Check arguments Catered to all types of outcome Apply at the beginning of pmcount(), cvcount(), drcount.inference(), catefitsurv(), catecvsurv(), and drsurv.inference()

Check arguments Catered to all types of outcome Apply at the beginning of pmcount(), cvcount(), drcount.inference(), catefitsurv(), catecvsurv(), and drsurv.inference()

Usage

arg.checks(
  fun,
  response,
  data,
  followup.time = NULL,
  tau0 = NULL,
  surv.min = NULL,
  ipcw.method = NULL,
  ps.method,
  minPS,
  maxPS,
  higher.y = NULL,
  score.method = NULL,
  abc = NULL,
  prop.cutoff = NULL,
  prop.multi = NULL,
  train.prop = NULL,
  cv.n = NULL,
  error.max = NULL,
  max.iter = NULL,
  initial.predictor.method = NULL,
  tree.depth = NULL,
  n.trees.rf = NULL,
  n.trees.boosting = NULL,
  B = NULL,
  Kfold = NULL,
  plot.gbmperf = NULL,
  error.maxNR = NULL,
  max.iterNR = NULL,
  tune = NULL,
  n.boot = NULL,
  plot.boot = NULL,
  interactions = NULL
)

Arguments

fun

A function for which argument check is needed; "catefit" for catefitcount() and catefitsurv(), "crossv" for catecvcount() and catecvsurv(), and "drinf" for drcount.inference() and drsurv.inference(). No default.

response

The type of response. Always 'survival' for this function.

data

A data frame containing the variables in the outcome and propensity score models; a data frame with n rows (1 row per observation).

followup.time

Follow-up time, interpreted as the potential censoring time. If the potential censoring time is known, followup.time is the name of a corresponding column in the data. Otherwise, set followup.time == NULL.

tau0

The truncation time for defining restricted mean time lost.

surv.min

Lower truncation limit for probability of being censored (positive and very close to 0).

ipcw.method

The censoring model. Allowed values are: 'breslow' (Cox regression with Breslow estimator of the baseline survivor function), 'aft (exponential)', 'aft (weibull)', 'aft (lognormal)' or 'aft (loglogistic)'. Default is 'breslow'.

ps.method

A character value for the method to estimate the propensity score. Allowed values include one of: 'glm' for logistic regression with main effects only (default), or 'lasso' for a logistic regression with main effects and LASSO penalization on two-way interactions (added to the model if interactions are not specified in ps.model). Relevant only when ps.model has more than one variable.

minPS

A numerical value (in `[0, 1]`) below which estimated propensity scores should be truncated. Default is 0.01.

maxPS

A numerical value (in `(0, 1]`) above which estimated propensity scores should be truncated. Must be strictly greater than minPS. Default is 0.99.

higher.y

A logical value indicating whether higher (TRUE) or lower (FALSE) values of the outcome are more desirable. Default is TRUE.

score.method

A vector of one or multiple methods to estimate the CATE score. Allowed values are: 'boosting', 'poisson', 'twoReg', 'contrastReg', 'negBin'. Default specifies all 5 methods.

abc

A logical value indicating whether the area between curves (ABC) should be calculated at each cross-validation iterations, for each score.method. Default is TRUE.

prop.cutoff

A vector of numerical values (in `(0, 1]`) specifying percentiles of the estimated log CATE scores to define nested subgroups. Each element represents the cutoff to separate observations in nested subgroups (below vs above cutoff). The length of prop.cutoff is the number of nested subgroups. An equally-spaced sequence of proportions ending with 1 is recommended. Default is seq(0.5, 1, length = 6).

prop.multi

A vector of numerical values (in `[0, 1]`) specifying percentiles of the estimated log CATE scores to define mutually exclusive subgroups. It should start with 0, end with 1, and be of length(prop.multi) > 2. Each element represents the cutoff to separate the observations into length(prop.multi) - 1 mutually exclusive subgroups. Default is c(0, 1/3, 2/3, 1).

train.prop

A numerical value (in `(0, 1)`) indicating the proportion of total data used for training. Default is 3/4.

cv.n

A positive integer value indicating the number of cross-validation iterations. Default is 10.

error.max

A numerical value > 0 indicating the tolerance (maximum value of error) for the largest standardized absolute difference in the covariate distributions or in the doubly robust estimated rate ratios between the training and validation sets. This is used to define a balanced training-validation splitting. Default is 0.1.

max.iter

A positive integer value indicating the maximum number of iterations when searching for a balanced training-validation split. Default is 5,000.

initial.predictor.method

A character vector for the method used to get initial outcome predictions conditional on the covariates in cate.model in score.method = 'twoReg' and 'contrastReg'. Allowed values include one of 'randomForest', 'boosting' and 'logistic' (fastest). Default is 'randomForest'.

tree.depth

A positive integer specifying the depth of individual trees in boosting (usually 2-3). Used only if score.method = 'boosting' or if score.method = 'twoReg' or 'contrastReg' and initial.predictor.method = 'boosting'. Default is 2.

n.trees.rf

A positive integer specifying the maximum number of trees in random forest. Used if score.method = 'ranfomForest' or if initial.predictor.method = 'randomForest' with score.method = 'twoReg' or 'contrastReg'. Default is 1000.

n.trees.boosting

A positive integer specifying the maximum number of trees in boosting (usually 100-1000). Used if score.method = 'boosting' or if initial.predictor.method = 'boosting' with score.method = 'twoReg' or 'contrastReg'. Default is 150.

B

A positive integer specifying the number of time cross-fitting is repeated in score.method = 'twoReg' and 'contrastReg'. Default is 3.

Kfold

A positive integer specifying the number of folds (parts) used in cross-fitting to partition the data in score.method = 'twoReg' and 'contrastReg'. Default is 6.

plot.gbmperf

A logical value indicating whether to plot the performance measures in boosting. Used only if score.method = 'boosting' or if score.method = 'twoReg' or 'contrastReg' and initial.predictor.method = 'boosting'. Default is TRUE.

error.maxNR

A numerical value > 0 indicating the minimum value of the mean absolute error in Newton Raphson algorithm. Used only if score.method = 'contrastReg'. Default is 0.001.

max.iterNR

A positive integer indicating the maximum number of iterations in the Newton Raphson algorithm. Used only if score.method = 'contrastReg'. Default is 150.

tune

A vector of 2 numerical values > 0 specifying tuning parameters for the Newton Raphson algorithm. tune[1] is the step size, tune[2] specifies a quantity to be added to diagonal of the slope matrix to prevent singularity. Used only if score.method = 'contrastReg'. Default is c(0.5, 2).

n.boot

A numeric value indicating the number of bootstrap samples used. This is only relevant if inference = TRUE. Default is 500.

plot.boot

A logic value indicating whether histograms of the bootstrapped log(rate ratio) should be produced at every n.boot/10-th iteration and whether the final histogram should be outputted. Default is FALSE.

interactions

A logical value indicating whether the outcome model should assume interactions between x and trt. If TRUE, interactions will be assumed only if at least 10 patients received each treatment option. Default is TRUE.

Value

Nothing. Will stop if arguments are incorrect.