Package {multipleOutcomes}

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 'jointCovariance'
coef(object, model_index = NULL, ...)

Arguments

Value

a vector of coefficient estimates

Creating Objects of Proportional Hazards Regression Model

Description

coxph_ is a wrapper function of survival::coxph to create an object to be passed into jointCovariance, the main function of this package through its argument .... The object defines how a proportional hazard model would be fitted.

Usage

coxph_(formula, data_index = 1)

Arguments

Details

Not all arguments of survival::coxph are supported in coxph_ due to the complexity in handling environment and scope, which is particularly difficult for arguments like weights, subset, etc.

Creating Objects of Generalized Estimation Equation Model

Description

gee_ is a wrapper function of gee::gee to create an object to be passed into jointCovariance, the main function of this package through its argument .... The object defines how a GEE model would be fitted.

This package does not import the package gee. Instead, codes of gee are modified and integrated to compute score and information matrix. Thus, users does not need to install the package gee to use this package.

Usage

gee_(formula, family, corstr, R = NULL, b = NULL, Mv = 1, data_index = 1)

Arguments

Details

Not all arguments of stats::gee are supported in gee_ due to the complexity in handling environment and scope, which is particularly difficult for arguments like subset, etc.

Creating Objects of Generalized Linear Models

Description

glm_ is a wrapper function of stats::glm to create an object to be passed into jointCovariance, the main function of this package through its argument .... The object defines how a GLM model would be fitted.

Usage

glm_(formula, family, data_index = 1)

Arguments

Details

Not all arguments of stats::glm are supported in glm_ due to the complexity in handling environment and scope, which is particularly difficult for arguments like weights, subset, etc.

Rectal Indomethacin for Prevention of Post-ERCP Pancreatitis

Description

Patient-level data from a multi-center, randomized, double-blind, placebo-controlled 2-arm trial (n = 602) of rectal indomethacin (100 mg) versus placebo to prevent post-ERCP pancreatitis in high-risk patients, as reported by Elmunzer, Higgins, et al. (2012) in the New England Journal of Medicine.

This dataset was originally collected, cleaned, reformatted, and released for public teaching and research use by Dr. Peter D. R. Higgins in the medicaldata R package as indo_rct. The version shipped here is redistributed in support of the worked examples in this package. The variable definitions below follow medicaldata; note that a few columns are stored as numeric (rather than factor) in this copy. Users who need the authoritative copy or accompanying documentation should consult medicaldata.

Format

A data frame with 602 observations on the following 33 variables:

id

Subject identifier (numeric); leading digit indicates center. Range 1001–4003.

site

Study site (factor, 4 levels): 1_UM = University of Michigan, 2_IU = Indiana University, 3_UK = University of Kentucky, 4_Case = Case Western Reserve University.

age

Age in years (numeric), range 19–90.

risk

Risk score for post-ERCP pancreatitis (numeric), range 1–5.5.

gender

Sex (factor): 1_female, 2_male.

outcome

Primary outcome: post-ERCP pancreatitis (numeric, 1 = yes, 0 = no).

sod

Sphincter of Oddi dysfunction present (factor): 0_no, 1_yes.

pep

History of prior post-ERCP pancreatitis (factor): 0_no, 1_yes.

recpanc

History of recurrent pancreatitis (factor): 0_no, 1_yes.

psphinc

Pancreatic sphincterotomy performed (factor): 0_no, 1_yes.

precut

Sphincter pre-cut needed to enter papilla (factor): 0_no, 1_yes.

difcan

Cannulation of papilla was difficult (factor): 0_no, 1_yes.

pneudil

Pneumatic dilation of papilla performed (factor): 0_no, 1_yes.

amp

Ampullectomy performed (factor): 0_no, 1_yes.

paninj

Contrast injected into pancreas (factor): 0_no, 1_yes.

acinar

Pancreas appeared to have acinarization on imaging (factor): 0_no, 1_yes.

brush

Brushings taken from pancreatic duct (factor): 0_no, 1_yes.

asa81

Aspirin used at 81 mg per day (factor with 3 levels): 0_no, 1_yes, and a third level retained from the source coding.

asa325

Aspirin used at 325 mg per day (factor with 3 levels): 0_no, 1_yes, and a third level retained from the source coding.

asa

Aspirin used at any dose (factor with 3 levels): 0_no, 1_yes, and a third level retained from the source coding.

prophystent

Pancreatic duct stent placed per endoscopist judgment (factor): 0_no, 1_yes.

therastent

Pancreatic duct stent placed to treat narrowing (factor): 0_no, 1_yes.

pdstent

Pancreatic duct stent placed for any reason (factor): 0_no, 1_yes.

sodsom

Sphincter of Oddi manometry performed (factor): 0_no, 1_yes.

bsphinc

Biliary sphincterotomy performed (factor): 0_no, 1_yes.

bstent

Biliary stent placed to relieve obstruction (factor): 0_no, 1_yes.

chole

Choledocholithiasis present (factor): 0_no, 1_yes.

pbmal

Biliary duct or pancreatic malignancy found (factor): 0_no, 1_yes.

train

Trainee participated in ERCP (factor): 0_no, 1_yes.

status

Patient status (factor): 0_inpatient, 1_outpatient.

type

Sphincter of Oddi dysfunction type (factor): 0_no SOD, 1_type 1, 2_type 2, 3_type 3.

rx

Treatment assignment (numeric, 1 = indomethacin, 0 = placebo).

bleed

Reportable gastrointestinal bleeding (numeric, coded 1 = no, 2 = yes; NA when not assessed).

Source

Higgins, P. D. R. medicaldata: Data Package for Medical Datasets. R package, dataset indo_rct. https://CRAN.R-project.org/package=medicaldata

References

Elmunzer BJ, Higgins PDR, Saini SD, et al. A randomized trial of rectal indomethacin to prevent post-ERCP pancreatitis. New England Journal of Medicine 2012; 366(15):1414–1422. doi:10.1056/NEJMoa1111103

Examples

data(indo)

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

Description

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

Usage

jointCovariance(..., data, nboot = 0, compute_cov = TRUE, seed = NULL)

Arguments

Value

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

Examples

## More examples can be found in the vignettes.
library(survival)
library(mvtnorm)
library(tidyr)
genData <- function(seed = NULL){
  
  set.seed(seed)
  n <- 300
  sigma <- matrix(.7, 4, 4)
  diag(sigma) <- 1
  v <- rmvnorm(n, sigma = sigma)
  x1 <- v[, 1]
  x2 <- v[, 2]
  z1 <- (v[, 3] > 0) + 0
  z2 <- v[, 4]
  
  trt <- rbinom(n, 1, .5)
  
  bet <- c(-.3,.3)
  y <- -log(runif(n))/
    exp(-.3 * x1 + .3 * x2 + z1 * .5 - z2 * .3 + .1 * trt + rnorm(n))
  
  z1[sample.int(n, 50)] <- NA
  z2[sample.int(n, 50)] <- NA
  x1[sample.int(n, 50)] <- NA
  x2[sample.int(n, 50)] <- NA
  death <- ifelse(y > 2, 0, 1)
  y[y > 2] <- 2
  
  pid <- paste0('pid-', 1:n)
  ret <- data.frame(
    y = y, trt = trt, 
    z1 = z1, z2 = z2, 
    x1 = x1, x2 = x2, 
    death, pid)
  ret
}

dat1 <- genData()

## create a dataset with repeated measurements x
dat2 <- dat1 %>% pivot_longer(c(x1, x2), names_to='visit', values_to='x') %>% 
  dplyr::select(x, trt, visit, pid) %>% as.data.frame()

dat2$visit <- as.factor(dat2$visit)
dat2$pid <- as.factor(dat2$pid)

fit <- jointCovariance(
  coxph_(Surv(time = y, event = death) ~ trt, data_index = 1),
  logrank_(Surv(time = y, event=death) ~ trt, data_index = 1),
  glm_(z1 ~ trt, family = 'binomial', data_index = 1), 
  glm_(z2 ~ trt, family = 'gaussian', data_index = 1), 
  mmrm_(x ~ trt + us(visit | pid), reml = TRUE, data_index = 2), 
  gee_(x ~ trt, family = 'gaussian', corstr = 'independence', data_index = 2), 
  data = list(dat1, dat2))

fit


bfit <-jointCovariance(
  coxph_(Surv(time=y, event=death) ~ trt, data_index = 1),
  logrank_(Surv(time=y, event=death) ~ trt, data_index = 1),
  glm_(z1 ~ trt, family = 'binomial', data_index = 1),
  glm_(z2 ~ trt, family = 'gaussian', data_index = 1),
  mmrm_(x ~ trt + us(visit | pid), reml = TRUE, data_index = 2),
  gee_(x ~ trt, family = 'gaussian', corstr = 'independence', data_index = 2),
  data = list(dat1, dat2), nboot = 10)

summary(bfit)

## km_() and quantile_() require nboot > 0 because they have no
## closed-form score. compute_cov is forced to FALSE for km_().
## When all models share one dataset, `data_index` and `list(...)`
## can be omitted.
kfit <- jointCovariance(
  km_(Surv(time = y, event = death) ~ trt, conf_type = 'log',
      times = c(0.5, 1, 1.5)),
  glm_(z1 ~ trt, family = 'binomial'),
  data = dat1, nboot = 30, seed = 1)

qfit <- jointCovariance(
  quantile_(y ~ trt, probs = c(0.25, 0.5, 0.75)),
  glm_(z2 ~ trt, family = 'gaussian'),
  data = dat1, nboot = 30, seed = 1)

Creating Objects of Kaplan-Meier Curve

Description

km_ is a wrapper function creating an object of Kaplan-Meier curve to be passed into jointCovariance, the main function of this package through its argument .... The object defines how a Kaplan-Meier curve would be fitted.

Usage

km_(formula, times = NULL, conf_type, data_index = 1)

Arguments

Details

Usually, g-transformation is applied to the survival probability S(t) to obtain pointwise confidence interval of a Kaplan-Meier curve. This can be achieved by specifying conf_type. For identity transformation, use conf_type = "plain".

This function can only be used with jointCovariance when the bootstrap method is used to estimate variance-covariance matrix of multiple outcome models.

Creating Objects of Logrank Test

Description

logrank_ is a wrapper function survival::coxph to create an object to be passed into jointCovariance, the main function of this package through its argument .... Logrank test is the score test under the proportional hazards regression model. The object defines how a logrank test would be computed.

Usage

logrank_(formula, ties = c("efron", "breslow", "exact"), data_index = 1)

Arguments

Details

Not all arguments of survival::coxph are supported in logrank_ due to the complexity in handling environment and scope, which is particularly difficult for arguments like weights, subset, etc.

Creating Objects of Mixed Models for Repeated Measures

Description

mmrm_ is a wrapper function of mmrm::mmrm to create an object to be passed into jointCovariance, the main function of this package through its argument .... The object defines how a MMRM model would be fitted.

Usage

mmrm_(
  formula,
  covariance = NULL,
  reml = TRUE,
  control = mmrm::mmrm_control(...),
  ...,
  data_index = 1
)

Arguments

Details

The argument weights of mmrm::mmrm is supported in mmrm_ due to the complexity in handling environment and scope.

Please always refer to help document of mmrm::mmrm before using mmrm_. For example, time variable and observation ID must be factor variables in some cases, otherwise error may be prompted. Users can call mmrm::mmrm using the same arguments being passed to mmrm_ to check validity.

Prognostic Variables Assisted Treatment Effect Detection

Description

pated is a wrapper function of jointCovariance 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(
  ...,
  data,
  nboot = 0,
  compute_cov = TRUE,
  seed = NULL,
  transform = "identity"
)

Arguments

Value

a data frame of testing results.

Examples

## More examples can be found in the vignettes.
library(survival)
library(mvtnorm)

genData <- function(seed = NULL){
  set.seed(seed)
  n <- 300
  sigma <- matrix(c(1, .6, .6, 1), 2)
  x <- rmvnorm(n, sigma = sigma)
  z1 <- rbinom(n, 1, .6)
  z2 <- rnorm(n)
  trt <- rbinom(n, 1, .5)

  bet <- c(-.2, .2)
  y <- -.5 + x %*% bet + z1 * .3 - z2 * .1 + .1 * trt - .1 * rnorm(n)
  death <- rbinom(n, 1, .8)
  data.frame(
    y = as.numeric(y), trt = trt,
    z1 = z1, z2 = z2,
    x1 = x[, 1], x2 = x[, 2],
    death, pid = paste0('s-', seq_len(n))
  )
}

dat <- genData(seed = 31415926)

## `data_index` defaults to 1 in every spec constructor and a single
## data.frame is auto-wrapped into a list, so neither needs spelling out
## when all models are fitted on the same dataset.
fit <-
  pated(
    coxph_(Surv(time = y, event = death) ~ trt),
    glm_(z1 ~ trt, family = 'binomial'),
    glm_(z2 ~ trt, family = 'gaussian'),
    glm_(x1 ~ trt, family = 'gaussian'),
    glm_(x2 ~ trt, family = 'gaussian'),
    data = dat
  )

fit

Plot PATED Analysis Results

Description

Plot PATED Analysis Results

Usage

## S3 method for class 'pated'
plot(x, ...)

Arguments

Value

NULL

Title Summarize an Analysis of Multiple Outcomes.

Description

Summarize an analysis of multiple outcomes.

Usage

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

Arguments

Value

an invisible object.

Examples

## no example

Creating Objects of Group Quantile Differences

Description

quantile_ is a wrapper function creating an object that, for each requested probability, computes the difference between the two arms' sample quantiles of the outcome. The object is passed to jointCovariance or pated through .... Because the empirical-quantile estimator has no tractable closed-form score, this engine is available only through the bootstrap path (nboot > 0).

Usage

quantile_(formula, probs = c(0.25, 0.5, 0.75), data_index = 1)

Arguments

Generating Data for Simulation and Testing

Description

simulateMoData generates data for simulation and testing purposes.

Usage

simulateMoData(n = 500, hr = 0.8, seed = NULL)

Arguments

Object Summaries

Description

summary method for class jointCovariance.

Usage

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

Arguments

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 'jointCovariance'
vcov(object, model_index = NULL, ...)

Arguments

Value

a matrix of covariance of all estimates