npORmissing: Nonparametric Targeted inference for the conditional odds...

In Larsvanderlaan/npOddsRatio: Semiparametric Targeted Maximum-Likelihood estimation of the conditional odds ratio in a partially-linear logistic-link regression model with post-treatment informative missingness.

Nonparametric Targeted inference for the conditional odds ratio with post-treatment informed outcome missingness.

Description

Nonparametric Targeted estimates and inference for the conditional odds ratio with a user-specified parametric working-model with 'post-treatment“ informative outcome missingness.. This version also allows for the outcome is missing-at-random conditional on Z,A,W. Note this version is totally nonparametric and requires no assumptions on the functional form of the conditional odds ratio for correct inference. The user-specified working model is used to find a best-approximation (relative to the working-model) of the true conditional odds ratio. This function can be viewed as a nonparametric version of the partially-linear logistic regression model where one uses the working model 'logit(P(Y=1|A,W)) = b* A f(W) + logit(P(Y=1|A=0,W)' for a user-specified parametric function 'f(W)' to approximate the true function. Nonparametrically correct and efficient inference is given for this best approximation of the conditional odds ratio.

Usage

npORMissing(
  working_formula = logOR ~ 1,
  W,
  A,
  Y,
 
    Z = stop("No Z variable given. Use spOR or npOR instead if the variable `Z` is not used"),
  Delta,
  weights = NULL,
  W_new = W,
  glm_formula_YZW = NULL,
  sl3_learner_YZW = NULL,
  glm_formula_Y0W = NULL,
  sl3_learner_Y0W = NULL,
  glm_formula_OR = NULL,
  sl3_learner_OR = NULL,
  glm_formula_A = NULL,
  sl3_learner_A = NULL,
  glm_formula_Delta = NULL,
  sl3_learner_Delta = NULL,
  sl3_learner_default = Lrnr_hal9001_custom$new(max_degree = 2, smoothness_orders = 1,
    num_knots = c(30, 10))
)

Arguments

working_formula	An working-model R formula object describing the functional form of the approximation of the conditional log odds ratio as a fnction of 'W'. #' This corresponds with 'f(W)' in the partially linear logistic-link model 'logit(P(Y=1|A,W)) = b*Af(W) + h(W)'.
W	A named matrix of baseline covariates
A	A binary vector with values in (0,1) encoding the treatment assignment
Y	A binary outcome variable with values in (0,1)
Z	A matrix of post-treatment variables that inform the missingness of the outcome 'Y'. This variable can be NULL if the missingness is only effected by 'A' and 'W'.
Delta	A binary vector that takes the value 1 if 'Y' is osberved/not-missing and 0 otherwise.
weights	An optional vector of weights for each observation. Use with caution. This can lead to invalid inferences if included naively.
W_new	An optional matrix of new values of the baseline covariates 'W' at which to predict odds ratio.
glm_formula_Y0W	(Not recommended). An optional R formula object describing the nuisance function 'logit(P(Y=1|A=0,W))'.
sl3_learner_Y0W	An optional tlverse/sl3 learner object used to estimate the nuisance (A=0) conditional outcome mean P(Y=1|A=0,W).
glm_formula_OR	(Not recommended). An optional R formula object describing the conditional odds ratio.
sl3_learner_OR	An optional tlverse/sl3 learner object used to estimate the true/nonparametric conditional odds ratio
glm_formula_A	(Not recommended). An optional R formula object describing the functional form of P(A=1|W). If provided, glm is used for the fitting. (Not recommended. This method allows for and works best with flexible machine-learning algorithms.)
sl3_learner_A	An optional tlverse/sl3 learner object used to estimate P(A=1|W). If both sl3_learner_A and glm_formula_A are not provided, a default learner is used (Lrnr_hal9001).
glm_formula_Delta	(Not recommended). An optional R formula object describing the functional form of P(Delta=1|A,W) to fit with glm. If provided, it is estimated using glm. (Not recommended. This method allows for and works best with flexible machine-learning algorithms.)
sl3_learner_Delta	An optional tlverse/sl3 learner object used to estimate P(Delta=1|A,W).
sl3_learner_default	A default sl3 Learner to be used if neither a glm formula or sl3 learner is provided for one of the nuisance functions. By default, Lrnr_hal9001 is used.
glm_formula_YZ	A(Not recommended). An optional R formula object describing the functional form of the full conditional outcome mean P(Y=1|Z,A,W). If provided, it is estimated using glm. (Not recommended. This method allows for and works best with flexible machine-learning algorithms.)
sl3_learner_YZ	An optional tlverse/sl3 learner object used to estimate the full conditional outcome mean P(Y=1|Z,A,W).

Larsvanderlaan/npOddsRatio documentation built on May 3, 2022, 12:05 p.m.