Parameter Builder for SEQuential Model and Estimates

Usage

SEQopts(
  bootstrap = FALSE,
  bootstrap.nboot = 100,
  bootstrap.sample = 0.8,
  bootstrap.CI = 0.95,
  bootstrap.CI_method = "se",
  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),
  expand.only = FALSE,
  fastglm.method = 2L,
  glm.package = "fastglm",
  parglm.control = NULL,
  followup.class = FALSE,
  followup.include = TRUE,
  followup.max = Inf,
  followup.min = 0,
  followup.spline = FALSE,
  followup.spline.df = 4L,
  hazard = FALSE,
  indicator.baseline = "_bas",
  indicator.squared = "_sq",
  km.curves = FALSE,
  multinomial = FALSE,
  ncores = availableCores(omit = 1L),
  nthreads = getDTthreads(),
  numerator = NA,
  parallel = FALSE,
  plot.colors = c("#F8766D", "#00BFC4", "#555555"),
  plot.labels = NA,
  plot.subtitle = NA,
  plot.title = NA,
  plot.type = "survival",
  risk.times = NA,
  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,
  visit = NA,
  visit.denominator = NA,
  visit.numerator = NA,
  weight.eligible_cols = c(),
  weight.lower = 0,
  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, default is 100
bootstrap.sample: Numeric: percentage of data to use when bootstrapping, should be in [0, 1], default is 0.8
bootstrap.CI: Numeric: defines the confidence interval after bootstrapping, default is 0.95 (95% CI)
bootstrap.CI_method: Character: selects which way to calculate bootstraps confidence intervals ("se", "percentile"), default is "se"
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, default is FALSE
denominator: String: denominator covariates to the right hand side of a formula object
deviation: Logical: create switch based on deviation from column deviation.col, default is FALSE
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, default is FALSE
deviation.excused_cols: Character list: excused columns for deviation switches
excused: Logical: in the case of censoring, whether there is an excused condition, default is FALSE
excused.cols: List: list of column names for treatment switch excuses - should be the same length, and ordered the same as treat.level
expand.only: Logical: if TRUE, SEQuential() returns the expanded data.table immediately after expansion and skips weighting, outcome modelling and survival/risk steps. Useful when you only need the expanded dataset (e.g. to inspect or store separately). Default is FALSE
fastglm.method: Integer: decomposition method for fastglm (0L-column-pivoted QR, 1L-unpivoted QR, 2L-LLT Cholesky, 3L-LDLT Cholesky), default is 2L
glm.package: Character: package to use for fitting GLMs, either "fastglm" (default) or "parglm". When "parglm" is selected the nthreads option controls the number of threads passed to parglm::parglm.fit(). For most realistic SEQTaRget workloads (expanded datasets up to approximately a few million rows) "fastglm" is faster; "parglm" may help only on substantially larger datasets where the parallel chunking outweighs its setup overhead.
parglm.control: A control object from parglm::parglm.control() to pass to parglm::parglm.fit(). Only used when glm.package = "parglm". Defaults to parglm::parglm.control(method = "FAST"). If you encounter a chol(): decomposition failed error (e.g. with near-singular model matrices on large datasets), pass parglm.control = parglm::parglm.control(method = "LAPACK") to use the more numerically stable QR decomposition instead, or switch to using the fastglm backend.
followup.class: Logical: treat followup as a class, e.g. expands every time to it's own indicator column, default is FALSE
followup.include: Logical: whether or not to include 'followup' and 'followup_squared' in the outcome model, default is TRUE
followup.max: Numeric: maximum time to expand about, default is Inf (no maximum)
followup.min: Numeric: minimum follow-up time since trial enrollment to include, must be non-negative, default is 0
followup.spline: Logical: treat followup as a natural cubic spline (splines::ns()), default is FALSE
followup.spline.df: Integer: degrees of freedom passed to splines::ns() when followup.spline = TRUE. With df = k, ns() places k - 1 interior knots at quantiles of followup. Must be >= 1; df = 1 is equivalent to a linear term and is generally not what you want. Default is 4 (3 interior knots).
hazard: Logical: hazard error calculation instead of survival estimation, default is FALSE
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, default is FALSE
multinomial: Logical: whether to expect multilevel treatment values, default is FALSE
ncores: Integer: number of cores to use in parallel processing, default is one less than system max, see parallelly::availableCores()
nthreads: Integer: number of threads to use for data.table processing, default is data.table::getDTthreads()
numerator: String: numerator covariates to the right hand side of a 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' (the default), 'risk', and 'inc' (in the case of censoring)
risk.times: Numeric vector: follow-up times (in the data's follow-up units) at which to report risk difference and risk ratio when km.curves = TRUE. Each requested time is snapped to the latest available follow-up at or before it. The final follow-up time is always included. Default NA reports only the final follow-up time.
seed: Integer: starting seed
selection.first_trial: Logical: selects only the first eligible trial in the expanded dataset, default FALSE
selection.prob: Numeric: percent of total IDs to select for selection.random, should be bound [0, 1], default is 0.8
selection.random: Logical: randomly selects IDs with replacement to run analysis, default FALSE
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, default is c(0, 1)
trial.include: Logical: whether or not to include 'trial' and 'trial_squared' in the outcome model, default is TRUE
visit: String: column name for visit indicator variable, e.g. "visit"
visit.denominator: String: visit denominator covariates to the right hand side of a formula object
visit.numerator: String: visit numerator covariates to the right hand side of a formula object
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: IPCW weights truncated at this lower bound, must be non-negative, default is 0
weight.lag_condition: Logical: whether weights should be conditioned on treatment lag value, default TRUE
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, default TRUE
weight.upper: Numeric: weights truncated at upper end at this weight, default is Inf
weighted: Logical: whether or not to perform weighted analysis, default is FALSE

Value

An object of class 'SEQopts'