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Last data update: 2014.03.03

R: The effect of green tea on weight loss.

greentea

R Documentation

The effect of green tea on weight loss.

Description

Randomized clinical trials of at least 12 weeks duration assessing the effect of green tea consumption on weight loss.

Usage

data(greentea)

Format

A data frame with 14 observations on the following 9 variables.


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outlook	`factor`	Denotes whether a study is unpublished, and if so, what outlook it has.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

In this setting, a more negative change in outcome is desired; specify the option higher.is.better=FALSE for the function forestsens().

Source

Jurgens TM, Whelan AM, Killian L, Doucette S, Kirk S, Foy E. "Green tea for weight loss and weight maintenance in overweight or obese adults." Cochrane Database of Systematic Reviews 2012, Issue 12. Art. No.: CD008650. DOI: 10.1002/14651858.CD008650.pub2.

Figure 6. Forest plot of comparison: 1 Primary outcomes, outcome: 1.2Weight loss studies conducted in/outside Japan.

Examples

data(greentea)
greentea

forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE)

# To fix the random number seed to make the results reproducible. 
forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE, random.number.seed=52)

# To modify the outlooks of all unpublished studies to, say, "negative". 
forestsens(greentea, binary=FALSE,mean.sd=TRUE,higher.is.better=FALSE,random.number.seed=52,
  outlook="negative")

# To modify the outlooks of all unpublished studies to, say, "negative", and 
# overruling the default standardized mean difference (SMD) assigned to "negative". 
# (In this case, for a negative outlook we might assign a positive SMD, which corresponds to 
# having weight loss under green tea treatment less than weight loss under control treatment, 
# i.e. the green tea treatment is less effective at achieving weight loss than control treatment.
forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE,random.number.seed=52,
  outlook="negative", smd.neg=0.4)

# To generate a forest plot for each of the ten default outlooks defined by forestsens().
forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE, random.number.seed=52,
  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/greentea.Rd_%03d_medium.png", width=480, height=480)
> ### Name: greentea
> ### Title: The effect of green tea on weight loss.
> ### Aliases: greentea
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(greentea)
> greentea
          study year   outlook expt.mean expt.sd expt.n ctrl.mean ctrl.sd
1       Kataoka 2004 published     -1.30     1.7     71     -0.80     1.7
2     Takashima 2004 published     -1.60     1.9     10     -1.40     1.5
3      Diepvens 2005 published     -4.21     2.7     23     -4.19     1.3
4      Kajimoto 2005 published     -0.55     2.1    129      0.60     2.0
5        Kozuma 2005 published     -2.70     1.5    107      0.80     0.9
6          Hill 2007 published      0.08     0.9     19     -0.45     1.2
7         Nagao 2007 published     -1.70     1.5    123     -0.10     1.7
8  Auvichayapat 2008 published     -2.70     2.2     30     -2.00     9.7
9           Hsu 2008 published     -0.15     2.0     41     -0.03     1.9
10       Takase 2008 published     -2.90     1.2     44      0.10     0.7
11    Takeshita 2008 published     -1.10     1.3     40     -0.30     1.5
12         Maki 2009  positive        NA      NA     65        NA      NA
13       Suzuki 2009 no effect        NA      NA     18        NA      NA
14         Wang 2010 no effect        NA      NA    139        NA      NA
   ctrl.n
1      71
2       9
3      23
4      66
5     119
6      19
7     117
8      30
9      37
10     45
11     41
12     63
13     20
14     43
> 
> forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE)
> 
> # To fix the random number seed to make the results reproducible. 
> forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE, random.number.seed=52)
> 
> # To modify the outlooks of all unpublished studies to, say, "negative". 
> forestsens(greentea, binary=FALSE,mean.sd=TRUE,higher.is.better=FALSE,random.number.seed=52,
+   outlook="negative")
> 
> # To modify the outlooks of all unpublished studies to, say, "negative", and 
> # overruling the default standardized mean difference (SMD) assigned to "negative". 
> # (In this case, for a negative outlook we might assign a positive SMD, which corresponds to 
> # having weight loss under green tea treatment less than weight loss under control treatment, 
> # i.e. the green tea treatment is less effective at achieving weight loss than control treatment.
> forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE,random.number.seed=52,
+   outlook="negative", smd.neg=0.4)
> 
> # To generate a forest plot for each of the ten default outlooks defined by forestsens().
> forestsens(greentea, binary=FALSE, mean.sd=TRUE, higher.is.better=FALSE, random.number.seed=52,
+   all.outlooks=TRUE)
           outlooks          m      m.se     m.lcl       m.ucl exp.m.lcl
1     very positive -0.7595748 0.2405226 -1.230990 -0.28815920 0.2920032
2          positive -0.6505204 0.2472598 -1.135141 -0.16590006 0.3213769
3         no effect -0.5847041 0.2586021 -1.091555 -0.07785317 0.3356941
4          negative -0.5190681 0.2744221 -1.056926  0.01878924 0.3475226
5     very negative -0.4106176 0.3073181 -1.012950  0.19171491 0.3631461
6  very positive CL -0.8785881 0.2505495 -1.369656 -0.38752014 0.2541944
7       positive CL -0.8136472 0.2429947 -1.289908 -0.33738634 0.2752961
8    current effect -0.7477622 0.2404875 -1.219109 -0.27641542 0.2954933
9       negative CL -0.6810894 0.2437871 -1.158903 -0.20327541 0.3138301
10 very negative CL -0.6140091 0.2529327 -1.109748 -0.11827011 0.3296420
       exp.m exp.m.ucl      tau2        Q        Qpval
1  0.4678653 0.7496422 0.7445545 227.0627 3.624438e-41
2  0.5217742 0.8471309 0.7911771 243.5768 1.378747e-44
3  0.5572708 0.9251002 0.8714181 267.5629 1.423991e-49
4  0.5950748 1.0189669 0.9890928 301.2634 1.308696e-56
5  0.6632405 1.2113251 1.2558302 374.0737 6.610070e-72
6  0.4153690 0.6787380 0.8116605 240.4635 6.096125e-44
7  0.4432385 0.7136331 0.7606052 229.3917 1.195787e-41
8  0.4734248 0.7584978 0.7444334 227.4553 3.006598e-41
9  0.5060654 0.8160535 0.7672315 236.0057 5.113724e-43
10 0.5411769 0.8884560 0.8308936 255.6094 4.373891e-47
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>