pb.bayesian.binary {altmeta} | R Documentation |
Bayesian Method for Assessing Publication Bias/Small-Study Effects in Meta-Analysis of a Binary Outcome
Description
Performs multiple methods introduced in Shi et al. (2020) to assess publication bias/small-study effects under the Bayesian framework in a meta-analysis of (log) odds ratios.
Usage
pb.bayesian.binary(n00, n01, n10, n11, p01 = NULL, p11 = NULL, data,
sig.level = 0.1, method = "bay", het = "mul",
sd.prior = "unif", n.adapt = 1000, n.chains = 3,
n.burnin = 5000, n.iter = 10000, thin = 2,
upp.het = 2, phi = 0.5, coda = FALSE,
traceplot = FALSE, seed = 1234)
Arguments
n00 |
a numeric vector or the corresponding column name in the argument |
n01 |
a numeric vector or the corresponding column name in the argument |
n10 |
a numeric vector or the corresponding column name in the argument |
n11 |
a numeric vector or the corresponding column name in the argument |
p01 |
an optional numeric vector specifying true event rates (e.g., from simulations) in the treatment group 0 across studies. |
p11 |
an optional numeric vector specifying true event rates (e.g., from simulations) in the treatment group 1 across studies. |
data |
an optional data frame containing the meta-analysis dataset. If |
sig.level |
a numeric value specifying the statistical significance level |
method |
a character string specifying the method for assessing publication bias via Bayesian hierarchical models. It can be one of |
het |
a character string specifying the type of heterogeneity assumption for the publication bias tests. It can be either |
sd.prior |
a character string specifying prior distributions for standard deviation parameters. It can be either |
n.adapt |
the number of iterations for adaptation in the Markov chain Monte Carlo (MCMC) algorithm. The default is 1,000. This argument and the following |
n.chains |
the number of MCMC chains. The default is 1. |
n.burnin |
the number of iterations for burn-in period. The default is 5,000. |
n.iter |
the total number of iterations in each MCMC chain after the burn-in period. The default is 10,000. |
thin |
a positive integer specifying thinning rate. The default is 2. |
upp.het |
a positive number for specifying the upper bound of uniform priors for standard deviation parameters (if |
phi |
a positive number for specifying the hyper-parameter of half-normal priors for standard deviation parameters (if |
coda |
a logical value indicating whether to output MCMC posterior samples. The default is |
traceplot |
a logical value indicating whether to draw trace plots for the regression slopes. The default is |
seed |
an integer for specifying the seed value for reproducibility. |
Details
The Bayesian models are specified in Shi et al. (2020). The vague prior N(0, 10^4
) is used for the regression intercept and slope, and the uniform prior U(0, upp.het
) and half-normal prior HN(phi
) are used for standard deviation parameters. The half-normal priors may be preferred in meta-analyses with rare events or small sample sizes.
Value
This function returns a list containing estimates of regression slopes and their credible intervals with the specified significance level (sig.level
) as well as MCMC posterior samples (if coda
= TRUE
). Each element name in this list is related to a certain publication bias method (e.g., est.bay
and ci.bay
represent the slope estimate and its credible interval based on the proposed Bayesian method). In addition, trace plots for the regression slope are drawn if traceplot
= TRUE
.
Note
The current version does not support other effect measures such as relative risks or risk differences.
Author(s)
Linyu Shi ls16d@my.fsu.edu
References
Egger M, Davey Smith G, Schneider M, Minder C (1997). "Bias in meta-analysis detected by a simple, graphical test." BMJ, 315(7109), 629–634. <doi: 10.1136/bmj.315.7109.629>
Jin Z-C, Wu C, Zhou X-H, He J (2014). "A modified regression method to test publication bias in meta-analyses with binary outcomes." BMC Medical Research Methodology, 14, 132. <doi: 10.1186/1471-2288-14-132>
Shi L, Chu H, Lin L (2020). "A Bayesian approach to assessing small-study effects in meta-analysis of a binary outcome with controlled false positive rate". Research Synthesis Methods, 11(4), 535–552. <doi: 10.1002/jrsm.1415>
Thompson SG, Sharp SJ (1999). "Explaining heterogeneity in meta-analysis: a comparison of methods." Statistics in Medicine, 18(20), 2693–2708. <doi: 10.1002/(SICI)1097-0258(19991030)18:20<2693::AID-SIM235>3.0.CO;2-V>
See Also
pb.hybrid.binary
, pb.hybrid.generic
Examples
data("dat.poole")
set.seed(654321)
## increase n.burnin and n.iter for better convergence of MCMC
rslt.poole <- pb.bayesian.binary(n00, n01, n10, n11, data = dat.poole,
method = "bay", het = "mul", sd.prior = "unif", n.adapt = 1000,
n.chains = 3, n.burnin = 500, n.iter = 2000, thin = 2, upp.het = 2)
rslt.poole
data("dat.ducharme")
set.seed(654321)
## increase n.burnin and n.iter for better convergence of MCMC
rslt.ducharme <- pb.bayesian.binary(n00, n01, n10, n11, data = dat.ducharme,
method = "bay", het = "mul", sd.prior = "unif", n.adapt = 1000,
n.chains = 3, n.burnin = 500, n.iter = 2000, thin = 2, upp.het = 2)
rslt.ducharme
data("dat.henry")
set.seed(654321)
## increase n.burnin and n.iter for better convergence of MCMC
rslt.henry <- pb.bayesian.binary(n00, n01, n10, n11, data = dat.henry,
method = "bay", het = "mul", sd.prior = "unif", n.adapt = 1000,
n.chains = 3, n.burnin = 500, n.iter = 2000, thin = 2, upp.het = 2)
rslt.henry