Package aides is planned to support users to do additional analysis or graphics. Users can use functions in aides by calling the library with following syntax:
library(aides)
Briefly, aides currently consists of three tasks as follows:
Disparity: a newly proposed assumption regarding disparities in sample size analysis.
Discordance: a newly proposed assumption regarding discordance in rank of study size analysis.
Sequential analysis: a method to examine the sufficiency of information size.
Users can import their data and do relevant tests or graphics using functions in package aides. The present package consists of four functions listed as follows:
Disparity:
PlotDistrSS() , TestDisparity() , and PlotDisparity() .
Discordance:
TestDiscordance() 
.
Sequential analysis:
DoSA() , DoOSA() , and PlotOSA() .
The following steps and syntax demonstrate how user can carry out disparity test. Figure 3.2 visualizes the test based on the excessive cases of oulier(s).
Step 1. Import data (Olkin 1995)
library(meta)
data("Olkin1995")
dataOlkin1995 <- Olkin1995
Step 2. Process data
dataOlkin1995$n <- dataOlkin1995$n.exp + dataOlkin1995$n.cont
Step 3. Check distribution of study sizes. Using function shapiro.test() is a simple way to test whether sample sizes distribute normally, and further visualization with statistics can be carried out by function PlotDistrSS().
shapiro.test(dataOlkin1995$n)
PlotDistrSS(dataOlkin1995$n)#>
#> Shapiro-Wilk normality test
#>
#> data: dataOlkin1995$n
#> W = 0.2596, p-value < 2.2e-16
Figure 3.1: An example for visualization of distribution of study sizes
Step 4. Test assumption of disparity in study size
TestDisparity(n = n,
data = dataOlkin1995,
study = author,
time = year)#> Summary of disparities in sample size test:
#> Number of outliers = 13 (Excessive cases = 36509; P-value < 0.001)
#> Variability = 3.658 (P-value < 0.001)
#>
#> Outlier detection method: MAD
#> Variability detection method: CV
Step 5. Illustrate disparity plot
TestDisparity(n = n,
data = dataOlkin1995,
study = author,
time = year,
plot = TRUE)
Figure 3.2: An example for disparity-outlier plot
Due to non-normal distribution among the study sizes as shown in Step 3 (Figure 3.1 and also see the result of shapiro test), robust method is recommended for testing variability, which can be carried out by the following syntax:
rsltDisparity <- TestDisparity(n = n,
data = dataOlkin1995,
study = author,
time = year,
vrblty = "MAD")#> Summary of disparities in sample size test:
#> Number of outliers = 13 (Excessive cases = 36509; P-value < 0.001)
#> Variability = 0.951 (P-value < 0.001)
#>
#> Outlier detection method: MAD
#> Variability detection method: MAD
The following syntax instead of step 5 aforementioned is recommended for illustrating disparity plot of variability based on robust coefficient of variation:
PlotDisparity(rsltDisparity,
which = "CV",
szFntAxsX = 1)
Figure 3.3: An example for disparity-variability (robust) plot
The following steps and syntax demonstrate how user can carry out discordance test. Figure 3.4 visualizes the test.
Step 1. Import data (example of the study by Fleiss 1993)
library(meta)
data("Fleiss1993bin")
dataFleiss1993bin <- Fleiss1993bin
Step 2. Process data
dataFleiss1993bin$n <- dataFleiss1993bin$n.asp + dataFleiss1993bin$n.plac
dataFleiss1993bin$se <- sqrt((1 / dataFleiss1993bin$d.asp) - (1 / dataFleiss1993bin$n.asp) + (1 / dataFleiss1993bin$d.plac) - (1 / dataFleiss1993bin$n.plac))
Step 3. Test assumption of discordance in study size
TestDiscordance(n = n,
se = se,
study = study,
data = dataFleiss1993bin)#> Summary of discordance in ranks test:
#> Statistics (Bernoulli exact): 2
#> P-value: 0.423
#> Note: No significant finding in the test of discordance in study size ranks.
Step 4. Illustrate discordance plot
TestDiscordance(n = n,
se = se,
study = study,
data = dataFleiss1993bin,
plot = TRUE)
Figure 3.4: An example for discordance plot
The following steps and syntax demonstrate how user can carry out sequential analysis. Figure 3.5 sequential analysis plot.
Step 1. Import data (example of the study by Fleiss 1993)
library(meta)
data("Fleiss1993cont")
dataFleiss1993cont <- Fleiss1993cont
Step 2. Do sequential analysis
DoSA(Fleiss1993cont,
source = study,
time = year,
m1 = mean.psyc,
sd1 = sd.psyc,
n1 = n.psyc,
m2 = mean.cont,
sd2 = sd.cont,
n2 = n.cont,
measure = "SMD",
PES = 0.2,
group = c("Psychotherapy", "Control"),
plot = FALSE)#> Summary of sequential analysis (main information)
#> Acquired sample size: 232
#> Required sample size (without adjusted): 3260
#> Cumulative z score: -2.556
#> Alpha-spending boundary: 7.347 and -7.347
#> Adjusted confidence interval is suggested to be performed.
#>
#> Adjusted confidence interval based on type I error < 0.000001:
#> -1.072 to 0.386
#>
#> Summary of sequential analysis (additional information)
#> 1. Assumed information
#> 1.1. Defined type I error: 0.05
#> 1.2. Defined type II error: 0.2
#> 1.3. Defined power: 0.8
#> 1.4. Presumed effect: 0.2
#> 1.5. Presumed variance: 4.153
#>
#> 2. Meta-analysis
#> 2.1. Setting of the meta-analysis
#> Data were pooled using inverse variance in random-effects model with DL method.
#> 2.2. Result of the meta-analysis
#> SMD: -0.343 (95% CI: -0.607 to -0.08)
#>
#> 3. Adjustment factor
#> The required information size is calculated without adjustment factor. Relevant parameters are listed as follows.
#> 3.1. Heterogeneity (I-squared): 0%
#> 3.2. Diversity (D-squared): 0%
#> 3.3. Adjustement factor: Undefined
Step 3. Visualize sequential analysis
DoSA(Fleiss1993cont,
source = study,
time = year,
m1 = mean.psyc,
sd1 = sd.psyc,
n1 = n.psyc,
m2 = mean.cont,
sd2 = sd.cont,
n2 = n.cont,
measure = "SMD",
PES = 0.2,
group = c("Psychotherapy", "Control"))
Figure 3.5: An example for sequential analysis
Observed sequential analysis is recommended for those pooled analysis without pre-specified parameters for sequential analysis. In this situation, thus, Step 2 should use following syntax:
DoOSA(Fleiss1993cont,
source = study,
time = year,
m1 = mean.psyc,
sd1 = sd.psyc,
n1 = n.psyc,
m2 = mean.cont,
sd2 = sd.cont,
n2 = n.cont,
measure = "SMD",
group = c("Psychotherapy", "Control"))#> Summary of observed sequential analysis (main information)
#> Acquired sample size: 232
#> Optimal sample size (without adjusted): 1106
#> Cumulative z score: -2.556
#> Alpha-spending boundary: 4.278 and -4.278
#> Adjusted confidence interval is suggested to be performed.
#>
#> Adjusted confidence interval based on type I error 9.42059679664986e-06:
#> -0.939 to 0.252
#>
#> Summary of observed sequential analysis (additional information)
#> 1. Observed information
#> 1.1. Defined type I error: 0.05
#> 1.2. Defined type II error: 0.2
#> 1.3. Defined power: 0.8
#> 1.4. Observed effect size -0.343
#> 1.5. Observed variance: 4.153
#>
#> 2. Meta-analysis
#> 2.1. Setting of the meta-analysis
#> Data were pooled using inverse variance in random-effects model with DL method.
#> 2.2. Result of the meta-analysis
#> SMD: -0.343 (95% CI: -0.607 to -0.08)
#>
#> 3. Adjustment factor
#> The optimal information size is calculated without adjustment factor. Relevant parameters are listed as follows.
#> 3.1. Heterogeneity (I-squared): 0%
#> 3.2. Diversity (D-squared): 0%
#> 3.3. Adjustement factor: Undefined
Plot for observed sequential analysis can be simply illustrated using is the following syntax instead of step 3 abovementioned:
DoOSA(Fleiss1993cont,
source = study,
time = year,
m1 = mean.psyc,
sd1 = sd.psyc,
n1 = n.psyc,
m2 = mean.cont,
sd2 = sd.cont,
n2 = n.cont,
measure = "SMD",
group = c("Psychotherapy", "Control"),
plot = TRUE)
Figure 3.6: An example for observed sequential analysis plot