| 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: | no |
| 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 |
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 |
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 |
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)