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Basic package workflow

Source: vignettes/steppedwedge.Rmd
steppedwedge.Rmd

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)
#>   cluster period trt      prob outcome_bin outcome_cont
#> 1       1      1   0 0.5667807           0  -3.02179837
#> 2       1      1   0 0.5667807           0  -0.07145287
#> 3       1      1   0 0.5667807           1   0.96807617
#> 4       1      1   0 0.5667807           0   0.29456948
#> 5       1      1   0 0.5667807           1  -0.83921584
#> 6       1      1   0 0.5667807           1  -0.42335941

dat <- load_data(
  time = "period",
  cluster_id = "cluster",
  individual_id = NULL,
  treatment = "trt",
  outcome = "outcome_bin",
  data = sw_data_example
)
#> Stepped wedge dataset loaded. Discrete time design with 15 clusters, 5 sequences, and 6 time points.

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)
#> $design_plot

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)
#> Treatment effect estimate: -0.189
#> Treatment effect 95% confidence interval: -0.448, 0.07
#> Converged: TRUE

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)
#> Treatment effect estimate: -0.189
#> Treatment effect 95% confidence interval: -0.448, 0.07
#> Converged: TRUE

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)
#> Treatment effect estimate: -0.188
#> Treatment effect 95% confidence interval: -0.438, 0.061
#> Converged:

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
)
#> boundary (singular) fit: see help('isSingular')

print(analysis_3)
#> Treatment effect estimate: -0.184
#> Treatment effect 95% confidence interval: -0.496, 0.129
#> Converged: TRUE

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
)
#> boundary (singular) fit: see help('isSingular')

print(analysis_4)
#> Treatment effect estimate: -0.184
#> Treatment effect 95% confidence interval: -0.497, 0.128
#> Converged: TRUE

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
)
#> boundary (singular) fit: see help('isSingular')

print(analysis_5)
#> Treatment effect estimate: -0.189
#> Treatment effect 95% confidence interval: -0.448, 0.07
#> Converged: TRUE

Continuous outcome

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


dat_cont <- load_data(
  time = "period",
  cluster_id = "cluster",
  individual_id = NULL,
  treatment = "trt",
  outcome = "outcome_cont",
  data = sw_data_example
)
#> Stepped wedge dataset loaded. Discrete time design with 15 clusters, 5 sequences, and 6 time points.

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

print(analysis_6)
#> Treatment effect estimate: 0.026
#> Treatment effect 95% confidence interval: -0.171, 0.223
#> Converged: TRUE

Plotting effect curves

The plot_effect_curve function plots effect estimates by exposure time for one or more analyze objects.


IT_model <- analyze(
  dat = dat_cont, method = "mixed", estimand_type = "TATE",
  estimand_time = c(1, 4), exp_time = "IT"
)
ETI_model <- analyze(
  dat = dat_cont, method = "mixed", estimand_type = "TATE", 
  estimand_time = c(1, 4), exp_time = "ETI"
)
NCS_4_model <- analyze(
  dat = dat_cont, method = "mixed", estimand_type = "TATE",
  estimand_time = c(1, 4), exp_time = "NCS", n_knots_exp = 4
)
TEH_model <- analyze(
  dat = dat_cont, method = "mixed", estimand_type = "TATE",
  estimand_time = c(1, 4), exp_time = "TEH"
)
#> boundary (singular) fit: see help('isSingular')

plot_effect_curves(IT_model, NCS_4_model, ETI_model, TEH_model, facet_nrow = 1)