stepdown {crctStepdown}

Conduct the randomisation-based stepdown procedure

Description

For a set of models fit with lme4, base R, or glmmrBase, the function will conduct the randomisation tests and generate p-values for the null hypotheses of no treatment effect that controls the family-wise error rate, and generates a 100(1-alpha)% confidence set for the treatment effect model parameters.

Usage

stepdown(
  fitlist,
  tr_var = "treat",
  cl_var = "cl",
  data,
  alpha = 0.05,
  plots = TRUE,
  n_permute = 1000,
  nsteps = 1000,
  type = "rw",
  rand_func = NULL,
  confint = TRUE,
  sigma = NULL,
  ci_start_values = NULL,
  verbose = TRUE
)

Arguments

fitlist

A list of models fitted with lme4, base R (lm or glm), or glmmrBase. All models should be fit using the same data frame.

tr_var

String indicating the name of the column in data that is a binary indicator for whether the observation was under the treatment (1=treatment, 0=control)

cl_var

String specifying the name of the column identifying the clusters/cluster-time

data

A data frame containing the data used to fit the models in fitlist

alpha

Numeric. 100(1-alpha)% confidence intervals are calculated. Default it 0.05

plots

Logical indicating whether to plot permutational distributions and confidence interval search during running of function. Default is TRUE

n_permute

Number of permutations of the randomisation test to run

nsteps

Number of steps of the confidence interval search process

type

Method of correction: options are "rw" = Romano-Wolf randomisation test based stepdown, "h" = Holm standard stepdown, "h" = Holm stepdown using randomisation test, "b" = standard Bonferroni, "br" = Bonerroni using randomisation test, or "none" = randomisation test with no correction.

rand_func

String of the name of a function that re-randomises the clusters. The function must take the arguments nJ for the number of clusters and nT for the number of time periods. The function should produce a data frame that identifies the clusters in the treatment group under the new randomisation scheme. The data frame can either have a single column with name cl_var or two columns of cl_var and t identifying the cluster ID and time period a cluster joins the treatment group. If NULL then clusters are randomised in a 1:1 ratio to treatment and control

confint

Logical indicating whether to run the confidence interval search process

sigma

optional, list of estimated covariance matrices of the observations from the models in fitlist. If provided then the weighted q-score statistic is used.

ci_start_values

Optional list. The list should contain named vectors "upper" and/or "lower" that provide a set of starting values for the upper and/or lower confidence interval searches, respectively. Alternatively, a named scalar scale can be provided such that the starting values of the confidence interval search procedure are est +/- scale*SE.

verbose

Logical indicating whether to provide detailed output

Value

A data frame with the point estimates, p-values, and confidence intervals

Examples

out <- twoarm_sim()
data <- out[[1]]
  fit1 <- lme4::glmer(y1 ~ treat + (1|cl) ,
data=data,
family="poisson")

fit2 <- lme4::glmer(y2 ~ treat + (1|cl),
                    data=data,
                    family="poisson")
 stepdown(fitlist=list(fit1,fit2),
          data=data,
          n_permute = 100,
          nsteps=100,
          plots=FALSE,
          verbose=TRUE)