Limits...
Estimating range of influence in case of missing spatial data: a simulation study on binary data.

Bihrmann K, Ersbøll AK - Int J Health Geogr (2015)

Bottom Line: The study was based on the simulation of missing outcomes in a complete data set.The effect of missing observations on the estimated range of influence depended to some extent on the missing data mechanism.In general, the overall effect of missing observations was small compared to the uncertainty of the range estimate.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Medical and Health Sciences, University of Copenhagen, Grønnegårdsvej 8, DK-1870 Frederiksberg C, Denmark. krbi@sund.ku.dk.

ABSTRACT

Background: The range of influence refers to the average distance between locations at which the observed outcome is no longer correlated. In many studies, missing data occur and a popular tool for handling missing data is multiple imputation. The objective of this study was to investigate how the estimated range of influence is affected when 1) the outcome is only observed at some of a given set of locations, and 2) multiple imputation is used to impute the outcome at the non-observed locations.

Methods: The study was based on the simulation of missing outcomes in a complete data set. The range of influence was estimated from a logistic regression model with a spatially structured random effect, modelled by a Gaussian field. Results were evaluated by comparing estimates obtained from complete, missing, and imputed data.

Results: In most simulation scenarios, the range estimates were consistent with ≤25% missing data. In some scenarios, however, the range estimate was affected by even a moderate number of missing observations. Multiple imputation provided a potential improvement in the range estimate with ≥50% missing data, but also increased the uncertainty of the estimate.

Conclusions: The effect of missing observations on the estimated range of influence depended to some extent on the missing data mechanism. In general, the overall effect of missing observations was small compared to the uncertainty of the range estimate.

Show MeSH

Related in: MedlinePlus

Range of influence in missing data. Estimated range of influence in complete data (solid line) and simulated missing data. Simulation scenarios were: A: MCAR, B: MAR0 OR=1/3, C: MAR0 OR=3, D: MAR1 OR=1/3, E: MAR1 OR=3, F: MNAR OR=1/3, G: MNAR OR=3.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4325952&req=5

Fig1: Range of influence in missing data. Estimated range of influence in complete data (solid line) and simulated missing data. Simulation scenarios were: A: MCAR, B: MAR0 OR=1/3, C: MAR0 OR=3, D: MAR1 OR=1/3, E: MAR1 OR=3, F: MNAR OR=1/3, G: MNAR OR=3.

Mentions: The median of the estimated range of influence within each simulation scenario (Figure 1) ranged from 9.4 km (SD 4.0) (MAR1 OR=1/3, 75%) to 14.8 km (SD 19.3) (MNAR OR=3, 75%). In all scenarios except MAR1, the range estimates were quite similar with less than 50% missing observations. They tended to be slightly larger than the estimate obtained from the complete data, but differences were small, especially taking into account the uncertainty of the estimates. With ≥50% missing observations, the variation between scenarios increased, yet so did the standard deviation of each estimate. There was no strict pattern relating to the number of missing observations displayed, except in the MAR1 OR=1/3 scenario where the range decreased with increasing number of missing observations.Figure 1


Estimating range of influence in case of missing spatial data: a simulation study on binary data.

Bihrmann K, Ersbøll AK - Int J Health Geogr (2015)

Range of influence in missing data. Estimated range of influence in complete data (solid line) and simulated missing data. Simulation scenarios were: A: MCAR, B: MAR0 OR=1/3, C: MAR0 OR=3, D: MAR1 OR=1/3, E: MAR1 OR=3, F: MNAR OR=1/3, G: MNAR OR=3.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4325952&req=5

Fig1: Range of influence in missing data. Estimated range of influence in complete data (solid line) and simulated missing data. Simulation scenarios were: A: MCAR, B: MAR0 OR=1/3, C: MAR0 OR=3, D: MAR1 OR=1/3, E: MAR1 OR=3, F: MNAR OR=1/3, G: MNAR OR=3.
Mentions: The median of the estimated range of influence within each simulation scenario (Figure 1) ranged from 9.4 km (SD 4.0) (MAR1 OR=1/3, 75%) to 14.8 km (SD 19.3) (MNAR OR=3, 75%). In all scenarios except MAR1, the range estimates were quite similar with less than 50% missing observations. They tended to be slightly larger than the estimate obtained from the complete data, but differences were small, especially taking into account the uncertainty of the estimates. With ≥50% missing observations, the variation between scenarios increased, yet so did the standard deviation of each estimate. There was no strict pattern relating to the number of missing observations displayed, except in the MAR1 OR=1/3 scenario where the range decreased with increasing number of missing observations.Figure 1

Bottom Line: The study was based on the simulation of missing outcomes in a complete data set.The effect of missing observations on the estimated range of influence depended to some extent on the missing data mechanism.In general, the overall effect of missing observations was small compared to the uncertainty of the range estimate.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Medical and Health Sciences, University of Copenhagen, Grønnegårdsvej 8, DK-1870 Frederiksberg C, Denmark. krbi@sund.ku.dk.

ABSTRACT

Background: The range of influence refers to the average distance between locations at which the observed outcome is no longer correlated. In many studies, missing data occur and a popular tool for handling missing data is multiple imputation. The objective of this study was to investigate how the estimated range of influence is affected when 1) the outcome is only observed at some of a given set of locations, and 2) multiple imputation is used to impute the outcome at the non-observed locations.

Methods: The study was based on the simulation of missing outcomes in a complete data set. The range of influence was estimated from a logistic regression model with a spatially structured random effect, modelled by a Gaussian field. Results were evaluated by comparing estimates obtained from complete, missing, and imputed data.

Results: In most simulation scenarios, the range estimates were consistent with ≤25% missing data. In some scenarios, however, the range estimate was affected by even a moderate number of missing observations. Multiple imputation provided a potential improvement in the range estimate with ≥50% missing data, but also increased the uncertainty of the estimate.

Conclusions: The effect of missing observations on the estimated range of influence depended to some extent on the missing data mechanism. In general, the overall effect of missing observations was small compared to the uncertainty of the range estimate.

Show MeSH
Related in: MedlinePlus