greentea: The effect of green tea on weight loss.

In SAMURAI: Sensitivity Analysis of a Meta-analysis with Unpublished but Registered Analytical Investigations

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outlook	factor	Denotes whether a study is unpublished, and if so, what outlook it has.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# To modify the outlooks of all unpublished studies to, say, "negative".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# To modify the outlooks of all unpublished studies to, say, "negative", and 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)

SAMURAI documentation built on May 1, 2019, 11:31 p.m.