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Type:

Package

Title:

Improved Methods for Constructing Prediction Intervals for Network Meta-Analysis

Version:

1.1-2

Date:

2023-05-21

Maintainer:

Hisashi Noma <noma@ism.ac.jp>

Description:

Improved methods to construct prediction intervals for network meta-analysis. The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022) <forthcoming> are implementable.

Imports:

stats, MASS, metafor

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

NeedsCompilation:

Packaged:

2023-05-22 01:01:14 UTC; Hisashi

Author:

Hisashi Noma

[aut, cre]

Depends:

R (≥ 3.5.0)

Repository:

CRAN

Date/Publication:

2023-05-22 04:10:02 UTC

The 'PINMA' package.

Description

Improved Methods for Constructing Prediction Intervals for Network Meta-Analysis.

References

Noma, H., Hamura, Y., Sugasawa, S. and Furukawa, T. A. (2022+). Improved methods to construct prediction intervals for network meta-analysis. Forthcoming.

Kenward-Roger-type adjustment for constructing prediction intervals of network meta-analysis

Description

Kenward-Roger-type adjustment for constructing prediction intervals of network meta-analysis.

Usage

KR(y, S)

Arguments

y

Contrast-based summary data of the outcome measure

S

Covariance estimates of y

Value

Results of the Kenward-Roger-type adjustment for inference of multivariate random-effects model and prediction intervals for network meta-analysis.

Estimates: Restricted maximum likelihood (REML) estimates, their SE, and Wald-type 95% confidence intervals by the Kenward-Roger-type adjustment.
Between-studies_SD: Between-studies SD estimate.
95%PI: 95% prediction intervals by the Kenward-Roger-type adjustment.

References

Noma, H., Hamura, Y., Sugasawa, S. and Furukawa, T. A. (2022+). Improved methods to construct prediction intervals for network meta-analysis. Forthcoming.

Examples

data(dstr)
attach(dstr)

# Transforming the arm-level data to the contrast-based summaryies
edat <- data.edit(study,trt,d,n)

y <- edat$y
S <- edat$S

KR(y,S)    # Results of the NMA analysis (log OR scale)

Parametric bootstrap procedure for constructing prediction intervals of network meta-analysis

Description

Parametric bootstrap procedure for constructing prediction intervals of network meta-analysis.

Usage

PBS(y, S, B=2000)

Arguments

y

Contrast-based summary data of the outcome measure

S

Covariance estimates of y

B

Number of bootstrap resampling (default: 2000).

Value

The parametric bootstrap prediction intervals for network meta-analysis.

Estimates: Restricted maximum likelihood (REML) estimates, their SE, and 95% Wald-type confidence intervals.
Between-studies_SD: Between-studies SD estimate.
95%PI: 95% prediction intervals by the parametric bootstrap.

References

Noma, H., Hamura, Y., Sugasawa, S. and Furukawa, T. A. (2022+). Improved methods to construct prediction intervals for network meta-analysis. Forthcoming.

Examples

data(dstr)
attach(dstr)

# Transforming the arm-level data to the contrast-based summaryies
edat <- data.edit(study,trt,d,n)

y <- edat$y
S <- edat$S

PBS(y,S,B=10)   # Results of the NMA analysis (log OR scale); B is recommended to be >= 1000.

Transforming arm-level data to contrast-based summary statistics

Description

Transforming arm-level data to contrast-based summary statistics.

Usage

data.edit(study,trt,d,n)

Arguments

study

Study ID

trt

Numbered treatment (=1,2,...)

d

Number of events

n

Sample size

Value

Contrast-based summary statistics are generated.

y: Contrast-based summary estimates.
S: Vectored within-study covariance matrix.

Examples

data(dstr)
attach(dstr)

edat <- data.edit(study,trt,d,n)

Siontis et al. (2018)'s network meta-analysis data

Description

study: Study ID
treat: Treatment
trt: Numbered treatment (1:CCTA, 2:CMR, 3:exercise ECG, 4:SPECT-MPI, 5:standard care, 6:Stress Echo)
n: Sample size
d: Number of events

Usage

data(dstr)

Format

A arm-based dataset with 29 rows and 5 variables

References

Siontis, G. C., Mavridis, D., Greenwood, J. P., et al. (2018). Outcomes of non-invasive diagnostic modalities for the detection of coronary artery disease: network meta-analysis of diagnostic randomised controlled trials. BMJ. 360: k504.

The ordinary t-approximation for constructing prediction intervals of network meta-analysis

Description

The ordinary t-approximation for constructing prediction intervals of network meta-analysis.

Usage

tPI(y, S)

Arguments

y

Contrast-based summary data of the outcome measure

S

Covariance estimates of y

Value

The ordinary t-approximation prediction intervals for network meta-analysis.

Estimates: Restricted maximum likelihood (REML) estimates, their SE, and Wald-type 95% confidence intervals.
Between-studies_SD: Between-studies SD estimate.
95%PI: 95% prediction intervals by the ordinary t-approximation.

References

Cooper, H., Hedges, L. V., and Valentine, J. C. (2009). The Handbook of Research Synthesis and Meta-Analysis, 2nd edition. New York: Russell Sage Foundation.

Chaimani, A., and Salanti, G. (2015). Visualizing assumptions and results in network meta-analysis: the network graphs package. Stata Journal 15, 905-920.

Examples

data(dstr)
attach(dstr)

# Transforming the arm-level data to the contrast-based summaryies
edat <- data.edit(study,trt,d,n)

y <- edat$y
S <- edat$S

tPI(y,S)   # Results of the NMA analysis (log OR scale)