Ignite Creation Date:
2024-05-05 @ 11:25 AM
Last Modification Date:
2024-10-26 @ 9:07 AM
Study NCT ID:
NCT00037362
Status:
COMPLETED
Last Update Posted:
2016-07-29
First Post:
2002-05-16
Brief Title:
Measuring Sensitivity to Nonignorability
Sponsor:
National Heart Lung and Blood Institute NHLBI
Organization:
National Heart Lung and Blood Institute NHLBI
Study Overview
Official Title:
None
Status:
COMPLETED
Status Verified Date:
2008-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
To develop a new statistical index that measures sensitivity to non-ignorability index of sensitivity to nonignorability or ISNI for model-based inferences
Detailed Description:
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 observationsThey 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
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 observationsThey 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
Study Oversight
Has Oversight DMC:
Is a FDA Regulated Drug?:
Is a FDA Regulated Device?:
Is an Unapproved Device?:
Is a PPSD?:
Is a US Export?:
Is an FDA AA801 Violation?:
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| R01HL068074 | NIH | None | httpsreporternihgovquickSearchR01HL068074 |