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Graphical sensitivity analysis with different methods of imputation for a trial with probable non-ignorable missing data

Graphical sensitivity analysis with different methods of imputation for a trial with probable non-ignorable missing data
Graphical sensitivity analysis with different methods of imputation for a trial with probable non-ignorable missing data
Graphical sensitivity analyses have recently been recommended for clinical trials with non-ignorable missing outcome. We demonstrate an adaptation of this methodology for a continuous outcome of a trial of three cognitive-behavioural therapies for mild depression in primary care, in which one arm had unexpectedly high levels of missing data. Fixed-value and multiple imputations from a normal distribution (assuming either varying mean and fixed standard deviation, or fixed mean and varying standard deviation) were used to obtain contour plots of the contrast estimates with their?P-values superimposed, their confidence intervals, and the root mean square errors. Imputation was based either on the outcome value alone, or on change from baseline. The plots showed fixed-value imputation to be more sensitive than imputing from a normal distribution, but the normally distributed imputations were subject to sampling noise. The contours of the sensitivity plots were close to linear in appearance, with the slope approximately equal to the ratio of the proportions of subjects with missing data in each trial arm.
imputation, missing data, randomized controlled trial, sensitivity analysis
1369-1473
397-413
Weatherall, M.
62047963-2ca7-4e9e-ad9e-1e73e9528d4e
Pickering, R.M.
4a828314-7ddf-4f96-abed-3407017d4c90
Harris, Scott
19ea097b-df15-4f0f-be19-8ac42c190028
Weatherall, M.
62047963-2ca7-4e9e-ad9e-1e73e9528d4e
Pickering, R.M.
4a828314-7ddf-4f96-abed-3407017d4c90
Harris, Scott
19ea097b-df15-4f0f-be19-8ac42c190028

Weatherall, M., Pickering, R.M. and Harris, Scott (2009) Graphical sensitivity analysis with different methods of imputation for a trial with probable non-ignorable missing data. Australian & New Zealand Journal of Statistics, 51 (4), 397-413. (doi:10.1111/j.1467-842X.2009.00553.x).

Record type: Article

Abstract

Graphical sensitivity analyses have recently been recommended for clinical trials with non-ignorable missing outcome. We demonstrate an adaptation of this methodology for a continuous outcome of a trial of three cognitive-behavioural therapies for mild depression in primary care, in which one arm had unexpectedly high levels of missing data. Fixed-value and multiple imputations from a normal distribution (assuming either varying mean and fixed standard deviation, or fixed mean and varying standard deviation) were used to obtain contour plots of the contrast estimates with their?P-values superimposed, their confidence intervals, and the root mean square errors. Imputation was based either on the outcome value alone, or on change from baseline. The plots showed fixed-value imputation to be more sensitive than imputing from a normal distribution, but the normally distributed imputations were subject to sampling noise. The contours of the sensitivity plots were close to linear in appearance, with the slope approximately equal to the ratio of the proportions of subjects with missing data in each trial arm.

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Published date: December 2009
Keywords: imputation, missing data, randomized controlled trial, sensitivity analysis

Identifiers

Local EPrints ID: 147729
URI: http://eprints.soton.ac.uk/id/eprint/147729
ISSN: 1369-1473
PURE UUID: 72ae006b-cdb5-41c0-bcc2-1571a65ece38

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Date deposited: 26 Apr 2010 12:45
Last modified: 14 Mar 2024 00:59

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Contributors

Author: M. Weatherall
Author: R.M. Pickering
Author: Scott Harris

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