# Can one assess whether missing data are missing at random in medical studies?

@article{Potthoff2006CanOA, title={Can one assess whether missing data are missing at random in medical studies?}, author={Richard F. Potthoff and Gail E. Tudor and Karen S. Pieper and Vic Hasselblad}, journal={Statistical Methods in Medical Research}, year={2006}, volume={15}, pages={213 - 234} }

For handling missing data, newer methods such as those based on multiple imputation are generally more accurate than older ones and entail weaker assumptions. Yet most do assume that data are missing at random (MAR). The issue of assessing whether the MAR assumption holds to begin with has been largely ignored. In fact, no way to directly test MAR is available. We propose an alternate assumption, MAR+, that can be tested. MAR+ always implies MAR, so inability to reject MAR+ bodes well for MAR…

## 118 Citations

What impact do assumptions about missing data have on conclusions? A practical sensitivity analysis for a cancer survival registry

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The method of pattern mixture sensitivity analysis after multiple imputation using colorectal cancer data as an example highlighted, which suggested a smaller association between Dukes’ stage and death, though the association remained positive and with similarly low p values.

Erratum to: What impact do assumptions about missing data have on conclusions? a practical sensitivity analysis for a cancer survival registry

- MedicineBMC Medical Research Methodology
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The method of pattern mixture sensitivity analysis after multiple imputation using colorectal cancer data as an example highlights the importance of making people aware of the need to test the MAR assumption and suggests a smaller association between Dukes’ stage and death.

Semi-Parametric Methods for Missing Data and Causal Inference

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This dissertation provides necessary and sufficient conditions for nonparametric identification of the full data distribution under MNAR with the aid of an IV and proposes inverse probability weighted estimation, outcome regression based estimation and doubly robust estimation of the mean of an outcome subject to MNAR.

Canonical Causal Diagrams to Guide the Treatment of Missing Data in Epidemiologic Studies

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Abstract With incomplete data, the “missing at random” (MAR) assumption is widely understood to enable unbiased estimation with appropriate methods. While the need to assess the plausibility of MAR…

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- 2019

Three different diagnostic tests are proposed that not only indicate when this assumption is incorrect but also suggest which variables are the most likely culprits, and evidence for its violation should encourage the careful statistician to conduct targeted sensitivity analyses.

MCAR is not necessary for the complete cases to constitute a simple random subsample of the target sample

- MathematicsStatistical methods in medical research
- 2016

It is shown that, unlike MCAR, AAR response mechanisms can be missing not at random (MNAR), and it is concluded that before pooling partially complete and complete cases into an analysis, the investigator should consider how selection might impact on the representativeness of the cases included in the pooled analysis (compared to those comprising the complete cases only).

Semiparametric Estimation with Data Missing Not at Random Using an Instrumental Variable.

- MathematicsStatistica Sinica
- 2018

This paper provides necessary and sufficient conditions for nonparametric identification of the full data distribution under MNAR with the aid of an IV and proposes inverse probability weighted estimation, outcome regression-based estimation and doubly robust estimation of the mean of an outcome subject to MNAR.

Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with incomplete data

- MedicineBMC Medical Research Methodology
- 2012

The practical utility of, and advocate, a pragmatic widely applicable approach to exploring plausible departures from the MAR assumption post multiple imputation is demonstrated and guidelines for applying this approach to epidemiological studies are developed.

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Existing methods to handling missing data in MSMs are reviewed and a simulation study is performed to compare the performance of complete case analysis, the last observation carried forward (LOCF), the missingness pattern approach (MPA), multiple imputation (MI) and inverse-probability-of-missingness weighting (IPMW).

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