MKmisc (version 1.9)

imputeSD: 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)

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.

Arguments

SD1

numeric vector, baseline SD.

SD2

numeric vector, follow-up SD.

SDchange

numeric vector, SD for changes from baseline.

Author

Matthias Kohl

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.

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)