metaanalysis {RTSA}

Fixed-effect or random-effects meta-analysis

Description

Computes a fixed-effect or random-effects meta-analysis including heterogeneity statistics. If mc is specified, a retrospective sample and trial size is calculated.

Usage

metaanalysis(
  outcome,
  data,
  side = 2,
  alpha = 0.05,
  beta = 0.1,
  weights = "IV",
  re_method = "DL_HKSJ",
  tau_ci_method = "BJ",
  cont_vartype = "equal",
  mc = NULL,
  RRR = NULL,
  sd_mc = NULL,
  study = NULL,
  conf_level = 0.95,
  zero_adj = 0.5,
  ...
)

Arguments

outcome

Outcome metric for the studies. Choose between: MD (mean difference), RR (relative risk), RD (risk difference), or OR (odds ratio).

data

A data.frame containing the study results. The data set must containing a specific set of columns. These are respectively 'eI' (events in intervention group), 'eC' (events in control group), 'nC' (participants intervention group) or 'nI' (participants control group) for discrete data, or, 'mI' (mean intervention group), 'mC' (mean control group), 'sdI' (standard error intervention group), 'sdC' (standard error control group),'nC' (participants intervention group) and 'nI' (participants control group) for continuous outcomes. Preferable also a 'study' column as an indicator of study.

side

Whether a 1- or 2-sided hypothesis test is used. Options are 1 or 2. Default is 2.

alpha

The level of type I error as a percentage, the default is 0.05 corresponding to 5%.

beta

The level of type II error as a percentage, the default is 0.1 corresponding to 10%. Not used unless a sample and trial size calculation is wanted.

weights

Method for calculating weights. Options are "MH" (Mantel-Haenzel and only optional for binary data) or "IV" (Inverse variance weighting). Default is "IV".

re_method

Methods are "DL" for DerSimonian-Laird or "DL_HKSJ" for DerSimonian-Laird with Hartung-Knapp-Sidik-Jonkman adjustment. Default is "DL_HKSJ".

tau_ci_method

Methods for computation of confidence interval for heterogeneity estimate tau. Calls rma.uni from the metafor package. Options are "BJ" and "QP". Default is "BJ"

cont_vartype

Variance type for continuous outcomes. Choices are "equal" (homogeneity of treatment group variances) or "non-equal" (heterogeneity of treatment group variances). Default is "equal".

mc

Minimum clinically relevant value. Used for sample and trial size calculation.

RRR

Relative risk reduction. Used for binary outcomes with outcome metric RR. Argument mc can be used instead. Must be a value between 0 and 1.

sd_mc

The expected standard deviation. Used for sample and trial size calculation for mean differences.

study

Optional vector of study IDs. If no study indicator is provided in 'data', a vector of study indicators e.g. names.

conf_level

Confidence interval coverage

zero_adj

Zero adjustment for null events in binary data. Options for now is 0.5. Default is 0.5.

...

Additional variables.

Value

A metaanalysis object which is a list with 6 or 7 elements.

study_results

A data.frame containing study results which is information about the individual studies

meta_results

A data.frame containing the results of the meta-analysis such as the pooled estimate, its standard error, confidence interval and p-value

hete_results

A list containing statistics about hetergeneity.

metaPrepare

A list containing the elements used for calculating the study results.

synthesize

A list containing the elements used for calculating the meta-analysis results.

settings

A list containing the arguments used in the metaanalysis call.

ris

(Only when mc has been specified or meta-analysis is created as part of RTSA). List of sample size and trial size calculation. See documentation for ris.

Examples

### Basic uses
# Use perioOxy data from package and run meta-analysis with default settings
data(perioOxy)
metaanalysis(outcome = "RR", data = perioOxy, study = perioOxy$trial)

# Run same meta-analysis but with odds ratio as outcome metric, Mantel-Haenzel 
# weights and DerSimionian-Laird for the variance estimate 
metaanalysis(outcome = "OR", data = perioOxy, study = perioOxy$trial,
 weights = "MH", re_method = "DL")
 
# Run meta-analysis with mean difference as outcome metric
data(eds)
metaanalysis(outcome = "MD", data = eds)

### Retrospective sample size calculation
# minimal clinical relevant difference set to an odds ratio of 0.7.
ma <- metaanalysis(outcome = "OR", data = perioOxy, mc = 0.7)
ma$ris