Title:	Analyze Multiple Exposure Realizations in Association Studies
Version:	0.1.1
Depends:	R (≥ 3.5.0), stats, nimble
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), ggplot2
Description:	Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 <doi:10.1667/RR13729.1>) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 <doi:10.1201/9781420010138>), extended regression calibration (Little et al. 2023 <doi:10.1038/s41598-023-42283-y>), Monte Carlo maximum likelihood (Stayner et al. 2007 <doi:10.1667/RR0677.1>), frequentist model averaging (Kwon et al. 2023 <doi:10.1371/journal.pone.0290498>), and Bayesian model averaging (Kwon et al. 2016 <doi:10.1002/sim.6635>). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic.
License:	MIT + file LICENSE
Imports:	Rcpp (≥ 1.0.10), RcppEigen, coda, numDeriv, MCMCvis, mvtnorm, memoise, methods
LinkingTo:	Rcpp, RcppEigen
NeedsCompilation:	yes
VignetteBuilder:	knitr
Config/testthat/edition:	3
Author:	Sander Roberti [aut, cre], William Wheeler [aut], Deukwoo Kwon [aut], Ruth Pfeiffer [ctb], NCI [cph, fnd]
Maintainer:	Sander Roberti <sander.roberti@nih.gov>
Packaged:	2026-03-25 01:30:38 UTC; robertis2
Repository:	CRAN
Date/Publication:	2026-03-29 16:40:16 UTC

Analyze multiple exposure realizations in association studies

Description

Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 <doi:10.1667/RR13729.1>) to characterize exposure uncertainty. Methods include regression calibration (Carroll et al. 2006 doi:10.1201/9781420010138), extended regression calibration (Little et al. 2023 doi:10.1038/s41598-023-42283-y), Monte Carlo maximum likelihood (Stayner et al. 2007 doi:10.1667/RR0677.1), frequentist model averaging (Kwon et al. 2023 doi:10.1371/journal.pone.0290498), and Bayesian model averaging (Kwon et al. 2016 doi:10.1002/sim.6635). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic.

Details

The main function is ameras.

Author(s)

Sander Roberti <sander.roberti@nih.gov>, William Wheeler <WheelerB@imsweb.com>, Ruth Pfeiffer <pfeiffer@mail.nih.gov>, and Deukwoo Kwon <DKwon@uams.edu

References

Roberti, S., Kwon D., Wheeler W., Pfeiffer R. (in preparation). ameras: An R Package to Analyze Multiple Exposure Realizations in Association Studies

Analyze multiple exposure realizations

Description

Fit regression models accounting for exposure uncertainty using multiple Monte Carlo exposure realizations. Six outcome model families are supported. The first is the Gaussian family for continuous outcomes,

Y_i \sim N(\mu_i, \sigma^2),

with \mu_i = \alpha_0 + \bm X_i^T \bm \alpha +\beta_1 D_i+\beta_2 D_i^2 + \bm M_i^T \bm \beta_{m1}D_i + \bm M_i^T \bm \beta_{m2}D_i^2. Here \bm X_i are covariates, D_i is the exposure with measurement error, and \bm M_i are binary effect modifiers. The quadratic exposure terms and effect modification are optional.

For non-Gaussian families, three relative risk models for the main exposure are supported, the usual exponential RR_i=\exp(\beta_1 D_i+\beta_2 D_i^2+ \bm M_i^T \bm \beta_{m1}D_i + \bm M_i^T \bm \beta_{m2} D_i^2) and the linear excess relative risk (ERR) model RR_i= 1+\beta_1 D_i+\beta_2 D_i^2 + \bm M_i^T \bm \beta_{m1}D_i + \bm M_i^T \bm \beta_{m2}D_i^2, where the quadratic and effect modification terms are optional. Finally, the linear-exponential relative risk model RR_i= 1+(\beta_1 + \bm{M}_i^T \bm{\beta}_{m1}) D_i \exp\{(\beta_2+ \bm{M}_i^T \bm{\beta}_{m2})D_i\} is supported.

The second supported family is logistic regression for binary outcomes, with probabilities

p_i/(1-p_i)=RR_i\exp(\alpha_0+\bm X_i^T \bm \alpha).

Third is Poisson regression for counts,

Y_i \sim \text{Poisson}(\mu_i),

where \mu_i=RR_i \exp(\alpha_0 +\bm X_i^T \bm \alpha)\times \text{offset}_i with optional offset.

Fourth is proportional hazards regression for time-to-event data, with hazard function

h(t) = h_0(t)RR_i\exp(\bm X_i^T \bm \alpha),

with h_0 the baseline hazard.

Fifth is multinomial logistic regression for a categorical outcome with Z>2 outcome categories, with the last category as the referent category (i.e., \alpha_{0,Z}=\bm \alpha_{Z}=\beta_{1,Z}=\beta_{2,Z}=\bm \beta_{m1,Z} = \bm \beta_{m2,Z}=0):

P(Y_i=z)=RR_i\exp(\alpha_{0,z}+\bm X_i^T \bm \alpha_{z})/\{1+\sum_{s=1}^{Z-1} RR_i\exp(\alpha_{0,s}+\bm X_i^T \bm \alpha_{s})\}

Sixth is conditional logistic regression for matched case control data, for which

P\left(Y_i = 1, Y_k = 0 \forall k \neq i \bigg| \sum_{i \in \mathcal{R}} Y_i = 1\right) = RR_i\exp(\bm X_i^T \bm \alpha)/\{\sum_{k \in \mathcal{R}}RR_k\exp(\bm X_k^T \bm \alpha)\},

where \mathcal{R} is the matched set corresponding to individual i.

Methods include regression calibration (Carroll et al. 2006 doi:10.1201/9781420010138), extended regression calibration (Little et al. 2023 doi:10.1038/s41598-023-42283-y), Monte Carlo maximum likelihood (Stayner et al. 2007 doi:10.1667/RR0677.1), frequentist model averaging (Kwon et al. 2023 doi:10.1371/journal.pone.0290498), and Bayesian model averaging (Kwon et al. 2016 doi:10.1002/sim.6635).

Usage

ameras(data, family="gaussian", Y, dosevars, M=NULL, X=NULL, offset=NULL, entry=NULL, 
  exit=NULL, setnr=NULL, methods="RC", deg=1, doseRRmod="ERR", transform=NULL,
  transform.jacobian=NULL, inpar=NULL, CI=c("proflik","percentile"),
  params.profCI="dose", maxit.profCI=20, tol.profCI=1e-2, loglim=1e-30, MFMA=100000, 
  prophaz.numints.BMA=10, ERRprior.BMA="doubleexponential", nburnin.BMA=5000, 
  niter.BMA=20000, nchains.BMA=2, thin.BMA=10, included.replicates.BMA=1:length(dosevars), 
  optim.method="Nelder-Mead", control=NULL, ... )

Arguments

Details

A transformation can be used to reparametrize parameters internally (i.e., such that the likelihoods are evaluated at transform(parameters), where parameters are unconstrained), and should be specified when fitting linear excess relative risk and linear-exponential models to ensure nonnegative odds/risk/hazard. The included function transform1 applies an exponential transformation to the desired parameters, see ?transform1. When supplying a function to transform, this should be a function of the full parameter vector, returning a full (transformed) parameter vector. In particular, the full parameter vector contains parameters in the following order: \alpha_0, \bm \alpha, \beta_1, \beta_2, \bm \beta_{m1}, \bm \beta_{m2}, \sigma, where \bm \alpha, \bm\beta_{m1} and \bm \beta_{m2} can be vectors, with lengths matching \bm X and \bm M, respectively. \sigma is only included for the linear model (Gaussian family), and no intercept is included for the proportional hazards and conditional logistic models. For the multinomial model, the full parameter vector is the concatenation of Z-1 parameter vectors in the order as given above, where Z is the number of outcome categories, with the last category chosen as the referent category. See vignette("transformations", package="ameras") for an example of how to specify a custom transformation function.

When no transformation is specified and the linear ERR model is used, transform1 is used for ERR parameters \beta_1 and \beta_2 by default, with lower limits -1/max(D) for \beta_1 in the linear dose-response and (0,-1/max(D^2)) for (\beta_1,\beta_2) in the linear-quadratic dose-response, respectively. For the linear-exponential model, a lower limit of 0 is used for \beta_1, and no transformation is used for \beta_2. If effect modifiers M are specified, no transformation is used for those parameters. When negative RRs are obtained during optimization, an error will be generated and a different transformation or bounds should be used. All output is returned in the original parametrization. The Jacobian of the transformation (transform.jacobian) is required when using a transformation. For transform1, the Jacobian is given by transform1.jacobian. No transformations are used in BMA, and FMA is applied on the parameters using the parametrization as given in above with variances obtained using the delta method with the provided Jacobian function.

Multiple options for confidence intervals are provided. For (extended) regression calibration and Monte Carlo maximum likelihood, Wald and profile likelihood intervals can be obtained. When a parameter transformation \bm\theta = h(\bm\eta) is used, CI="wald.transformed" yields the CI h(\bm\eta \pm 1.96 \bm V) with \bm V the vector of standard deviations estimated using the inverse Hessian matrix, and CI="wald.orig" uses the delta method to obtain the CI h(\bm\eta)\pm 1.96 \bm V_* where \bm V_* is the vector of standard deviations estimated using J H^{-1} J^T with J the Jacobian of the transformation and H is the Hessian. When no transformation is used, CI="wald.orig" should be used. The third option is proflik, which uses the profile likelihood to compute confidence bounds. For FMA and BMA, the options for confidence/credible intervals are CI="percentile" which uses 2.5% and 97.5% percentiles, and CI="hpd" which computes highest posterior density intervals using HPDinterval from the coda package, both using the FMA samples or Bayesian posterior samples.

For BMA, a prior distribution for exposure-response parameters can be chosen when using linear or linear-exponential exposure-response model. The options are normal, horshoe, and double exponential priors, and the same priors truncated at 0 to yield positive values. In particular:

• Normal: \beta_j \sim N(0,1000) for all exposure-response parameters \beta_j

• Horseshoe (shrinkage prior): \tau \sim \text{Cauchy}(0,1)^+; \lambda_j \sim \text{Cauchy}(0,1)^+; \beta_j \sim N(0, \tau^2 \lambda_j^2). Here \tau is shared across all parameters

• Double exponential (shrinkage prior): \lambda_j \sim \text{Cauchy}(0,1)^+; \beta_j \sim \text{DoubleExponential}(0,\lambda_j)

For all other parameters, and when using the exponential exposure-response model or the Gaussian outcome family, the prior is N(0, 1000). For the parameter \sigma in the Gaussian family, this prior is truncated at 0.

Because the proportional hazards model is not available in nimble, ameras uses a piecewise constant baseline hazard for Bayesian model averaging. The interval min(entry), max(exit)) is divided into prophaz.numints.BMA subintervals with cutpoints obtained as quantiles of the distribution of event times among cases, and a baseline hazard parameter is estimated for each subinterval.

Value

The output is an object of class amerasfit with a component call and a component for every method supplied to methods. For each method, the output is a list containing

For RC, ERC, and MCML the following additional output is included:

For BMA the output additionally contains:

Finally, for FMA the output additionally contains:

The class amerasfit supports the methods coef, summary, and traceplot.

References

Roberti, S., Kwon D., Wheeler W., Pfeiffer R. (in preparation). ameras: An R Package to Analyze Multiple Exposure Realizations in Association Studies

Examples

  data(data, package="ameras")
  dosevars <- paste0("V", 1:10)
  ameras(data=data, family="gaussian", Y="Y.gaussian", dosevars=dosevars, 
  M=c("M1", "M2"), X=c("X1","X2")) 

Example data

Description

Data includes outcomes of all six supported types in the appropriately named columns. For proportional hazards regression, the observed exit time is time and event status is event. For conditional logistic regression, the matched set variable is setnr. The data has 10 exposure replicates in columns V1-V10.

Examples

 data(data, package="ameras")

 # Display a few rows of the data
 data[1:5, ]

Traceplots for MCMC samples

Description

Produce MCMC traceplots for amerasfit objects.

Usage

traceplot(object, ...)

## S3 method for class 'amerasfit'
traceplot(object, iter = 5000, Rhat = TRUE, n.eff = TRUE, pdf = FALSE, ...)

Arguments

Details

Wrapper for MCMCvis::MCMCtrace to produce MCMC diagnostic plots. See ?MCMCtrace for more plotting options that can be provided through ....

Value

Traceplots and posterior density plots.

See Also

MCMCtrace

Examples

   data(data, package="ameras")
   fit <- ameras(data, methods="BMA", Y="Y.gaussian", dosevars=paste0("V", 1:10))
   traceplot(fit)

Exponential parameter transformation

Description

Applies exponential transformation f(\theta_i)=\exp(\theta_i)+L_i to one or multiple components of parameter vector \bm \theta, where L_i are lower limits that can be different for each component

Usage

transform1(params, index.t=1:length(params), lowlimit=rep(0,length(index.t)), 
  boundcheck=FALSE, boundtol=1e-3, ... )

Arguments

Value

Transformed parameter vector.

Examples

params <- c(.1, .5, 1)
transform1(params, lowlimit=c(0, -1, 1))

Inverse of exponential parameter transformation

Description

Inverse of transform1 for the purpose of deriving initial values.

Usage

transform1.inv(params, index.t=1:length(params), lowlimit=rep(0,length(index.t)), ... )

Arguments

Value

Transformed parameter vector.

Examples

params <- c(.1, .5, 1) # Desired initial values on original scale
transform1.inv(params, lowlimit=c(0, -1, 1)) # Initial values to use on transformed scale

Jacobian of the exponential parameter transformation

Description

Computes the Jacobian matrix of transform1. Note that lower limits do not need to be specified as the Jacobian is independent of those

Usage

transform1.jacobian(params, index.t=1:length(params), ... )

Arguments

Value

Jacobian matrix.

Examples

params <- c(.1, .5, 1)
transform1.jacobian(params)