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,
  followup.class = FALSE,
  followup.include = TRUE,
  followup.max = Inf,
  followup.min = 0,
  followup.spline = FALSE,
  followup.spline.df = 4L,
  glm.package = "fastglm",
  hazard = FALSE,
  indicator.baseline = "_bas",
  indicator.squared = "_sq",
  km.curves = FALSE,
  multinomial = FALSE,
  ncores = availableCores(omit = 1L),
  nthreads = getDTthreads(),
  numerator = NA,
  parallel = FALSE,
  parglm.control = NULL,
  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, 4L-full-pivoted QR, 5L-Bidiagonal Divide and Conquer SVD), default is 2L
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).
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. Note that when bootstrap = TRUE only the main fit uses "parglm": the bootstrap refits always use "fastglm", warm-started from the main fit's coefficients, which is faster per resample than parglm's per-fit thread setup.
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
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.
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; because the follow-up grid is zero-indexed (followup runs 0:survival.max), this final time is survival.max + 1, so e.g. survival.max = 120 reports a row at 121. Default NA reports only the final follow-up time.
seed: Integer: starting seed; the default NULL draws a single random integer when SEQopts() is called, so set this explicitly for reproducible bootstrap results
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. Truncation is applied only to the weights used to fit the outcome model; the weights reported in weight.statistics and in the returned data (when data.return = TRUE) are the untruncated values.
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. The percentiles are taken from the untruncated weight distribution (as reported in weight.statistics), and as with weight.lower/weight.upper the truncation affects only the weights used to fit the outcome model.
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. As with weight.lower, truncation affects only the weights used to fit the outcome model, not those reported in weight.statistics or the returned data.
weighted: Logical: whether or not to perform weighted analysis, default is FALSE

Value

An object of class 'SEQopts'