Title: Asymptotic Covariance Matrix of Regression Models for Multiple Outcomes
Version: 0.4
Description: Regression models can be fitted for multiple outcomes simultaneously. This package computes estimates of parameters across fitted models and returns the matrix of asymptotic covariance. Various applications of this package, including CUPED (Controlled Experiments Utilizing Pre-Experiment Data), multiple comparison adjustment, are illustrated.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.1
Imports: dplyr, momentfit, numDeriv, stringr, survival
Suggests: asaur, coin, ggplot2, iBST, invGauss, JM, joint.Cox, knitr, mvtnorm, pec, randomForestSRC, rmarkdown, survminer, tidyr
VignetteBuilder: knitr
Depends: R (≥ 2.10)
LazyData: true
NeedsCompilation: no
Packaged: 2024-05-30 14:10:13 UTC; zhhan
Author: Han Zhang [aut, cre]
Maintainer: Han Zhang <zhangh.ustc@gmail.com>
Repository: CRAN
Date/Publication: 2024-05-30 15:00:03 UTC

ACTG 320 Clinical Trial Dataset

Description

actg dataset from Hosmer et al.

Format

A data frame

id

Identification Code

time

Time to AIDS diagnosis or death (days).

censor

Event indicator. 1 = AIDS defining diagnosis, 0 = Otherwise.

time_d

Time to death (days)

censor_d

Event indicator for death (only). 1 = Death, 0 = Otherwise.

tx

Treatment indicator. 1 = Treatment includes IDV, 0 = Control group.

txgrp

Treatment group indicator. 1 = ZDV + 3TC. 2 = ZDV + 3TC + IDV. 3 = d4T + 3TC. 4 = d4T + 3TC + IDV.

strat2

CD4 stratum at screening. 0 = CD4 <= 50. 1 = CD4 > 50.

sex

0 = Male. 1 = Female.

raceth

Race/Ethnicity. 1 = White Non-Hispanic. 2 = Black Non-Hispanic. 3 = Hispanic. 4 = Asian, Pacific Islander. 5 = American Indian, Alaskan Native. 6 = Other/unknown.

ivdrug

IV drug use history. 1 = Never. 2 = Currently. 3 = Previously.

hemophil

Hemophiliac. 1 = Yes. 0 = No.

karnof

Karnofsky Performance Scale. 100 = Normal; no complaint no evidence of disease. 90 = Normal activity possible; minor signs/symptoms of disease. 80 = Normal activity with effort; some signs/symptoms of disease. 70 = Cares for self; normal activity/active work not possible.

cd4

Baseline CD4 count (Cells/Milliliter).

priorzdv

Months of prior ZDV use (months).

age

Age at Enrollment (years).

Source

ftp://ftp.wiley.com/public/sci_tech_med/survival

References

Hosmer, D.W. and Lemeshow, S. and May, S. (2008) Applied Survival Analysis: Regression Modeling of Time to Event Data: Second Edition, John Wiley and Sons Inc., New York, NY

Examples

data(actg)

Extract Model Coefficients

Description

coef is a generic function.

Usage

## S3 method for class 'multipleOutcomes'
coef(object, model_index = NULL, ...)

Arguments

object

an object returned by multipleOutcomes().

model_index

NULL if displaying coefficients of all fitted models; otherwise, an integer indicating the fitted model.

...

for debugging only

Value

a vector of coefficient estimates

Fitting Regression Models for Multiple Outcomes and Returning the Matrix of Covariance

Description

multipleOutcomes can fit different types of models for multiple outcomes simultaneously and return model parameters and variance-covariance matrix for further analysis.

Usage

multipleOutcomes(..., family, data, data_index = NULL, score_epsilon = 1e-06)

Arguments

...

formulas of models to be fitted, or moment functions for gmm.

family

a character vector of families to be used in the models. Currently only gaussian, binomial, coxph and gmm are supported. long for longitudinal data may be supported in the future. family can be of length 1 if all models are fitted in the same family; otherwise family should be specified for each of the models in ....

data

a data frame if all models are fitted on the same dataset; otherwise a list of data frames for fitting models in .... Note that a dataset can be used to fit multiple models, thus, length(data) is unnecessary to be equal to the number of models in .... The row names in a data frame are treated as sample IDs. Consequently, for any two records in different data frames that correspond to the same sample, their row names should be consistent.

data_index

NULL if data is a data frame; otherwise, a vector in integer specifying mapping a model in ... to a data frame in data (a list).

score_epsilon

whatever.

Value

It returns an object of class "multipleOutcomes", which is a list containing the following components:

coefficients an unnamed vector of coefficients of all fitted models. Use id_map for variable mapping.
mcov a unnamed matrix of covariance of coefficients. Use id_map for variable mapping.
id_map a list mapping the elements in coefficients and mcov to variable names.
n_shared_sample_sizes a matrix of shared sample sizes between datasets being used to fit the models.
call the matched call.

Examples

## More examples can be found in the vignettes.
library(mvtnorm)
genData <- function(seed = NULL){

  set.seed(seed)
  n <- 400
  sigma <- matrix(c(1, .6, .6, 1), 2)
  x <- rmvnorm(n, sigma = sigma)
  gam <- c(.1, -.2)
  z <- rbinom(n, 1, plogis(1-1/(1+exp(-.5+x%*%gam+.1*rnorm(n)))))

  bet <- c(-.2,.2)
  #y <- rbinom(n, 1, plogis(1-1/(1+exp(-.5+x%*%bet + .2*z-.3*rnorm(n)))))
  y <- -.5+x%*%bet + .2*z-.3*rnorm(n)

  data.frame(y = y, z = z, x1 = x[, 1], x2 = x[, 2])

}

dat <- genData(123456)
dat1 <- head(dat,200)
dat2 <- tail(dat,200)
## fitting four models simultaneously.
fit <-
  multipleOutcomes(
    y ~ z + x1 - 1,
    z ~ x1 + x2,
    z ~ x1 - 1,
    y ~ x2,
    ## z can be fitted with a linear or logistic regression
    family = c('gaussian', 'binomial', 'gaussian','gaussian'),
    data = list(dat1, dat2),
    ## each dataset is used to fit two models
    data_index = c(1, 1, 2, 2)
  )

  ## unnamed coefficients of all model parameters
  coef(fit)

  ## named coefficients of a specific model
  coef(fit, 2)

  ## unnamed covariance matrix of all model parameters
  vcov(fit)

  ## named covariance matrix of a specific model
  vcov(fit, 1)

  ## summary of all parameter estimates
  summary(fit)

  ## summary of parameters in a specific model
  summary(fit, 4)

Prognostic Variables Assisted Treatment Effect Detection

Description

pated is a wrapper function of multipleOutcomes for testing treatment effect in randomized clinical trials. It assumes that prognostic variables are fully randomized. This assumption can help enhancing statistical power of conventional approaches in detecting the treatment effect. Specifically, the sensitivity of the conventional models specified in ... are improved by pated.

Usage

pated(..., family, data)

Arguments

...

formulas of models to be fitted, or moment functions for gmm.

family

a character vector of families to be used in the models. Currently only gaussian, binomial, coxph and gmm are supported. long for longitudinal data may be supported in the future. family can be of length 1 if all models are fitted in thesame family; otherwise family should be specified for each of the models in ....

data

a data frame if all models are fitted on the same dataset; otherwise a list of data frames for fitting models in .... Note that a dataset can be used to fit multiple models, thus, length(data) is unnecessary to be equal to the number of models in .... The row names in a data frame are treated as sample IDs. Consequently, for any two records in different data frames that correspond to the same sample, their row names should be consistent.

Value

a data frame of testing results.

Examples

# see vignettes

Title Summarize an Analysis of Multiple Outcomes.

Description

Summarize an analysis of multiple outcomes.

Usage

## S3 method for class 'summary.multipleOutcomes'
print(x, ...)

Arguments

x

an object returned by multipleOutcomes().

...

for debugging only.

Value

an invisible object.

Examples

## no example

Object Summaries

Description

summary method for class multipleOutcomes.

Usage

## S3 method for class 'multipleOutcomes'
summary(object, model_index = NULL, ...)

Arguments

object

an object returned by multipleOutcomes().

model_index

NULL if displaying summary of all fitted models; otherwise, an integer indicating the fitted model.

...

for debugging only

Value

a list

Calculate Variance-Covariance Matrix for a Fitted Model Object

Description

Returns the variance-covariance matrix of the main parameters of fitted model objects. The "main" parameters of models correspond to those returned by coef.

Usage

## S3 method for class 'multipleOutcomes'
vcov(object, model_index = NULL, ...)

Arguments

object

an object returned by multipleOutcomes().

model_index

NULL if displaying covariance matrix of all fitted models; otherwise, an integer indicating the fitted model.

...

for debugging only

Value

a matrix of covariance of all estimates