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Last data update: 2014.03.03

R: Healing of duodenal ulcers by Helicobacter pylori eradication...

Hpylori

R Documentation

Healing of duodenal ulcers by Helicobacter pylori eradication therapy

Description

Randomized clinical trials comparing duodenal ulcer acute healing among (1) patients on ulcer healing drug + Helicobacter pylori eradication therapy vs. (2) patients ulcer healing drug alone. The event counts represent the numbers of patients not healed.

Usage

data(Hpylori)

Format

A data frame with 33 observations on the following 7 variables.


study	`character`	Name of study or principal investigator
year	`numeric`	Year (optional)
outlook	`factor`	Denotes whether a study is unpublished, and if so, what outlook it has.
ctrl.n	`numeric`	The sample size of the control arm.
expt.n	`numeric`	The sample size of the experimental arm.
ctrl.events	`numeric`	The number of (undesired) events within the control arm.
expt.events	`numeric`	The number of (undesired) events within the experimental arm.

Details

The outlook of a study can be one of the following: published, very positive, positive, negative, very negative, current effect, no effect, very positive CL, positive CL, negative CL, or very negative CL.

Since the outcome event is undesired, when using the function forestsens(), specify the option higher.is.better=FALSE.

Source

Ford AC, Delaney B, Forman D, Moayyedi P. "Eradication therapy for peptic ulcer disease in Helicobacter pylori positive patients." Cochrane Database of Systematic Reviews 2006, Issue 2. Art No.: CD003840. DOI: 10.1002/14651858.CD003840.pub4.

Figure 3. Forest plot of comparison: 1 duodenal ulcer acute healing hp eradication + ulcer healing drug vs. ulcer healing drug alone, outcome: 1.1 Proportion not healed.

Examples

data(Hpylori)
Hpylori

forestsens(table=Hpylori, binary=TRUE, higher.is.better=FALSE, scale=0.8)

# To fix the random number seed to make the results reproducible.
forestsens(table=Hpylori, binary=TRUE, higher.is.better=FALSE, scale=0.8, 
  random.number.seed=106)

# To modify the outlooks of all unpublished studies to, say, "very negative". 
forestsens(table=Hpylori, binary=TRUE, higher.is.better=FALSE, scale=0.8, 
  random.number.seed=106, outlook="very negative")

# To modify the outlooks of all unpublished studies to, say, "very negative", 
# and overruling the default relative risk assigned to "very negative".
forestsens(table=Hpylori, binary=TRUE, higher.is.better=FALSE, scale=0.8, 
  random.number.seed=106, outlook="very negative", rr.vneg=2.5)

# To generate a forest plot for each of the ten default outlooks 
# defined by forestsens().
forestsens(table=Hpylori, binary=TRUE, higher.is.better=FALSE, scale=0.8, 
  random.number.seed=106, all.outlooks=TRUE)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(SAMURAI)
Loading required package: metafor
Loading required package: Matrix
Loading 'metafor' package (version 1.9-8). For an overview 
and introduction to the package please type: help(metafor).
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/SAMURAI/Hpylori.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Hpylori
> ### Title: Healing of duodenal ulcers by Helicobacter pylori eradication
> ###   therapy
> ### Aliases: Hpylori
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(Hpylori)
> Hpylori
           study year       outlook expt.events expt.n ctrl.events ctrl.n
1          Rauws 1990     published           7     24           5     26
2         Graham 1991     published           4     53          10     52
3  Bayrerdorffer 1992     published           2     29           4     29
4        Hosking 1992     published           8     78          21     77
5  Bianchi Porro 1993     published           7     91          12     92
6      HentscheI 1993     published           1     52           3     52
7      Mantzaris 1993     published           5     17           8     16
8           Wang 1993     published           3     23           6     36
9            Lin 1994     published           0     21           2     21
10        Spinzi 1994     published           2     24           3     29
11 Bayrerdorffer 1995     published           4    136          12    128
12         Katoh 1995     published           0     27           1     25
13         Logan 1995     published           2     70           6     78
14        Pinero 1995     published           8     30           7     30
15       Sobhani 1995     published           7     59          15     60
16         Avsar 1996     published           2     23          10     22
17      Figueroa 1996     published           4     57           4     43
18       Harford 1996     published          36    127          25     69
19          Kato 1996     published           0     28           1     23
20      O'Morain 1996     published           9    102          15    106
21       Parente 1996     published           7     63           1     33
22         Porro 1996     published           2     17           9     15
23     Shirotani 1996     published           4     25           6     25
24       Bardhan 1997     published           4    141           6     74
25    Carpintero 1997     published           3     78           3     44
26       Pounder 1997     published           5     61           8     30
27        Graham 1998      negative          NA     77          NA     76
28      Schwartz 1998      positive          NA    292          NA     60
29       Kepecki 1999      negative          NA     39          NA     34
30    van Zanten 1999 very positive          NA     98          NA     48
31          Wong 1999      negative          NA     57          NA     57
32         Asaka 2001      positive          NA    205          NA     51
33         Mones 2001      positive          NA     42          NA     43
> 
> forestsens(table=Hpylori, binary=TRUE, higher.is.better=FALSE, scale=0.8)
> 
> # To fix the random number seed to make the results reproducible.
> forestsens(table=Hpylori, binary=TRUE, higher.is.better=FALSE, scale=0.8, 
+   random.number.seed=106)
> 
> # To modify the outlooks of all unpublished studies to, say, "very negative". 
> forestsens(table=Hpylori, binary=TRUE, higher.is.better=FALSE, scale=0.8, 
+   random.number.seed=106, outlook="very negative")
> 
> # To modify the outlooks of all unpublished studies to, say, "very negative", 
> # and overruling the default relative risk assigned to "very negative".
> forestsens(table=Hpylori, binary=TRUE, higher.is.better=FALSE, scale=0.8, 
+   random.number.seed=106, outlook="very negative", rr.vneg=2.5)
> 
> # To generate a forest plot for each of the ten default outlooks 
> # defined by forestsens().
> forestsens(table=Hpylori, binary=TRUE, higher.is.better=FALSE, scale=0.8, 
+   random.number.seed=106, all.outlooks=TRUE)
           outlooks          m       m.se      m.lcl       m.ucl exp.m.lcl
1     very positive -0.6445051 0.09166791 -0.8241709 -0.46483926 0.4385985
2          positive -0.5754850 0.08962038 -0.7511377 -0.39983228 0.4718294
3         no effect -0.3640366 0.08731983 -0.5351803 -0.19289283 0.5855637
4          negative -0.2200922 0.14007800 -0.4946400  0.05445568 0.6097904
5     very negative -0.1442005 0.17426481 -0.4857532  0.19735231 0.6152336
6  very positive CL -0.5929134 0.09017425 -0.7696517 -0.41617515 0.4631744
7       positive CL -0.5634364 0.08923876 -0.7383412 -0.38853167 0.4779060
8    current effect -0.5501355 0.08929009 -0.7251408 -0.37513011 0.4842564
9       negative CL -0.5032177 0.08807591 -0.6758433 -0.33059213 0.5087272
10 very negative CL -0.4877946 0.08763787 -0.6595617 -0.31602758 0.5170779
       exp.m exp.m.ucl        tau2         Q        Qpval
1  0.5249223 0.6282361 0.000000000  27.80480 6.789802e-01
2  0.5624320 0.6704325 0.000000000  23.61799 8.579094e-01
3  0.6948658 0.8245703 0.005844567  32.75467 4.297949e-01
4  0.8024448 1.0559657 0.323290974  77.51036 1.201473e-05
5  0.8657142 1.2181731 0.642299175 126.32285 3.755889e-13
6  0.5527146 0.6595647 0.000000000  24.73109 8.167711e-01
7  0.5692495 0.6780517 0.000000000  23.72963 8.540352e-01
8  0.5768717 0.6871999 0.000000000  24.30827 8.330444e-01
9  0.6045821 0.7184982 0.000000000  24.27600 8.342549e-01
10 0.6139789 0.7290393 0.000000000  24.43146 8.283829e-01
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>