Multiple imputation of missing blood pressure covariates in survival analysis

Buuren, Stef van; Boshuizen, Hendriek C.; Knook, D. L.

doi:10.1002/(sici)1097-0258(19990330)18:6<681::aid-sim71>3.3.co;2-i

Cited by 440 publications

(518 citation statements)

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“…The number of infants available for oxygen supply (n = 7676), phototherapy (n = 6654), parenteral nutrition (n = 6704) and venous access (n = 7209) was smaller compared with the other variables (n = 10 470 to n = 10 830). We used the Multivariate Imputation by Chained Equation method on all variables to deal with these missing data (25). In total, five predictions were conducted and afterwards pooled to produce estimates and confidence intervals that incorporate missing‐data uncertainty.…”

Section: Methodsmentioning

confidence: 99%

Risk indicators for hearing loss in infants treated in different Neonatal Intensive Care Units

Dommelen¹,

Mohangoo²,

Verkerk³

et al. 2010

Acta Paediatrica

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Section: Methodsmentioning

confidence: 99%

Risk indicators for hearing loss in infants treated in different Neonatal Intensive Care Units

Dommelen¹,

Mohangoo²,

Verkerk³

et al. 2010

Acta Paediatrica

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“…White and Royston derived an approximate conditional distribution for proportional hazards survival data that reduced to a regression model of X ( p ) with predictors

X^{(- p)}, δ, and Ĥ_{0} (Y),

where

Ĥ_{0} (Y)

is the estimated cumulative baseline hazard function . One adaptation of this would be to using log(Y) in place of

Ĥ_{0} (Y)

. Another adaptation would be to use a regression model for X ( p ) with predictors X (− p ) , δ f 1 ( Y ),and (1 − δ ) f 2 ( Y ), where f 1 ( Y ) and f 2 ( Y ) are functions of Y specified using splines or step functions.…”

Section: Introductionmentioning

confidence: 99%

Multiple imputation of missing covariates for the Cox proportional hazards cure model

et al. 2016

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We explore several approaches for imputing partially observed covariates when the outcome of interest is a censored event time and when there is an underlying subset of the population that will never experience the event of interest. We call these subjects “cured,” and we consider the case where the data are modeled using a Cox proportional hazards (CPH) mixture cure model. We study covariate imputation approaches using fully conditional specification (FCS). We derive the exact conditional distribution and suggest a sampling scheme for imputing partially observed covariates in the CPH cure model setting. We also propose several approximations to the exact distribution that are simpler and more convenient to use for imputation. A simulation study demonstrates that the proposed imputation approaches outperform existing imputation approaches for survival data without a cure fraction in terms of bias in estimating CPH cure model parameters. We apply our multiple imputation techniques to a study of patients with head and neck cancer.

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“…Each of these sets of values ‘fill-in’ the missing values (assuming MAR) and create multiple ‘complete’ datasets, so called ‘multiply’ datasets. Simulation studies have shown that as few as 3 ‘multiply’ datasets are adequate for a dataset with 20 % missing values [14]. Other studies have shown that 5–10 ‘multiply’ datasets are usually optimum depending on the proportion missing [7].…”

Section: Methodsmentioning

confidence: 99%

Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand

et al. 2016

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BackgroundData containing notified cases of disease are often compromised by incomplete or partial information related to individual cases. In an effort to enhance the value of information from enteric disease notifications in New Zealand, this study explored the use of Bayesian and Multiple Imputation (MI) models to fill risk factor data gaps. As a test case, overseas travel as a risk factor for infection with campylobacteriosis has been examined.MethodsTwo methods, namely Bayesian Specification (BAS) and Multiple Imputation (MI), were compared regarding predictive performance for various levels of artificially induced missingness of overseas travel status in campylobacteriosis notification data. Predictive performance of the models was assessed through the Brier Score, the Area Under the ROC Curve and the Percent Bias of regression coefficients. Finally, the best model was selected and applied to predict missing overseas travel status of campylobacteriosis notifications.ResultsWhile no difference was observed in the predictive performance of the BAS and MI methods at a lower rate of missingness (<10 %), but the BAS approach performed better than MI at a higher rate of missingness (50 %, 65 %, 80 %). The estimated proportion (95 % Credibility Intervals) of travel related cases was greatest in highly urban District Health Boards (DHBs) in Counties Manukau, Auckland and Waitemata, at 0.37 (0.12, 0.57), 0.33 (0.13, 0.55) and 0.28 (0.10, 0.49), whereas the lowest proportion was estimated for more rural West Coast, Northland and Tairawhiti DHBs at 0.02 (0.01, 0.05), 0.03 (0.01, 0.08) and 0.04 (0.01, 0.06), respectively. The national rate of travel related campylobacteriosis cases was estimated at 0.16 (0.02, 0.48).ConclusionThe use of BAS offers a flexible approach to data augmentation particularly when the missing rate is very high and when the Missing At Random (MAR) assumption holds. High rates of travel associated cases in urban regions of New Zealand predicted by this approach are plausible given the high rate of travel in these regions, including destinations with higher risk of infection. The added advantage of using a Bayesian approach is that the model’s prediction can be improved whenever new information becomes available.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-016-1784-8) contains supplementary material, which is available to authorized users.

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Multiple imputation of missing blood pressure covariates in survival analysis

Cited by 440 publications

References 0 publications

Risk indicators for hearing loss in infants treated in different Neonatal Intensive Care Units

Risk indicators for hearing loss in infants treated in different Neonatal Intensive Care Units

Multiple imputation of missing covariates for the Cox proportional hazards cure model

Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand

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