metamean {metamedian} | R Documentation |
Meta-Analysis of the (difference of) means
Description
The function meta-analyzes one-group or two-group studies where each study reports one of the following summary measures:
S1: median, minimum and maximum values, and sample size
S2: median, first and third quartiles, and sample size
S3: median, minimum and maximum values, first and third quartiles, and sample size
S4: mean, standard deivation, and sample size.
This function estimates the study-specific means and their standard errors from the S1, S2, S3, or S4 summary data. When studies report S1, S2, or S3 summary data, a number of approaches can be applied to estimate the study-specific means and their standard errors. Then, this function estimates the pooled mean (for one-group studies) or the pooled difference of means (for two-group studies) based on the standard inverse variance method via the rma.uni
function. The convention used for calculating differences of means in two-group studies is: mean in group 1 minus mean in group 2.
Usage
metamean(
data,
mean_method = "mln",
se_method,
sd_method,
nboot = 1000,
pool_studies = TRUE,
...
)
Arguments
data |
data frame containing the study-specific summary data. For one-group studies, this data frame can contain the following columns:
For two group studies, this data frame can also contain the following columns for the summary data of the second group: | ||||||||||||||||
mean_method |
character string specifying the approach used to estimate the study-specific means. The options are the following:
| ||||||||||||||||
se_method |
character string specifying the approach used to estimate the standard errors of the study-specific means estimators in scenarios S1, S2, and S3. The options are the following:
| ||||||||||||||||
sd_method |
character string specifying the approach used to estimate the study-specific standard deviations when applying the naive standard error estimator (if applicable). The options are the following:
| ||||||||||||||||
nboot |
integer specifying the number of bootstrap samples to use when using parametric bootstrap to estimate the study-specific standard errors in scenarios S1, S2, and S3. The default is | ||||||||||||||||
pool_studies |
logical scalar specifying whether to meta-analyze the studies. If this argument is set to | ||||||||||||||||
... |
optional arguments that are passed into the |
Value
an object of class "rma.uni". See documentation of rma.uni
.
References
Bland M. (2015). Estimating mean and standard deviation from the sample size, three quartiles, minimum, and maximum. International Journal of Statistics in Medical Research. 4(1):57-64.
Cai S., Zhou J., and Pan J. (2021). Estimating the sample mean and standard deviation from order statistics and sample size in meta-analysis. Statistical Methods in Medical Research. 30(12):2701-2719.
Hozo S.P., Djulbegovic B., and Hozo I. (2005). Estimating the mean and variance from the median, range, and the size of a sample. BMC Medical Research Methodology. 5(1):1-10.
Luo D., Wan X., Liu J., and Tong T. (2016). Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Statistical Methods in Medical Research. 27(6):1785-805.
McGrath S., Zhao X., Steele R., Thombs B.D., Benedetti A., and the DEPRESsion Screening Data (DEPRESSD) Collaboration. (2020). Estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis. Statistical Methods in Medical Research. 29(9):2520-2537.
McGrath S., Katzenschlager S., Zimmer A.J., Seitel A., Steele R., and Benedetti A. (2023). Standard error estimation in meta-analysis of studies reporting medians. Statistical Methods in Medical Research. 32(2):373-388.
McGrath S., Zhao X., Ozturk O., Katzenschlager S., Steele R., and Benedetti A. (in press). metamedian: An R package for meta-analyzing studies reporting medians. Research Synthesis Methods.
Shi J., Luo D., Weng H., Zeng X.T., Lin L., Chu H., and Tong T. (2020a). Optimally estimating the sample standard deviation from the five-number summary. Research synthesis methods. 11(5):641-654.
Shi J., Tong T., Wang Y., and Genton M.G. (2020b). Estimating the mean and variance from the five-number summary of a log-normal distribution. Statistics and Its Interface. 13(4):519-531.
Wan X., Wang W., Liu J., and Tong T. (2014). Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Medical Research Methodology. 14(1):1-13.
Yang X., Hutson A.D., and Wang D. (2022). A generalized BLUE approach for combining location and scale information in a meta-analysis. Journal of Applied Statistics. 49(15):3846-3867.
Examples
## Method for Unknown Non-Normal Distributions
metamean(data = dat.age, mean_method = "mln", se_method = "bootstrap", nboot = 50)
## Box-Cox method
metamean(data = dat.age, mean_method = "bc", se_method = "bootstrap", nboot = 50)
## Quantile Matching Estimation method
metamean(data = dat.age, mean_method = "qe", se_method = "bootstrap", nboot = 50)