outcome	Either a character string or a vector of two character strings; for a numeric or binary outcome, the single character string indicates the name of the numeric or binary outcome variable; for binomial outcome data, the vector of two character strings indicates the "# of successes" variable and the "# of trials" variable, respectively. Values in the outcome variable(s) must be either numeric or Boolean (T/F).
time_type	One of c("discrete", "continuous"); whether the model treats time as discrete or continuous.
data	A dataframe containing the stepped wedge trial data.

Value

An object of class sw_data

Examples

example_data <- load_data(time ="period", cluster_id = "cluster", individual_id = NULL,
treatment = "trt", outcome = "outcome_cont", data = sw_data_example)
base::summary(example_data)

---

Plot stepped wedge design

Description

Plot stepped wedge design

Usage

plot_design(dat)

Arguments

dat	A dataframe containing the stepped wedge trial data.

Value

A list with a plot of the stepped wedge design.

Examples

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

---

Summarize a list returned by steppedwedge::analysis()

Description

Summarize a list returned by steppedwedge::analysis()

Usage

## S3 method for class 'sw_analysis'
print(x, ...)

Arguments

x	A list returned by steppedwedge::analysis()
...	Additional arguments

Value

A summary of the analysis object, including an estimate and 95

Examples

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

# Analysis example: 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", family = poisson)

results_tate

---

Example stepped wedge data

Description

Data generated for the purpose of demonstrating the steppedwedge package

Usage

sw_data_example

Format

## 'sw_data_example' A data frame with 2,063 rows and 6 columns:

cluster

Cluster id

period

Time period

trt

Treatment indicator

outcome_bin

Binary outcome

outcome_cont

Continuous outcome

...

---