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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 )

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.