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Title:

Weighted Meta-Analysis with Pseudo-Populations

Version:

1.2.0

Description:

Implementation of integrative weighting approaches for multiple observational studies and causal inferences. The package features three weighting approaches, each representing a special case of the unified weighting framework, introduced by Guha and Li (2024) <doi:10.1093/biomtc/ujae070>, which includes an extension of inverse probability weights for data integration settings.

License:

GPL-3

Encoding:

UTF-8

RoxygenNote:

7.3.2

LazyData:

true

Imports:

pkgcond, ggplot2, zeallot, caret, randomForest, forcats, utils, stats, grDevices

Depends:

R (≥ 3.5.0)

NeedsCompilation:

Packaged:

2025-06-17 02:16:54 UTC; doggydoggy

Author:

Subharup Guha [aut, cre], Mengqi Xu [aut], Kashish Priyam [aut], Yi Li [aut]

Maintainer:

Subharup Guha <s.guha@ufl.edu>

Repository:

CRAN

Date/Publication:

2025-06-17 04:20:06 UTC

Compute balancing weights using FLEXOR or other methods

Description

This function calculates balancing weights based on the specified pseudo-population method.

Usage

balancing.weights(
  S,
  Z,
  X,
  method,
  naturalGroupProp,
  num.random = 40,
  gammaMin = 0.001,
  gammaMax = (1 - 0.001),
  seed = NULL,
  verbose = TRUE
)

Arguments

S

Vector of factor levels representing the study memberships. Takes values in {1, ..., J}.

Z

Vector of factor levels representing the group memberships. Takes values in {1, ..., K}.

X

Covariate matrix of N rows and p columns.

method

Pseudo-population method, i.e., weighting method. Take values in FLEXOR, IC, or IGO.

naturalGroupProp

Relevant only for FLEXOR method: a fixed user-specified probability vector \theta.

num.random

Relevant only for FLEXOR method: number of random starting points of \gamma in the two-step iterative procedure. Default is 40.

gammaMin

Relevant only for FLEXOR method: Lower bound for each \gamma_s in the two-step iterative procedure. Default is 0.001.

gammaMax

Relevant only for FLEXOR method: Upper bound for each \gamma_s in the two-step iterative procedure. Default is 0.999.

seed

Seed for random number generation. Default is NULL.

verbose

Logical; Relevant only for FLEXOR method: if TRUE (default), displays progress messages during computation to the console. Set to FALSE to suppress these messages.

Value

An S3 list object with the following components:

wt.v: N empirically normalized sample weights.
percentESS: Percentage sample effective sample size (ESS) for the pseudo-population.

Examples

data(demo)
balancing.weights(S, Z, X, method = "IC", naturalGroupProp)

Estimate causal effects using FLEXOR or other methods

Description

This function estimates causal effects based on the specified pseudo-population method. The FLEXOR method involves an iterative two-step procedure.

Usage

causal.estimate(
  S,
  Z,
  X,
  Y,
  B = 100,
  method,
  naturalGroupProp = NULL,
  num.random = 40,
  gammaMin = 0.001,
  gammaMax = (1 - 0.001),
  seed = NULL,
  verbose = TRUE
)

Arguments

S

Vector of factor levels representing the study memberships. Takes values in {1, ..., J}.

Z

Vector of factor levels representing the group memberships. Takes values in {1, ..., K}.

X

Covariate matrix of N rows and p columns.

Y

Matrix of L outcomes, with dimensions N \times L.

B

Number of bootstrap samples for variance estimation. Default is 100.

method

Pseudo-population method, i.e., weighting method. Take values in FLEXOR, IC, or IGO.

naturalGroupProp

Relevant only for FLEXOR method: a fixed user-specified probability vector \theta.

num.random

Relevant only for FLEXOR method: number of random starting points of \gamma in the two-step iterative procedure. Default is 40.

gammaMin

Relevant only for FLEXOR method: Lower bound for each \gamma_s in the two-step iterative procedure. Default is 0.001.

gammaMax

Relevant only for FLEXOR method: Upper bound for each \gamma_s in the two-step iterative procedure. Default is 0.999.

seed

Seed for random number generation. Default is NULL.

verbose

Logical; if TRUE (default), displays progress messages during computation to the console. Set to FALSE to suppress these messages.

Value

An S3 list object with the following components:

percentESS

Percentage sample effective sample size (ESS) of the pseudo-population.

moments.ar

An array of dimension 3 \times K \times L, containing:

Estimated means, standard deviations (SDs), and medians (dimension 1),
For K groups (dimension 2),
And L counterfactual outcomes (dimension 3).

otherFeatures.v

Estimated mean group differences for L outcomes.

collatedMoments.ar

An array of dimension 3 \times K \times L \times B, containing:

moments.ar of the bth bootstrap sample (dimensions 1–3),
For B bootstrap samples (dimension 4).

collatedOtherFeatures.mt

A matrix of dimension L \times B containing:

otherFeatures.v of the bth bootstrap sample (dimension 1),
For B bootstrap samples (dimension 2).

collatedESS

A vector of length B

containing percentage sample ESS for B bootstrap samples.

method: Pseudo-population method, i.e., weighting method.

Examples

data(demo)
set.seed(1)
causal.estimate(S, Z, X, Y, B = 5, method = "IC", naturalGroupProp)

Demo Dataset

Description

A dataset containing example data for demonstration purposes.

Usage

data(demo)

Format

An rda object, with 450 observations and the following variables:

S: A vector of factor levels, representing the study memberships.
Z: A vector of factor levels, representing the group memberships.
X: A covariate matrix.
Y: An outcome matrix.
naturalGroupProp: The relative group prevalences of the larger natural population. Necessary only for FLEXOR weights; it should be skipped for IC and IGO weights.
groupNames: Disease subtype names "IDC" or "ILC"

Details

Demo Dataset

Examples

data(demo)

Plot method for objects of class 'causal_estimates'

Description

Plot method for objects of class 'causal_estimates'

Usage

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

Arguments

x

An object of class 'causal_estimates'.

...

Additional arguments including:

y_limit: The y-axis range. Default is c(0,50).
color: The boxplot color. Default is "red".

Value

A boxplot of percent sample ESS for a specific weighting method (FLEXOR, IC, or IGO)

Summary method for objects of class 'balancing_weights'

Description

Summary method for objects of class 'balancing_weights'

Usage

## S3 method for class 'balancing_weights'
summary(object, ...)

Arguments

object

An object of class 'balancing_weights'

...

Additional arguments affecting the summary produced (so far no additional arguments are needed, so leave blank).

Value

Printed summary of the 'balancing_weights' object, including:

Weight length: The total number of weights.
Weight distribution: Statistical summary of weight values.
percentESS: Percentage sample effective sample size (ESS) for the pseudo-population.

Examples

data(demo)
output1 <- balancing.weights(S, Z, X, method = "IC", naturalGroupProp)
summary(output1)

Summary method for objects of class 'causal_estimates'

Description

Summary method for objects of class 'causal_estimates'

Usage

## S3 method for class 'causal_estimates'
summary(object, ...)

Arguments

object

An object of class 'causal_estimates'

...

Additional arguments affecting the summary produced (so far no additional arguments are needed, so leave blank).

Value

Printed summary of the 'causal_estimates' object, including:

Percentage sample ESS: Percentage sample effective sample size (ESS) for the pseudo-population.
Mean differences with 95% CI: The mean differences between two groups with their corresponding 95% confidence intervals.
Sigma ratios with 95% CI: The ratios of standard deviations between two groups with their corresponding 95% confidence intervals.

Examples

data(demo)
set.seed(1)
output2 <- causal.estimate(S, Z, X, Y, B = 5, method = "IC", naturalGroupProp)
summary(output2)