print.gformula_survival function

Print and summary methods for "gformula" objects

Print and summary method for objects of class "gformula_survival", "gformula_continuous_eof", or "gformula_binary_eof".

## S3 method for class 'gformula_survival'
print(
  x,
  all_times = FALSE,
  coefficients = FALSE,
  stderrs = FALSE,
  rmses = FALSE,
  hazardratio = FALSE,
  fits = FALSE,
  ...
)

## S3 method for class 'gformula_continuous_eof'
print(
  x,
  coefficients = FALSE,
  stderrs = FALSE,
  rmses = FALSE,
  fits = FALSE,
  ...
)

## S3 method for class 'gformula_binary_eof'
print(
  x,
  coefficients = FALSE,
  stderrs = FALSE,
  rmses = FALSE,
  fits = FALSE,
  ...
)

## S3 method for class 'gformula'
summary(object, ...)

## S3 method for class 'summary.gformula'
print(
  x,
  all_times = TRUE,
  coefficients = FALSE,
  stderrs = FALSE,
  rmses = FALSE,
  hazardratio = FALSE,
  fits = TRUE,
  ...
)

Arguments

• x: Object of class "gformula_survival", "gformula_continuous_eof", "gformula_binary_eof", or "summary.gformula" (for print).
• all_times: Logical scalar indicating whether to print the results for all time points. This argument is only applicable to objects of class "gformula_survival". If this argument is set to FALSE, the results are only shown for the final time point. The default is FALSE for print and TRUE for summary.
• coefficients: Logical scalar indicating whether to print the model coefficients. The default is FALSE.
• stderrs: Logical scalar indicating whether to print the standard error of the model coefficients. The default is FALSE.
• rmses: Logical scalar indicating whether to print the model root mean square errors (RMSEs). The default is FALSE.
• hazardratio: Logical scalar indicating whether to print the hazard ratio between two interventions (if computed). If bootstrapping was used, 95% confidence intervals will be given. This argument is only applicable to objects of class "gformula_survival". The default is FALSE.
• fits: Logical scalar indicating whether to print summaries of the fitted models for the time-varying covariates, outcome, and competing event (if applicable). This argument is only effective if the argument model_fits was set to TRUE in gformula. The default is FALSE for print and TRUE for summary.
• ...: Other arguments.
• object: Object of class "gformula" (for summary).

Returns

No value is returned for the print functions. The summary function returns the object passed to it and adds the class "summary.gformula" to it.

Examples

## Estimating the effect of static treatment strategies on risk of a
## failure event

id <- 'id'
time_points <- 7
time_name <- 't0'
covnames <- c('L1', 'L2', 'A')
outcome_name <- 'Y'
outcome_type <- 'survival'
covtypes <- c('binary', 'bounded normal', 'binary')
histories <- c(lagged, lagavg)
histvars <- list(c('A', 'L1', 'L2'), c('L1', 'L2'))
covparams <- list(covmodels = c(L1 ~ lag1_A + lag_cumavg1_L1 + lag_cumavg1_L2 +
                                  L3 + t0,
                                L2 ~ lag1_A + L1 + lag_cumavg1_L1 +
                                  lag_cumavg1_L2 + L3 + t0,
                                A ~ lag1_A + L1 + L2 + lag_cumavg1_L1 +
                                  lag_cumavg1_L2 + L3 + t0))
ymodel <- Y ~ A + L1 + L2 + L3 + lag1_A + lag1_L1 + lag1_L2 + t0
intervention1.A <- list(static, rep(0, time_points))
intervention2.A <- list(static, rep(1, time_points))
int_descript <- c('Never treat', 'Always treat')
nsimul <- 10000

gform_basic <- gformula(obs_data = basicdata_nocomp, id = id,
                        time_points = time_points,
                        time_name = time_name, covnames = covnames,
                        outcome_name = outcome_name,
                        outcome_type = outcome_type, covtypes = covtypes,
                        covparams = covparams, ymodel = ymodel,
                        intervention1.A = intervention1.A,
                        intervention2.A = intervention2.A,
                        int_descript = int_descript,
                        histories = histories, histvars = histvars,
                        basecovs = c('L3'), nsimul = nsimul,
                        seed = 1234)
summary(gform_basic)

See Also

gformula