Title: Replicability-Analysis Tools for Meta-Analysis
Version: 1.2.0
Depends: R (≥ 4.1), meta (≥ 6.0-0)
Suggests: metafor (≥ 1.9.9), lme4, numDeriv, BiasedUrn, knitr, rmarkdown
Date: 2023-12-15
URL: https://github.com/IJaljuli/metarep
Description: User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings. - Replicability-analysis for fixed-effects and random-effect meta analysis: - r(u)-value; - lower bounds on the number of studies with replicated positive and\or negative effect; - Allows detecting inconsistency of signals; - forest plots with the summary of replicability analysis results; - Allows Replicability-analysis with or without the common-effect assumption.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
NeedsCompilation: yes
RoxygenNote: 7.2.3
VignetteBuilder: knitr
LazyData: true
Packaged: 2023-12-15 18:05:08 UTC; jaljuli
Author: Iman Jaljuli [cre, aut]
Maintainer: Iman Jaljuli <jaljuli.iman@gmail.com>
Repository: CRAN
Date/Publication: 2023-12-15 18:20:02 UTC

Data in meta-analysis reported in review CD002943, 'Cochrane library'.

Description

A dataset containing the meta-data of the the intervention 'Invitation letter' (CMP001), in the review "PStrategies for increasing the participation of women in community breast cancer screening" (CD002943) the results were reported by 5 studies, and analysed by Fixed-Effects meta-analysis.

Usage

CD002943_CMP001

Format

A data frame with 5 rows of 12 variables:

STUDY

Name of the study.

STUDY_WEIGHT

Stydy weight in meta-analysis as reported in th review.

N_EVENTS1

Number of events in the first group tested.

N_EVENTS2

Number of events in the second group tested.

N_TOTAL1

Number of patirnts in the first group tested.

N_TOTAL2

Number of patirnts in the second group tested.

GROUP1

Names of the first group in each study.

GROUP2

Names of the second group in each study.

N_STUDIES

Overall number of studies in the meta-analysis

CMP_ID

Cochrane Database review number

SM

A character string indicating which summary measure ("RR", "OR", "RD", or "ASD") is to be used for pooling of studies.

RANDOM

"YES" or "NO" indicating whether random-effects meta-analysis was performed.

Source

https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD002943/full

Data in meta-analysis reported in review CD003366, 'Cochrane library'.

Description

A dataset containing the meta-data of the outcome 'Leukopaenia' (CMP005), in the review "Texane-containing regimins for metastatic breast cancer" (CD003366) the results were reported by 28 studies, and analysed by Random-Effects meta-analysis.

Usage

CD003366_CMP005

Format

A data frame with 28 rows and 12 variables:

STUDY

Name of the study.

STUDY_WEIGHT

Stydy weight in meta-analysis as reported in th review.

N_EVENTS1

Number of events in the first group tested.

N_EVENTS2

Number of events in the second group tested.

N_TOTAL1

Number of patirnts in the first group tested.

N_TOTAL2

Number of patirnts in the second group tested.

GROUP1

Names of the first group in each study.

GROUP2

Names of the second group in each study.

N_STUDIES

Overall number of studies in the meta-analysis

CMP_ID

Cochrane Database review number

SM

A character string indicating which summary measure ("RR", "OR", "RD", or "ASD") is to be used for pooling of studies.

RANDOM

"YES" or "NO" indicating whether random-effects meta-analysis was performed.

Source

https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD003366.pub3/full

Data in meta-analysis reported in review CD006823, 'Cochrane library'.

Description

A dataset containing the meta-data of the outcome 'Seroma formation' (CMP001), in the review "Wound drainage after axillary dissection for carcinoma of the breast" (CD006823) the results were reported by 7 studies, and analysed by Random-Effects meta-analysis.

Usage

CD006823_CMP001

Format

A data frame with 7 rows and 12 variables:

STUDY

Name of the study.

STUDY_WEIGHT

Stydy weight in meta-analysis as reported in th review.

N_EVENTS1

Number of events in the first group tested.

N_EVENTS2

Number of events in the second group tested.

N_TOTAL1

Number of patirnts in the first group tested.

N_TOTAL2

Number of patirnts in the second group tested.

GROUP1

Names of the first group in each study.

GROUP2

Names of the second group in each study.

N_STUDIES

Overall number of studies in the meta-analysis

CMP_ID

Cochrane Database review number

SM

A character string indicating which summary measure ("RR", "OR", "RD", or "ASD") is to be used for pooling of studies.

RANDOM

"YES" or "NO" indicating whether random-effects meta-analysis was performed.

Source

https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD006823.pub2/full

Data in meta-analysis reported in review CD007077, 'Cochrane library'.

Description

A dataset containing the meta-data of the outcome 'cosmesis' (CMP001), in the review "Partial breast irradiation for early breast cancer" (CD007077) the results were reported by 5 studies, and analysed by Fixed-Effects meta-analysis.

Usage

CD007077_CMP001

Format

A data frame with 5 rows and 12 variables:

STUDY

Name of the study.

STUDY_WEIGHT

Stydy weight in meta-analysis as reported in th review.

N_EVENTS1

Number of events in the first group tested.

N_EVENTS2

Number of events in the second group tested.

N_TOTAL1

Number of patirnts in the first group tested.

N_TOTAL2

Number of patirnts in the second group tested.

GROUP1

Names of the first group in each study.

GROUP2

Names of the second group in each study.

N_STUDIES

Overall number of studies in the meta-analysis

CMP_ID

Cochrane Database review number

SM

A character string indicating which summary measure ("RR", "OR", "RD", or "ASD") is to be used for pooling of studies.

RANDOM

"YES" or "NO" indicating whether random-effects meta-analysis was performed.

Source

https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD007077.pub3/full

Lower bounds on the number of studies with replicated effect

Description

lower bounds on the number of studies with increased and\ or decreased effect.

Usage

find_umax(
  x,
  alternative = "two-sided",
  t = 0.05,
  confidence = 0.95,
  common.effect = FALSE
)

Arguments

x

Object of class 'meta'

alternative

'less', 'greater' or 'two-sided'

t

truncation threshold for truncated-Pearsons' test ('t=0.05' by default). t is ignored if 'common.effect = TRUE'.

confidence

Confidence level used in the computaion of the lower bound(s) u_{max}^L and\or u_{max}^R.

common.effect

Use common.effect = FALSE (default) for replicability-analysis combining with no assumptions (Pearson or truncated-Pearson test).

Value

An object of class list reporting the bounds on the number of studies with a positive or negative effect, as follows:

worst.case

A charachter vector of the names of n-u_{max}+1 studies at which the the r(u_{max})-value is computed.

side

The direction of the replicated signal in the 'worst.case' studies. 'less' if the effect is negative, 'greater' if positive.

u_max

The bound on the number of studies with either a positive or a negative effect.

r-value

The 'u-out-of-n' r(u)--value calculated with u=u_max.

Replicability_Analysis

Report of the replicability lower bounds on the number of studies with negative effect and with positive effect.

Examples

n.i.1 <- c( 20, 208, 24, 190, 58, 36, 51)
a.i <- c( 2,79,0,98,15,34,9) 
n.i.2 <- c( 20, 119, 22, 185, 29, 51, 47)
c.i <- c(9,106,14,98,12,49,9) 
m1 <- metabin( event.e = a.i,n.e = n.i.1,
               event.c = c.i,n.c = n.i.2,
               studlab = paste('Study',1:7), sm = 'OR',
               common = FALSE, random = TRUE )
find_umax(m1 , common.effect = FALSE, alternative = 'two-sided',
          t = 0.05 , confidence = 0.95 )

Forest plot to display the result of a meta-analysis with replicability analysis resuls

Description

Draws a forest plot in the active graphics window (using grid graphics system).

Usage

## S3 method for class 'metarep'
forest(x, ...)

Arguments

x

An object of class 'metarep'.

...

Arguments to be passed to methods, see forest.meta

Value

No return value, called for side effects

See Also

forest.meta, metarep,

Examples

n.i.1 <- c( 20, 208, 24, 190, 58, 36, 51)
a.i <- c( 2,79,0,98,15,34,9) 
n.i.2 <- c( 20, 119, 22, 185, 29, 51, 47)
c.i <- c(9,106,14,98,12,49,9) 
m1 <- metabin( event.e = a.i,n.e = n.i.1,event.c = c.i,n.c = n.i.2,
               studlab = paste0('Study ' , 1:7) , sm = 'OR' ,
               common = FALSE, random = TRUE )
mr1 <- metarep(  m1 , u = 2, common.effect = FALSE , t = 0.05 , 
               alternative = 'two-sided', report.u.max = TRUE)
forest(mr1, layout = "RevMan5", common = FALSE,
       label.right = "Favours control", col.label.right = "red",
       label.left = "Favours experimental", col.label.left = "green",
       prediction = TRUE)

One-sided replicability analysis

Description

One-sided replicability analysis

Usage

metaRvalue.onesided.U(
  x,
  u = 2,
  common = FALSE,
  random = TRUE,
  alternative = "less",
  do.truncated.umax = TRUE,
  alpha.tilde = 0.05
)

Arguments

x

object of class 'meta'

u

integer between 2-n

common

logical

random

logical

alternative

'less' or 'greater' only.

do.truncated.umax

logical.

alpha.tilde

between (0,1)

Value

No return value, called for internal use only.

Replicability-analysis of a meta-analysis

Description

Add results of replicability-analysis to a meta-analysis, whether common- or random-effects.

Usage

metarep(
  x,
  u = 2,
  t = 0.05,
  alternative = "two-sided",
  report.u.max = FALSE,
  confidence = 0.95,
  common.effect = FALSE
)

Arguments

x

object of class 'meta'

u

replicability requirement. u must be an intiger between 2 and n (nmber of studies in the meta-analysis).

t

truncation threshold for truncated-Pearsons' test ('t=0.05' by default). t is ignored if 'common.effect = TRUE'.

alternative

use 'less', 'greater' or 'two-sided'

report.u.max

use TREU to report the lower bounds on number of studies with replicated effect.

confidence

Confidence level used in the computaion of the lower bound(s) u_{max}^L and\or u_{max}^R.

common.effect

Use common.effect = FALSE (default) for replicability-analysis combining with no assumptions (Pearson or truncated-Pearson test). Replicability-analysis based on the test-statistic of common-effects model can be applied using common.effect = TRUE.

Value

An object of class list containing meta-analysis and replicability analysis results, as follows:

worst.case.studies

A charachter vector of the names of n-u+1 studies at which the the r(u)-value is computed.

r.value

r(u)-value for the specied u.

side

The direction of the effect with the lower one-sided r(u)-value

u_L, u_R

Lower bounds of the number of studies with decreased or increased effect, respectively. Both bounds are reported simultinualsly only when performing replicability analysis for two-sided alternative with no assumptions

Examples

 n.i.1 <- c( 20, 208, 24, 190, 58, 36, 51)
a.i <- c( 2,79,0,98,15,34,9) 
n.i.2 <- c( 20, 119, 22, 185, 29, 51, 47)
c.i <- c(9,106,14,98,12,49,9) 
m1 <- metabin( event.e = a.i,n.e = n.i.1,event.c = c.i,n.c = n.i.2,
               studlab = paste0('Study ' , 1:7) , sm = 'OR' ,
               common = FALSE, random = TRUE )
mr1 <- metarep(  m1 , u = 2, common.effect = FALSE , t = 0.05 , 
               alternative = 'two-sided', report.u.max = TRUE)
forest(mr1, layout='revman5',digits.pval = 4 , test.overall = TRUE )

Print meta-analysis with replicability-analysis results

Description

Print method for objects of class 'metarep'.

Usage

## S3 method for class 'metarep'
print(x, details.methods = TRUE, ...)

Arguments

x

An object of class 'metarep'

details.methods

A logical specifying whether details on statistical methods should be printed

...

Arguments to be passed to methods, see print.meta

Value

No return value, called for side effects.

Examples

n.i.1 <- c( 20, 208, 24, 190, 58, 36, 51)
a.i <- c( 2,79,0,98,15,34,9) 
n.i.2 <- c( 20, 119, 22, 185, 29, 51, 47)
c.i <- c(9,106,14,98,12,49,9) 
m1 <- metabin( event.e = a.i,n.e = n.i.1,event.c = c.i,n.c = n.i.2,
               studlab = paste0('Study ' , 1:7) , sm = 'OR' ,
               common = FALSE, random = TRUE )
mr1 <- metarep(  m1 , u = 2, common.effect = FALSE , t = 0.05 , 
               alternative = 'two-sided', report.u.max = TRUE) 
print(mr1, digits = 2)

Print detailed meta-analysis with replicability-analysis results

Description

Print method for objects of class 'summary.metarep'.

Usage

## S3 method for class 'summary.metarep'
print(x, details.methods = TRUE, ...)

Arguments

x

An object of class 'summary.metarep'

details.methods

A logical specifying whether details on statistical methods should be printed

...

Arguments to be passed to methods, see print.summary.meta

Value

No return value, called for side effects.

Examples

n.i.1 <- c( 20, 208, 24, 190, 58, 36, 51)
a.i <- c( 2,79,0,98,15,34,9) 
n.i.2 <- c( 20, 119, 22, 185, 29, 51, 47)
c.i <- c(9,106,14,98,12,49,9) 
m1 <- metabin( event.e = a.i,n.e = n.i.1,event.c = c.i,n.c = n.i.2,
               studlab = paste0('Study ' , 1:7) , sm = 'OR' ,
               common = FALSE, random = TRUE )
mr1 <- metarep(  m1 , u = 2, common.effect = FALSE , t = 0.05 , 
               alternative = 'two-sided', report.u.max = TRUE) 
print(summary(mr1), digits = 2)

Summary of meta-analysis with replicability-analysis results

Description

Summary method for objects of class 'metarep'.

Usage

## S3 method for class 'metarep'
summary(object, ...)

Arguments

object

An object of class 'metarep'.

...

Arguments to be passed to methods, see summary.meta

Value

A list of the quantities for replicability analysis, as follows:

meta-analysis results:

Summary of the supplied 'meta' object.

r.value:

r-value of the tested alternative.

u.increased:

Maximal number of studies at which replicability of increasing effect can be claimed. It will be reported unless the alternative is 'less'.

u.decreased:

Maximal number of studies at which replicability of increasing effect can be claimed. It will be reported unless the alternative is 'greater'.

Examples

n.i.1 <- c( 20, 208, 24, 190, 58, 36, 51)
a.i <- c( 2,79,0,98,15,34,9) 
n.i.2 <- c( 20, 119, 22, 185, 29, 51, 47)
c.i <- c(9,106,14,98,12,49,9) 
m1 <- metabin( event.e = a.i,n.e = n.i.1,event.c = c.i,n.c = n.i.2,
               studlab = paste0('Study ' , 1:7) , sm = 'OR' ,
               common = FALSE, random = TRUE )
mr1 <- metarep(  m1 , u = 2, common.effect = FALSE , t = 0.05 , 
               alternative = 'two-sided', report.u.max = TRUE)
               summary(mr1)

Truncated-Pearsons' test

Description

Apply Truncated-Pearsons' test or ordinary Pearsons' test on one-sided p-values.

Usage

truncatedPearson(p, alpha.tilde = 1)

Arguments

p

one-sided p-values of the individual studies for testing one-sided alternative based on z-test.

alpha.tilde

truncartion threshold for truncated-Pearson test. Use alpha.tilde = 1 for ordinary Pearsons' test for combining p-values.

Value

A 'list' containing the following quantities:

chisq:

Pearson test statistic

df:

degrees of freedom of truncated-Pearson statistic

rvalue:

p-value of the test

validp:

p-values used in the test.

Examples

truncatedPearson( p = c( 0.001 , 0.01 , 0.1  ) , alpha.tilde = 1 )
truncatedPearson( p = c( 0.001 , 0.01 , 0.1  ) , alpha.tilde = 0.05 )