vaccine (version 1.3.0)

params_ce_np: Set parameters controlling nonparametric estimation of controlled effect curves

Description

This should be used in conjunction with est_ce to set parameters controlling nonparametric estimation of controlled effect curves; see examples.

Usage

params_ce_np(
  dir = "decr",
  edge_corr = FALSE,
  grid_size = list(y = 101, s = 101, x = 5),
  surv_type = "survML-G",
  density_type = "binning",
  density_bins = 15,
  deriv_type = "m-spline",
  convex_type = "GCM"
)

Value

A list of options.

Arguments

dir

One of c("decr", "incr"); controls the direction of monotonicity. If dir="decr", it is assumed that CR decreases as a function of the marker. If dir="incr", it is assumed that CR increases as a function of the marker.

edge_corr

Boolean. If TRUE, the "edge correction" is performed to adjust for bias near the marker lower limit (see references). It is recommended that the edge correction is only performed if there are at least (roughly) 10 events corresponding to the marker lower limit.

grid_size

A list with keys y, s, and x; controls the rounding of data values. Decreasing the grid size values results in shorter computation times, and increasing the values results in more precise estimates. If grid_size$s=101, this means that a grid of 101 equally-spaced points (defining 100 intervals) will be created from min(S) to max(S), and each S value will be rounded to the nearest grid point. For grid_size$y, a grid will be created from 0 to t_0, and then extended to max(Y). For grid_size$x, a separate grid is created for each covariate column (binary/categorical covariates are ignored).

surv_type

One of c("Cox", "survSL", "survML-G", "survML-L"); controls the method to use to estimate the conditional survival and conditional censoring functions. If type="Cox", a survival function based on a Cox proportional hazard model will be used. If type="survSL", the Super Learner method of Westling 2023 is used. If type="survML-G", the global survival stacking method of Wolock 2022 is used. If type="survML-L", the local survival stacking method of Polley 2011 is used.

density_type

One of c("binning", "parametric"); controls the method to use to estimate the density ratio f(S|X)/f(S).

density_bins

An integer; if density_type="binning", the number of bins to use. If density_bins=0, the number of bins will be selected via cross-validation.

deriv_type

One of c("m-spline", "linear"); controls the method to use to estimate the derivative of the CR curve. If deriv_type="linear", a linear spline is constructed based on the midpoints of the jump points of the estimated function (plus the estimated function evaluated at the endpoints), which is then numerically differentiated. deriv_type="m-spline" is similar to deriv_type="linear" but smooths the set of points (using the method of Fritsch and Carlson 1980) before differentiating.

convex_type

One of c("GCM", "CLS"). Whether the greatest convex minorant ("GCM") or convex least squares ("CLS") projection should be used in the smoothing of the primitive estimator Gamma_n. convex_type="CLS" is experimental and should be used with caution.

Examples

data(hvtn505)
dat <- load_data(time="HIVwk28preunblfu", event="HIVwk28preunbl", vacc="trt",
                 marker="IgG_V2", covariates=c("age","BMI","bhvrisk"),
                 weights="wt", ph2="casecontrol", data=hvtn505)
# \donttest{
ests_np <- est_ce(
  dat = dat,
  type = "NP",
  t_0 = 578,
  params_np = params_ce_np(edge_corr=TRUE, surv_type="survML-L")
)
# }