Type: Package
Title: Receiver Operating Characteristic Based Signature Identification
Version: 0.1.0
Description: Optimal linear combination predictive signatures for maximizing the area between two Receiver Operating Characteristic (ROC) curves (treatment vs. control).
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
RoxygenNote: 7.1.1
Imports: glmnet, MASS
NeedsCompilation: no
Packaged: 2022-08-23 22:41:30 UTC; huangxx13
Author: Xin Huang [aut, cre, cph]
Maintainer: Xin Huang <xin.huang@abbvie.com>
Repository: CRAN
Date/Publication: 2022-08-24 09:12:36 UTC

AUC

Description

Empirical AUC estimate

Usage

AUC(outcome, predict)

Arguments

outcome

binary outcome (1: desired outcome; 0: otherwise)

predict

prediction score

Details

Function for AUC when input is X and Y.

Value

a numeric value of empirical estimation of area under the ROC curves

Examples

# no run

C.index

Description

Empirical c-index estimate

Usage

C.index(yvar, score, censorvar, data)

Arguments

yvar

column name for observed time

score

column name for marker value

censorvar

column name for censor (1 is event, 0 is censored)

data

input data matrix

Details

Function for c-index when input is X and Y.

Value

a numeric value of empirical estimation of c-index

Examples

# no run

HIC

Description

function for HIC calculation

Usage

HIC(beta, Z, index, w = 1)

Arguments

beta

estimates of coefficient beta

Z

matrix prepared for ROCSI

index

m*n by 2 matrix for the subindex for the pair difference in Z

w

a vector of weights Z (can be used for inverse probability weighting for missing data, default is 1)

Details

Function for HIC calculation

Value

A numeric value with corresponding HIC

Examples

# no run

MClogit

Description

function for modified covariate methods based on glmnet

Usage

MClogit(
  dataset,
  yvar,
  xvars,
  trtvar,
  cvar = NULL,
  nfolds = 5,
  type = "binary",
  newx = NULL,
  bestsub = "lambda.1se",
  type.measure = "auc"
)

Arguments

dataset

data matrix for training dataset

yvar

column name for outcome

xvars

a string vector of column names for input markers

trtvar

column name for treatment (the column should contain binary code with 1 being treatment and 0 being control)

cvar

column name for censor (the column should contain binary code with 1 being event and 0 being censored)

nfolds

n fold CV used for cv.glmnet

type

outcome type ("binary" for binary outcome and "survival" for time-to-event outcome)

newx

data matrix for testing dataset X

bestsub

criteria for best lambda, used by glmnet

type.measure

type of measure used by glmnet

Details

function for ROCSI

Value

A list with ROCSI output

x.logit

final beta estimated from MClogit

predScore

a data.frame of testing data and its predictive signature scores (based on beta.aABC) for each subjects

abc

ABC in testing dataset based on optimal beta

fit.cv

the fitted glmnet object

Examples

n <- 100
k <- 5
prevalence <- sqrt(0.5)
rho<-0.2
sig2 <- 2
rhos.bt.real <- c(0, rep(0.1, (k-3)))*sig2
y.sig2 <- 1
yvar="y.binary"
xvars=paste("x", c(1:k), sep="")
trtvar="treatment"
prog.eff <- 0.5
effect.size <- 1
a.constent <- effect.size/(2*(1-prevalence))
ObsData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                    sig2=sig2, y.sig2=y.sig2, rho=rho,
                    rhos.bt.real=rhos.bt.real, a.constent=a.constent)
TestData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                     sig2=sig2, y.sig2=y.sig2, rho=rho,
                     rhos.bt.real=rhos.bt.real, a.constent=a.constent)
bst.mod <- MClogit(dataset=ObsData$data, yvar=yvar, xvars=xvars,
trtvar=trtvar, nfolds = 5, newx=TestData$data,
type="binary", bestsub="lambda.1se")
bst.mod$abc
bst.mod$x.logit[-1,1]

ROCSI

Description

function for ROCSI

Usage

ROCSI(
  Dtrain,
  Dtest = NULL,
  yvar,
  xvars,
  trtvar,
  cvar = NULL,
  nfolds = 5,
  type = "binary"
)

Arguments

Dtrain

data matrix for training dataset

Dtest

optional data matrix for testing dataset

yvar

column name for outcome

xvars

a string vector of column names for input markers

trtvar

column name for treatment (the column should contain binary code with 1 being treatment and 0 being control)

cvar

column name for censor (the column should contain binary code with 1 being event and 0 being censored)

nfolds

n fold CV used for cv.glmnet

type

outcome type ("binary" for binary outcome and "survival" for time-to-event outcome)

Details

function for ROCSI

Value

A list with ROCSI output

beta.aABC

final beta estimated from ROCSI based on ABC^{(acv)}

beta.1se

final beta estimated from lambda.1se based on nfold CV

lambda.aABC

optimal lambda selected by optimizing ABC^{(acv)}

fit.cv

fitted cv.glmnet model

log

log matrix of all lambdas and ABCs

abc.test

ABC in testing dataset based on optimal beta

abc.test1se

ABC in testing dataset based on 1se beta

predScore

a data.frame of testing data and its predictive signature scores (based on beta.aABC) for each subjects

predScore.1se

a data.frame of testing data and its predictive signature scores (based on beta.1se) for each subjects

Examples

n <- 100
k <- 5
prevalence <- sqrt(0.5)
rho<-0.2
sig2 <- 2
rhos.bt.real <- c(0, rep(0.1, (k-3)))*sig2
y.sig2 <- 1
yvar="y.binary"
xvars=paste("x", c(1:k), sep="")
trtvar="treatment"
prog.eff <- 0.5
effect.size <- 1
a.constent <- effect.size/(2*(1-prevalence))
ObsData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                    sig2=sig2, y.sig2=y.sig2, rho=rho,
                    rhos.bt.real=rhos.bt.real, a.constent=a.constent)
TestData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                     sig2=sig2, y.sig2=y.sig2, rho=rho,
                     rhos.bt.real=rhos.bt.real, a.constent=a.constent)
bst.aabc <- ROCSI(Dtrain=ObsData$data, Dtest = TestData$data, yvar=yvar,
xvars=xvars, trtvar=trtvar, cvar=NULL, nfolds=5, type="binary")
bst.aabc$beta.aABC
bst.aabc$log
bst.aabc$abc.test
bst.aabc$beta.1se
bst.aabc$abc.test1se

beta2theta

Description

Function to translate beta into theta, the n-sphere constrain

Usage

beta2theta(beta)

Arguments

beta

estimates of coefficient beta

Details

Function to translate beta into theta, the n-sphere constrain

Value

a numeric vector for theta (dimension-1)

Examples

# no run

cvfolds0

Description

internal function for generating CV fold index

Usage

cvfolds0(X, Y, idx, nfolds = 5)

Arguments

X

marker matrix for non-responders

Y

marker matrix for responders

idx

m*n by 2 matrix for row index of marker matrix, first column is row index in X; second column is for Y

nfolds

the cross-validation folds

Details

Function for generate CV fold index

Value

a vector containing CV fold index for each row in Z

Examples

# no run

data.gen

Description

Function for simulated data generation

Usage

data.gen(
  n,
  k,
  prevalence = sqrt(0.5),
  prog.eff = 1,
  sig2,
  y.sig2,
  rho,
  rhos.bt.real,
  a.constent
)

Arguments

n

Total sample size

k

Number of markers

prevalence

prevalence of predictive biomarkers with values above the cutoff

prog.eff

effect size beta for prognostic biomarker

sig2

standard deviation of each marker

y.sig2

Standard Deviation of the error term in the linear component

rho

rho*sig2 is the entries for covariance matrix between pairs of different k markers

rhos.bt.real

correlation between each prognostic and predictive markers

a.constent

a constant is set such that there is no overall treatment effect

Details

Function for simulated data generation

Value

A list of simulated clinical trial data with heterogeneous prognostic and predictive biomarkers

Examples

n <- 500
k <- 10
prevalence <- sqrt(0.5)
rho<-0.2
sig2 <- 2
rhos.bt.real <- c(0, rep(0.1, (k-3)))*sig2
y.sig2 <- 1
prog.eff <- 0.5
effect.size <- 1
a.constent <- effect.size/(2*(1-prevalence))
ObsData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                    sig2=sig2, y.sig2=y.sig2, rho=rho,
                    rhos.bt.real=rhos.bt.real, a.constent=a.constent)

grad.sub

Description

Internal function of grad_square in the GCV

Usage

grad.sub(z, beta)

Arguments

z

(m x n) x p data matrix as prepared for ROCSI

beta

estimates of coefficient beta

Details

Internal function of grad_square in the GCV

Value

grad_square in the GCV

Examples

# no run

gradsqr

Description

Internal function for HIC calculation

Usage

gradsqr(beta, Z0, index, w = 1)

Arguments

beta

estimates of coefficient beta

Z0

(m x n) x p Z matrix as prepared for ROCSI

index

m*n by 2 matrix for the subindex for the pair difference in Z

w

a vector of weights Z (can be used for inverse probability weighting for missing data, default is 1)

Details

Internal function for HIC calculation

Value

gradient square for the GCV.

Examples

# no run

hessAUC

Description

function for Hessian matrix of AUC

Usage

hessAUC(beta, Z, w = 1)

Arguments

beta

estimates of coefficient beta

Z

(m x n) x p data matrix as prepared for ROCSI

w

a vector of weights Z (can be used for inverse probability weighting for missing data, default is 1)

Details

function for Hessian matrix of AUC

Value

Hessian matrix of AUC.

Examples

# no run

hessAUC.sub

Description

Internal function for hessAUC

Usage

hessAUC.sub(z, beta)

Arguments

z

(m x n) x p data matrix as prepared for ROCSI

beta

estimates of coefficient beta

Details

Internal function for hessAUC

Value

Hessian matrix components.

Examples

# no run

pair.diff

Description

internal function for generating Z matrix (binary endpoint)

Usage

pair.diff(X, Y, A)

Arguments

X

marker matrix for non-responders

Y

marker matrix for responders

A

Treatment arm indicator (1 is treatment, 0 is control)

Details

Function for generate Z matrix for binary endpoint

Value

A list of prepared data input for ROCSI

Examples

# no run

pair.diff.surv

Description

internal function for generating Z matrix (time-to-event endpoint)

Usage

pair.diff.surv(X, Y, A, C)

Arguments

X

marker matrix

Y

a vector for observed time

A

a vector for Treatment arm indicator (1 is treatment, 0 is control)

C

a vector for censor (1 is event, 0 is censored)

Details

Function for generate Z matrix for time-to-event endpoint

Value

A list of prepared data input for ROCSI

Examples

# no run

theta2beta

Description

Function to translate theta into beta

Usage

theta2beta(theta)

Arguments

theta

n-sphere coordination

Details

Function to translate beta into theta, the n-sphere constrain

Value

a numeric vector for beta (dimension+1)

Examples

# no run