| DoSA {aides} | R Documentation |
Sequential analysis.
Description
DoSA() is a function for conducting sequential analysis.
Usage
DoSA(
data = NULL,
source = NULL,
time = NULL,
n = NULL,
es = NULL,
se = NULL,
r1 = NULL,
m1 = NULL,
sd1 = NULL,
n1 = NULL,
r2 = NULL,
m2 = NULL,
sd2 = NULL,
n2 = NULL,
group = c("Group 1", "Group 2"),
ref = 2,
prefer = "small",
measure = "ES",
model = "random",
method = "DL",
pooling = "IV",
trnsfrm = "logit",
poolProp = "IV",
alpha = 0.05,
beta = 0.2,
PES = NULL,
RRR = NULL,
PV = "post-hoc",
adjust = "D2",
plot = FALSE,
id = FALSE,
invert = FALSE,
smooth = FALSE,
SAP = FALSE,
BSB = FALSE
)
Arguments
data |
DATAFRAME consists of relevant information. |
source |
CHARACTER for labeling the included data sets. |
time |
NUMERIC values of time sequence. |
n |
INTEGER values of sample sizes. |
es |
NUMERIC values of effect sizes. |
se |
NUMERIC values of standard errors for the effect sizes. |
r1 |
INTEGER values of observed events in group 1 in the included data. |
m1 |
NUMERIC values of estimated means in group 1 in the included data. |
sd1 |
NUMERIC values of standard deviations in group 1 in the included data. |
n1 |
INTEGER values of sample sizes in group 1 in the included data. |
r2 |
INTEGER values of observed events in group 2 in the included data. |
m2 |
NUMERIC values of estimated means in group 2 in the included data. |
sd2 |
NUMERIC values of standard deviations in group 2 in the included data. |
n2 |
INTEGER values of sample sizes in group 2 in the included data. |
group |
CHARACTER for labeling two groups. |
ref |
NUMERIC values of 1 or 2 for indicating group 1 or 2 as reference. |
prefer |
CHARACTER of "small" and "large" for indicating which direction is beneficial effect in statistic test. |
measure |
CHARACTER for indicating which statistic measure should be used. |
model |
CHARACTER of "random" and "fixed" for indicating whether to use random-effects model or fixed-effect model. |
method |
CHARACTER for indicating which estimator should be used in random-effects model. In addition to the default "DL" method, the current version also supports "REML" and "PM" methods for calculating heterogeneity estimator. |
pooling |
CHARACTER for indicating which method has to be used for pooling binary data. Besides, current version also supports "MH" and "Peto" for binary data pooling. |
trnsfrm |
CHARACTER for indicating which method for transforming pooled proportion. Current version supports "none", "logit", "log", "arcsine", and "DAT" for the transformation. |
poolProp |
CHARACTER for indicating which method has to be used for pooling proportion. Current version supports "IV" and "GLMM" for the data pooling. |
alpha |
NUMERIC value between 0 to 1 for indicating the assumed type I error. |
beta |
NUMERIC value between 0 to 1 for indicating the assumed type II error. |
PES |
NUMERIC value for indicating the presumed meaningful effect size. |
RRR |
NUMERIC value between 0 and 1 for indicating the presumed relative
risk reduction. This parameter only works for dichotomous outcome
by replacing parameter |
PV |
NUMERIC value for indicating the presumed variance of the meaningful effect size. Current version allows a numeric value, "post-hoc", and "PES" based on different considerations. |
adjust |
CHARACTER for indicating how to adjust optimal information size. Current version consists of "none", "D2", "I2", "CHL", "CHM", and "CHH" for the adjustment. |
plot |
LOGIC value for indicating whether to illustrate alpha-spending monitoring plot. |
id |
LOGIC value for indicating whether to label each data source. |
invert |
LOGIC value for indicating whether to invert plot. |
smooth |
LOGIC value for indicating whether to smooth error boundaries. |
SAP |
LOGIC value for indicating whether to show sequential-adjusted power. |
BSB |
LOGIC value for indicating whether to illustrate beta-spending boundaries. |
Details
Basic information for the function DoSA(): DoSA() supports sequential analysis of aggregate data synthesis based on head-to-head comparison using either binary or continuous data in each group. Minimum information for the function DoSA() encompasses a data set of study-level data, and information for analysis settings in terms of time sequence, presumed meaningful effect size, and presumed variance of the meaningful effect size. Operative points of using function DoSA() are listed below:
1.1. Parameter data should be used for assigning a data set.
1.2. Study-level data have to be assigned according to outcome type:
1.2.1. For dichotomous outcome: Parameter n1 and n2 should be defined
with parameter r1 and r2.
1.2.2. For continuous outcome: parameter n1 and n2 should be defined
with parameter m1, sd1, m2, sd2.
1.3. Parameter source, time, PES, and PV are required for conducting
sequential analysis. Other parameters are auxiliary.
Default in the function DoSA() Certain defaults have been elucidated in the introductory section about the parameters, but some of them need to be elaborated upon due to their complexity.
2.1. Default on the parameter measure is "ES" that automatically uses risk
ratio ("RR") for binary outcome and mean difference ("MD") for continuous
outcome respectively. Argument "OR" and "SMD" can be used for the parameter
measure when original analysis pools data based on odds ratio or standardized
mean difference.
2.2. Default on the parameter method is "DL" for applying DerSimonian-Laird
heterogeneity estimator in the original pooled analysis. Other eligible arguments
for the parameter are "REML" for restricted maximum-likelihood estimator,
"PM" for Paule-Mandel estimator, "ML" for maximum-likelihood estimator,
"HS" for Hunter-Schmidt estimator, "SJ" for Sidik-Jonkman estimator,
"HE" for Hedges estimator, and "EB" for empirical Bayes estimator.
2.3. Default on the parameter pooling is "IV" for applying inverse variance
weighting method. Other commonly-used and eligible arguments for the parameter
are "MH" for Mantel-Haenszel method and "Peto" for pooling data using Peto
method. The arguments "MH" and "Peto" are exclusively available for binary
outcomes, while the argument "IV" will be automatically applied in the case
of continuous outcomes.
2.4. Default on the parameter adjust is "D2" for adjusting required
information size (RIS) based on diversity (D-squared statistics). Other eligible
arguments for the parameter are "None" for the RIS without adjustment, "I2"
for adjusted RIS based on I-squared statistics, "CHL" for adjusted RIS based
on low heterogeneity by multiplying 1.33, "CHM" for adjusted RIS by multiplying
2 due to moderate heterogeneity, and "CHL" for adjusted RIS by multiplying
4 due to high heterogeneity.
Value
DoSA() returns a summary on the result of sequential analysis, and can be
stored as an object in DoSA class. Explanations of returned information are
listed as follows:
studies |
Numbers of studies included in the sequential analysis. |
AIS |
Acquired information size refers to the total sample size in the sequential analysis. |
alpha |
A numeric value of type I error for the sequential analysis. |
beta |
A numeric value of type II error for the sequential analysis. |
PES |
A numeric value of presumed meaningful effect size for the sequential analysis. |
RRR |
A numeric value of relative risk reduction. |
variance |
A numeric value of presumed variance of the meaningful effect size for the sequential analysis. |
diversity |
A numeric value to show diversity in the pooled analysis. |
AF |
A numeric value of adjustment factor. |
RIS.org |
A numeric value for required information size without adjustment. |
RIS.adj |
A numeric value for adjusted required information size. |
frctn |
A vector of fraction of each study included in the sequential analysis. |
weight |
A vector of weight of each study included in the sequential analysis. |
es.cum |
A vector of cumulative effect size in the sequential analysis. |
se.cum |
A vector of standard error for the cumulative effect size in the sequential analysis. |
zval.cum |
A vector of cumulative z-value in the sequential analysis. |
asb |
A data frame of alpha-spending values for each study. |
aslb |
A numeric value for lower alpha-spending boundary. |
asub |
A numeric value for upper alpha-spending boundary. |
Author(s)
Enoch Kang
References
Jennison, C., & Turnbull, B. W. (2005). Meta-analyses and adaptive group sequential designs in the clinical development process. Journal of biopharmaceutical statistics, 15(4), 537–558. https://doi.org/10.1081/BIP-200062273.
Wetterslev, J., Jakobsen, J. C., & Gluud, C. (2017). Trial sequential analysis in systematic reviews with meta-analysis. BMC medical research methodology, 17(1), 1-18.
NCSS Statistical Software (2023). Group-sequential analysis for two proportions. In PASS Documentation. Available online: https://www.ncss.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Group-Sequential_Analysis_for_Two_Proportions.pdf
See Also
Examples
## Not run:
# 1. Import a dataset of study by Fleiss (1993)
library(meta)
data("Fleiss1993bin")
# 2. Perform observed sequential analysis
output <- DoSA(Fleiss1993bin, study, year,
r1 = d.asp, n1 = n.asp,
r2 = d.plac, n2 = n.plac,
measure = "RR",
PES = 0.05,
RRR = 0.2,
group = c("Aspirin", "Control"))
## End(Not run)