Survival estimates of a prognostic classification depended more on year of treatment than on imputation of missing values

Dijk, M. R. van; Steyerberg, Ewout W.; Stenning, Sally; Habbema, J. Dik F.

doi:10.1016/j.jclinepi.2005.08.015

Cited by 11 publications

(9 citation statements)

References 20 publications

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“…Some case studies also illustrate benefits of imputation. 73,453 Possibly, many clinicians are unaware of the problems with CC analyses, and of modern developments in this area, especially on (multiple) imputation methods. The inclusion of the outcome in a multiple imputation process may be met with skepticism, while it is clearly necessary.…”

Section: Discussionmentioning

confidence: 99%

Clinical Prediction Models

Steyerberg¹

2009

Statistics for Biology and Health

1,317

720

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Section: Discussionmentioning

confidence: 99%

Clinical Prediction Models

Steyerberg¹

2009

Statistics for Biology and Health

1,317

720

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“…This not only allowed the analysis to be carried out on the complete dataset, but avoided potential problems of unreliability that are generally associated with a complete case analysis where any records with a missing value are removed. 19,20 Patients who died at the scene were not considered in this study as they are not included in the TARN database and this may have influenced the performance of the tools. The TARN dataset contains only trauma patients and so these tools may perform differently if applied to mass casualty incidents that contain chemical, biological, radiological or nuclear components.…”

Section: Discussionmentioning

confidence: 99%

Performance characteristics of five triage tools for major incidents involving traumatic injuries to children

Price

Brace-McDonnell

Stallard

et al. 2016

Injury

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Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-forprofit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Results: Of the 31,292 cases, 1,029 died (3.3%), 6,842 (21.9%) had major trauma (defined by an injury severity score > 15) and 14,711 (47%) were aged eight years or younger. There was variation in the performance accuracy of the tools to predict major trauma or death (sensitivities ranging between 36.4% and 96.2%; specificities 66.0% to 89.8%). Performance 3 characteristics varied with the age of the child. CareFlight had the best overall performance at predicting death, with the following sensitivity and specificity (95% CI) respectively: 95.3% (93.8 to 96.8) and 80.4% (80.0 to 80.9). JumpSTART was superior for the triaging of children under 8 years; sensitivity and specificity (95% CI) respectively: 86.3% (83.1 to 89.5) and 84.8% (84.2 to 85.5). The triage tools were generally better at identifying patients who would die than those with non-fatal severe injury. Conclusion:This statistical evaluation has demonstrated variability in the accuracy of triage tools at predicting outcomes for children who sustain traumatic injuries. No single tool performed consistently well across all evaluated scenarios.

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“…A flexible and common approach of dealing with missing values is multiple imputation [15-17,30]. Multiple imputation with chained equations works as follows: for each variable with missing values an individual imputation model is fitted.…”

Section: Discussionmentioning

confidence: 99%

Imputation of missing values of tumour stage in population-based cancer registration

Eisemann

Waldmann

Katalinic

2011

BMC Med Res Methodol

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BackgroundMissing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equations for analysing the stage-specific numbers of cases of malignant melanoma and female breast cancer.MethodsThis analysis was based on the malignant melanoma data set and the female breast cancer data set of the cancer registry Schleswig-Holstein, Germany. The cases with complete tumour stage information were extracted and their stage information partly removed according to a MAR missingness-pattern, resulting in five simulated data sets for each cancer entity. The missing tumour stage values were then treated with multiple imputation with chained equations, using polytomous regression, predictive mean matching, random forests and proportional sampling as imputation models. The estimated tumour stages, stage-specific numbers of cases and survival curves after multiple imputation were compared to the observed ones.ResultsThe amount of missing values for malignant melanoma was too high to estimate a reasonable number of cases for each UICC stage. However, multiple imputation of missing stage values led to stage-specific numbers of cases of T-stage for malignant melanoma as well as T- and UICC-stage for breast cancer close to the observed numbers of cases. The observed tumour stages on the individual level, the stage-specific numbers of cases and the observed survival curves were best met with polytomous regression or predictive mean matching but not with random forest or proportional sampling as imputation models.ConclusionsThis limited simulation study indicates that multiple imputation with chained equations is an appropriate technique for dealing with missing information on tumour stage in population-based cancer registries, if the amount of unstaged cases is on a reasonable level.

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Survival estimates of a prognostic classification depended more on year of treatment than on imputation of missing values

Cited by 11 publications

References 20 publications

Clinical Prediction Models

Clinical Prediction Models

Performance characteristics of five triage tools for major incidents involving traumatic injuries to children

Imputation of missing values of tumour stage in population-based cancer registration

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