Parameter Builder for SEQuential Model and Estimates

Usage

SEQopts(
  bootstrap = FALSE,
  bootstrap.nboot = 100,
  bootstrap.sample = 0.8,
  cense = NA,
  cense.denominator = NA,
  cense.eligible = NA,
  cense.numerator = NA,
  compevent = NA,
  covariates = NA,
  data.return = FALSE,
  denominator = NA,
  deviation = FALSE,
  deviation.col = NA,
  deviation.conditions = c(NA, NA),
  deviation.excused = FALSE,
  deviation.excused_cols = c(NA, NA),
  excused = FALSE,
  excused.cols = c(NA, NA),
  fastglm.method = 2L,
  followup.class = FALSE,
  followup.include = TRUE,
  followup.max = Inf,
  followup.min = -Inf,
  followup.spline = FALSE,
  hazard = FALSE,
  indicator.baseline = "_bas",
  indicator.squared = "_sq",
  km.curves = FALSE,
  multinomial = FALSE,
  ncores = parallel::detectCores() - 1,
  nthreads = data.table::getDTthreads(),
  numerator = NA,
  parallel = FALSE,
  plot.colors = c("#F8766D", "#00BFC4", "#555555"),
  plot.labels = NA,
  plot.subtitle = NA,
  plot.title = NA,
  plot.type = "survival",
  seed = NULL,
  selection.first_trial = FALSE,
  selection.prob = 0.8,
  selection.random = FALSE,
  subgroup = NA,
  survival.max = Inf,
  treat.level = c(0, 1),
  trial.include = TRUE,
  weight.eligible_cols = c(),
  weight.lower = -Inf,
  weight.lag_condition = TRUE,
  weight.p99 = FALSE,
  weight.preexpansion = TRUE,
  weight.upper = Inf,
  weighted = FALSE
)

Arguments

bootstrap: Logical: defines if SEQuential should run bootstrapping, default is FALSE
bootstrap.nboot: Integer: number of bootstraps
bootstrap.sample: Numeric: percentage of data to use when bootstrapping, should in [0, 1], default is 0.8
cense: String: column name for additional censoring variable, e.g. loss-to-follow-up
cense.denominator: String: censoring denominator covariates to the right hand side of a formula object
cense.eligible: String: column name for indicator column defining which rows to use for censoring model
cense.numerator: String: censoring numerator covariates to the right hand side of a formula object
compevent: String: column name for competing event indicator
covariates: String: covariates to the right hand side of a formula object
data.return: Logical: whether to return the expanded dataframe with weighting information
denominator: String: denominator covariates to the right hand side of a to formula object
deviation: Logical: create switch based on deviation from column deviation.col
deviation.col: Character: column name for deviation
deviation.conditions: Character list: RHS evaluations of the same length as treat.levels
deviation.excused: Logical: whether deviations should be excused by deviation.excused_cols
deviation.excused_cols: Character list: excused columns for deviation switches
excused: Logical: in the case of censoring, whether there is an excused condition
excused.cols: List: list of column names for treatment switch excuses - should be the same length, and ordered the same as treat.level
fastglm.method: Integer: decomposition method for fastglm (1-QR, 2-Cholesky, 3-LDLT, 4-QR.FPIV)
followup.class: Logical: treat followup as a class, e.g. expands every time to it's own indicator column
followup.include: Logical: whether or not to include 'followup' and 'followup_squared' in the outcome model
followup.max: Numeric: maximum time to expand about, default is Inf (no maximum)
followup.min: Numeric: minimum time to expand aboud, default is -Inf (no minimum)
followup.spline: Logical: treat followup as a cubic spline
hazard: Logical: hazard error calculation instead of survival estimation
indicator.baseline: String: identifier for baseline variables in covariates, numerator, denominator - intended as an override
indicator.squared: String: identifier for squared variables in covariates, numerator, denominator - intended as an override
km.curves: Logical: Kaplan-Meier survival curve creation and data return
multinomial: Logical: whether to expect multilevel treatment values
ncores: Integer: number of cores to use in parallel processing, default is one less than system max
nthreads: Integer: number of threads to use for data.table processing
numerator: String: numerator covariates to the right hand side of a to formula object
parallel: Logical: define if the SEQuential process is run in parallel, default is FALSE
plot.colors: Character: Colors for output plot if km.curves = TRUE, defaulted to ggplot2 defaults
plot.labels: Character: Color labels for output plot if km.curves = TRUE in order e.g. c("risk.0", "risk.1")
plot.subtitle: Character: Subtitle for output plot if km.curves = TRUE
plot.title: Character: Title for output plot if km.curves = TRUE
plot.type: Character: Type of plot to create if km.curves = TRUE, available options are 'survival', 'risk', and 'inc' (in the case of censoring)
seed: Integer: starting seed
selection.first_trial: Logical: selects only the first eligible trial in the expanded dataset
selection.prob: Numeric: percent of total IDs to select for selection.random, should be bound [0, 1]
selection.random: Logical: randomly selects IDs with replacement to run analysis
subgroup: Character: Column name to stratify outcome models on
survival.max: Numeric: maximum time for survival curves, default is Inf (no maximum)
treat.level: List: treatment levels to compare
trial.include: Logical: whether or not to include 'trial' and 'trial_squared' in the outcome model
weight.eligible_cols: List: list of column names for indicator columns defining which weights are eligible for weight models - in order of treat.level
weight.lower: Numeric: weights truncated at lower end at this weight
weight.lag_condition: Logical: whether weights should be conditioned on treatment lag value
weight.p99: Logical: forces weight truncation at 1st and 99th percentile weights, will override provided weight.upper and weight.lower
weight.preexpansion: Logical: whether weighting should be done on pre-expanded data
weight.upper: Numeric: weights truncated at upper end at this weight
weighted: Logical: whether or not to preform weighted analysis, default is FALSE

Value

An object of class 'SEQopts'