imputeSD: Impute Standard Deviations for Changes from Baseline
In MKmisc: Miscellaneous Functions from M. Kohl

imputeSD

R Documentation

Impute Standard Deviations for Changes from Baseline

Description

The function imputes standard deviations for changes from baseline adopting the approach describe in the Cochrane handbook, Section 16.1.3.2.

Usage

imputeSD(SD1, SD2, SDchange)

Arguments

`SD1`	numeric vector, baseline SD.
`SD2`	numeric vector, follow-up SD.
`SDchange`	numeric vector, SD for changes from baseline.

Details

The function imputes standard deviations for changes from baseline adopting the approach describe in the Cochrane handbook, Section 16.1.3.2.

1) Missing SD1 are replaced by correspondig values of SD2 and vice versa.

2) Correlations for complete data (rows) are computed.

3) Minimum, mean and maximum correlation (over rows) are computed.

4) Missing values of SDchange are computed by the formula provided in the handbook. The minimum, mean and maximum correlation are used leading to maximal, mean and minimal SD values that may be used for imputation as well as a sensitivity analysis.

Value

data.frame with possibly imputed SD1 and SD2 values as well as the given SDchange values are returen. Moreover, the computed correlations as well as possible values for the imputation of SDchange are returned.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.handbook.cochrane.org.

Examples

SD1 <- c(0.149, 0.022, 0.036, 0.085, 0.125, NA, 0.139, 0.124, 0.038)
SD2 <- c(NA, 0.039, 0.038, 0.087, 0.125, NA, 0.135, 0.126, 0.038)
SDchange <- c(NA, NA, NA, 0.026, 0.058, NA, NA, NA, NA)
imputeSD(SD1, SD2, SDchange)

MKmisc documentation built on Nov. 20, 2022, 1:05 a.m.