Description Usage Arguments Details Note Author(s) References See Also Examples
This function imputes missing effect sizes for unpublished studies and creates a forest plot. A set of forest plots can be generated for multiple imputations.
1 2 3 4 5 6 7 8 9 10 11 | forestsens(table,
binary = TRUE, mean.sd = FALSE,
higher.is.better = FALSE,
outlook = NA, all.outlooks = FALSE,
rr.vpos = NA, rr.pos = NA, rr.neg = NA, rr.vneg = NA,
smd.vpos = NA, smd.pos = NA, smd.neg = NA, smd.vneg = NA,
level = 95,
binary.measure = "RR", continuous.measure="SMD",
summary.measure="SMD", method = "DL",
random.number.seed = NA, sims = 10, smd.noise = 0.01,
plot.title = "", scale = 1, digits = 3)
|
table |
The name of the table containing the meta-analysis data. |
binary |
|
mean.sd |
|
higher.is.better |
|
outlook |
If you want all unpublished studies to be assigned the same outcome, set this parameter to one of the following values: |
all.outlooks |
If |
rr.vpos |
The user-defined relative risk for binary outcomes in unpublished studies with a |
rr.pos |
The user-defined relative risk for binary outcomes in unpublished studies with a "positive" outlook. |
rr.neg |
The user-defined relative risk for binary outcomes in unpublished studies with a "negative" outlook. |
rr.vneg |
The user-defined relative risk for binary outcomes in unpublished studies with a "very negative" outlook. |
smd.vpos |
The user-defined standardized mean difference for continuous outcomes in unpublished studies with a "very positive" outlook. |
smd.pos |
The user-defined standardized mean difference for continuous outcomes in unpublished studies with a "positive" outlook. |
smd.neg |
The user-defined standardized mean difference for continuous outcomes in unpublished studies with a "negative" outlook. |
smd.vneg |
The user-defined standardized mean difference for continuous outcomes in unpublished studies with a "very negative" outlook. |
level |
The confidence level, as a percent. |
binary.measure |
The effect size measure used for binary outcomes. "RR" for relative risk; "OR" for odds ratios. |
continuous.measure |
The effect size measure used for continuous outcomes. "SMD" for standardized mean difference (with the assumption of equal variances). |
summary.measure |
The measure used for summary effect sizes. |
method |
The same parameter in the escalc() function of the metafor package. "DL" for the DerSimonian-Laird method. |
random.number.seed |
Leave as |
sims |
The number of simulations to run per study when imputing unpublished studies with binary outcomes. |
smd.noise |
The standard deviation of Gaussian random noise to be added to standardized mean differences when imputing unpublished studies with continuous outcomes. |
plot.title |
Main title of forest plot. |
scale |
Changes the scaling of fonts in the forest plot. |
digits |
The number of significant digits (decimal places) to appear in the table of summary results which appears if |
For unpublished studies with binary outcomes, random numbers are generated from binomial distributions to impute the number of events in the experimental arms of experimental studies. The parameter of these distributions depends out the outlook of the unpublished study and the rate of events in the control arms of published studies. By default, 10 simulations are run and their average is used to impute the number of events in the experimental arm.
For unpublished studies with continuous outcomes, a 'very good' approximator mentioned by Borenstein is used to impute the variance of the standardized mean difference. See Borenstein et al, 2009, pages 27-28.
The function employs functions in the metafor
package: escalc()
and forest()
.
Noory Kim
Borenstein M, Hedges LV, Higgins JPT, and Rothstein HR (2009). Introduction to Meta-Analysis. Chichester UK: Wiley.
Cooper HC, Hedges LV, & Valentine JC, eds. (2009). The handbook of research synthesis and meta-analysis (2nd ed.). New York: Russell Sage Foundation.
DerSimonian R and Laird N (1986). "Meta-analysis in clinical trials." Controlled Clinical Trials 7:177-188 (1986).
Viechtbauer W (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. http://www.jstatsoft.org/v36/i03/.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | library(SAMURAI)
data(Hpylori)
forestsens(Hpylori, binary=TRUE, higher.is.better=FALSE)
forestsens(Hpylori, binary=TRUE, higher.is.better=FALSE, plot.title="Test")
forestsens(Hpylori, binary=TRUE, higher.is.better=FALSE, random.number.seed=52)
forestsens(Hpylori, binary=TRUE, higher.is.better=FALSE, outlook="negative")
forestsens(Hpylori, binary=TRUE, higher.is.better=FALSE, all.outlooks=TRUE)
data(greentea)
forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE)
forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE,
outlook="negative")
forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE,
outlook="negative", smd.noise=0.3)
|
Loading required package: metafor
Loading required package: Matrix
Loading 'metafor' package (version 2.0-0). For an overview
and introduction to the package please type: help(metafor).
outlooks m m.se m.lcl m.ucl exp.m.lcl
1 very positive -0.6624489 0.09243055 -0.8436095 -0.48128836 0.4301551
2 positive -0.5765907 0.08961385 -0.7522306 -0.40095075 0.4713141
3 no effect -0.3636069 0.08612856 -0.5324158 -0.19479805 0.5871847
4 negative -0.2169731 0.14039793 -0.4921480 0.05820178 0.6113119
5 very negative -0.1403204 0.17543644 -0.4841696 0.20352866 0.6162087
6 very positive CL -0.5892393 0.09006694 -0.7657672 -0.41271134 0.4649770
7 positive CL -0.5698771 0.08942482 -0.7451465 -0.39460765 0.4746647
8 current effect -0.5370043 0.08878312 -0.7110161 -0.36299262 0.4911449
9 negative CL -0.5101088 0.08816278 -0.6829047 -0.33731298 0.5051475
10 very negative CL -0.4776710 0.08747771 -0.6491241 -0.30621780 0.5225032
exp.m exp.m.ucl tau2 Q Qpval
1 0.5155872 0.6179867 0.000000000 29.99229 5.684842e-01
2 0.5618105 0.6696830 0.000000000 23.81930 8.508820e-01
3 0.6951644 0.8230008 0.002045797 32.26403 4.537029e-01
4 0.8049516 1.0599288 0.326067267 77.90845 1.060222e-05
5 0.8690797 1.2257203 0.654919128 128.42082 1.676299e-13
6 0.5547491 0.6618533 0.000000000 23.97694 8.452490e-01
7 0.5655950 0.6739444 0.000000000 23.47662 8.627318e-01
8 0.5844966 0.6955916 0.000000000 23.10906 8.748291e-01
9 0.6004302 0.7136854 0.000000000 23.49643 8.620617e-01
10 0.6202262 0.7362263 0.000000000 24.82313 8.131282e-01
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