InitialSubset: InitialSubset

In NMAoutlier: Detecting Outliers in Network Meta-Analysis

Description

Choose an initial clean (i.e. likely outlier-free) subset of studies.

Usage

InitialSubset(
  TE,
  seTE,
  treat1,
  treat2,
  studlab,
  crit1,
  studies,
  P,
  reference,
  t1.label,
  t2.label,
  n_cores,
  ...
)

Arguments

TE	Estimate of treatment effect, i.e. difference between first and second treatment (e.g. log odds ratio, mean difference, or log hazard ratio).
seTE	Standard error of treatment estimate.
treat1	Label/Number for first treatment.
treat2	Label/Number for second treatment.
studlab	Study labels (important when multi arm studies are included).
crit1	A character string indicating the criterion to be used for selecting the initial subset, this criterion could be the minimum of median absolute residuals ("R") or the maximum of median absolute likelihood contributions ("L"). Default value is "R".
studies	An optional vector specifying a subset of studies to be used. The default value is the number of studies.
P	An optional vector specifying the number of samples for the choice of the initial subset.
reference	Reference treatment group.
t1.label	numbers to treatment 1 IDs.
t2.label	numbers to treatment 2 IDs.
n_cores	the number of cores that the process is running using the parallel.

Value

An initial clean subset of studies.

NMAoutlier documentation built on Oct. 11, 2021, 5:23 p.m.