SEQopts: Parameter Builder for SEQuential Model and Estimates
In SEQTaRget: Sequential Trial Emulation

SEQopts

R Documentation

Parameter Builder for SEQuential Model and Estimates

Description

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`