Measuring Sensitivity to Nonignorability

This study has been completed.
Sponsor:
Information provided by:
National Heart, Lung, and Blood Institute (NHLBI)
ClinicalTrials.gov Identifier:
NCT00037362
First received: May 16, 2002
Last updated: January 18, 2008
Last verified: January 2008

May 16, 2002
January 18, 2008
September 2001
August 2005   (final data collection date for primary outcome measure)
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Complete list of historical versions of study NCT00037362 on ClinicalTrials.gov Archive Site
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Measuring Sensitivity to Nonignorability
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To develop a new statistical index that measures sensitivity to non-ignorability (index of sensitivity to nonignorability, or ISNI) for model-based inferences.

BACKGROUND:

Despite a considerable number of recent developments, missing data and associated methodology continues to be an important topic of research in biostatistics, medicine and public health. As investigators begin to understand the limitations of model-based inferences under the assumption of non-ignorable missingness, recent attention has turned to the formulation and implementation of sensitivity analyses. Having a general-purpose index to assess sensitivity to departures from ignorability would be extremely useful to researchers in a variety of fields in the health sciences. This is especially true if the index is relatively easy to compute and interpret.

DESIGN NARRATIVE:

It would be useful to have a general, easily computed diagnostic that characterizes data sets with respect to their potential for sensitivity to nonignorability. The investigators have developed a diagnostic that measures the effect of small perturbations from ignorability on coefficient estimates in the univariate linear model with missing observations.They will extend their analysis in a number of directions: i) They will develop a general class of diagnostics for Bayes and direct- likelihood inferences, and demonstrate its application to a number of important special cases. ii) They will develop an analogous theory for sensitivity to nonignorability in frequentist estimation and testing. iii) They will develop a general form of the diagnostic for the coarse-date model, a generalization of missing data that includes censoring and rounding as special cases. iv) They will analyze a number of real- world data sets that represent important cases where nonignorability is of interest, including dropout in longitudinal data, censored survival data, and cross-over in clinical trials.

Observational
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  • Cardiovascular Diseases
  • Heart Diseases
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Ma G, Troxel AB, Heitjan DF. An index of local sensitivity to nonignorable drop-out in longitudinal modelling. Stat Med. 2005 Jul 30;24(14):2129-50.

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Completed
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August 2005
August 2005   (final data collection date for primary outcome measure)

No eligibility criteria

Male
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No
Contact information is only displayed when the study is recruiting subjects
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NCT00037362
1165
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National Heart, Lung, and Blood Institute (NHLBI)
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Investigator: Daniel Heitjan Columbia University Health Sciences
National Heart, Lung, and Blood Institute (NHLBI)
January 2008

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP