Analysis of local sensitivity to nonignorability with missing outcomes and predictors

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The ISNI (index of sensitivity to local nonignorability) method quantifies local sensitivity of parametric inferences to nonignorable missingness in an outcome variable. Here we extend ISNI to the situations where both outcomes and predictors can be missing and where the missingness mechanism can be either parametric or semi-parametric. We define the quantity MinNI (minimum nonignorability) to be an approximation to the norm of the smallest value of the transformed nonignorability that gives a nonnegligible displacement of the estimate of the parameter of interest. We illustrate our method in a complete data set from which we synthetically delete observations according to various patterns. We then apply the method to real-data examples involving the normal linear model and conditional logistic regression.

Original languageEnglish (US)
Pages (from-to)1342-1352
Number of pages11
Issue number4
StatePublished - Dec 2022


  • minimum nonignorability index
  • missing outcomes
  • missing predictors
  • sensitivity analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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