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>3.0.co;2-r

Cited by 1,805 publications

(1,113 citation statements)

References 15 publications

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“…Missing data on costs and clinical effects for the various follow‐up periods were imputed using multiple imputation by chained equations (MICE) as implemented in STATA 14 (Van Buuren, Boshuizen, & Knook, 1999). For this, we made the assumption that data were “missing at random” (Faria, Gomes, Epstein, & White, 2014).…”

Section: Methodsmentioning

confidence: 99%

Cost effectiveness of guided Internet-based interventions for depression in comparison with control conditions: An individual-participant data meta-analysis

Kolovos

Dongen²,

Riper

et al. 2018

Depress Anxiety

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BackgroundThere is limited evidence on the cost effectiveness of Internet‐based treatments for depression. The aim was to evaluate the cost effectiveness of guided Internet‐based interventions for depression compared to controls.MethodsIndividual–participant data from five randomized controlled trials (RCT), including 1,426 participants, were combined. Cost‐effectiveness analyses were conducted at 8 weeks, 6 months, and 12 months follow‐up.ResultsThe guided Internet‐based interventions were more costly than the controls, but not statistically significant (12 months mean difference = €406, 95% CI: − 611 to 1,444). The mean differences in clinical effects were not statistically significant (12 months mean difference = 1.75, 95% CI: − .09 to 3.60 in Center for Epidemiologic Studies Depression Scale [CES‐D] score, .06, 95% CI: − .02 to .13 in response rate, and .00, 95% CI: − .03 to .03 in quality‐adjusted life‐years [QALYs]). Cost‐effectiveness acceptability curves indicated that high investments are needed to reach an acceptable probability that the intervention is cost effective compared to control for CES‐D and response to treatment (e.g., at 12‐month follow‐up the probability of being cost effective was .95 at a ceiling ratio of 2,000 €/point of improvement in CES‐D score). For QALYs, the intervention's probability of being cost effective compared to control was low at the commonly accepted willingness‐to‐pay threshold (e.g., at 12‐month follow‐up the probability was .29 and. 31 at a ceiling ratio of 24,000 and 35,000 €/QALY, respectively).ConclusionsBased on the present findings, guided Internet‐based interventions for depression are not considered cost effective compared to controls. However, only a minority of RCTs investigating the clinical effectiveness of guided Internet‐based interventions also assessed cost effectiveness and were included in this individual–participant data meta‐analysis.

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Section: Methodsmentioning

confidence: 99%

Cost effectiveness of guided Internet-based interventions for depression in comparison with control conditions: An individual-participant data meta-analysis

Kolovos

Dongen²,

Riper

et al. 2018

Depress Anxiety

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“…Logistic regression was used to identify the predictors of missingness. Data were assumed to be missing at random, and values for the missing predictors were imputed using multiple imputation techniques based on chained equations 29. The multiple imputation model included all predictors of missingness, the outcome, all pre‐specified predictors of the risk model, and the estimate of the cumulative hazard function 30.…”

Section: Methodsmentioning

confidence: 99%

Prediction of thrombo‐embolic risk in patients with hypertrophic cardiomyopathy (HCMRisk‐CVA)

Guttmann

Pavlou

O’Mahony

et al. 2015

European J of Heart Fail

127

105

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AimsAtrial fibrillation (AF) and thrombo‐embolism (TE) are associated with reduced survival in hypertrophic cardiomyopathy (HCM), but the absolute risk of TE in patients with and without AF is unclear. The primary aim of this study was to derive and validate a model for estimating the risk of TE in HCM. Exploratory analyses were performed to determine predictors of TE, the performance of the CHA2DS2‐VASc score, and outcome with vitamin K antagonists (VKAs).Methods and resultsA retrospective, longitudinal cohort of seven institutions was used to develop multivariable Cox regression models fitted with pre‐selected predictors. Bootstrapping was used for validation. Of 4821 HCM patients recruited between 1986 and 2008, 172 (3.6%) reached the primary endpoint of cerebrovascular accident (CVA), transient ischaemic attack (TIA), or systemic peripheral embolus within 10 years. A total of 27.5% of patients had a CHA2DS2‐VASc score of 0, of whom 9.8% developed TE during follow‐up. Cox regression revealed an association between TE and age, AF, the interaction between age and AF, TE prior to first evaluation, NYHA class, left atrial (LA) diameter, vascular disease, and maximal LV wall thickness. There was a curvilinear relationship between LA size and TE risk. The model predicted TE with a C‐index of 0.75 [95% confidence interval (CI) 0.70–0.80] and the D‐statistic was 1.30 (95% CI 1.05–1.56). VKA treatment was associated with a 54.8% (95% CI 31–97%, P = 0.037) relative risk reduction in HCM patients with AF.ConclusionsThe study shows that the risk of TE in HCM patients can be identified using a small number of simple clinical features. LA size, in particular, should be monitored closely, and the assessment and treatment of conventional vascular risk factors should be routine practice in older patients. Exploratory analyses show for the first time evidence for a reduction of TE with VKA treatment. The CHA2DS2‐VASc score does not appear to correlate well with the clinical outcome in patients with HCM and should not be used to assess TE risk in this population.

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“…These were complete case analysis (CC), single imputation (SI) using predictive mean matching [5], MI fitting separate flexible additive imputation models to each incomplete covariate with predictive mean matching [21] (MI-aregImpute), MI using regression switching (MI-MICE) and the addition of predictive mean matching (MI-MICE-PMM) [5]. Predictive mean matching incorporates a non-parametric element and therefore relies less on the parametric assumptions of the imputation models.…”

Section: Methodsmentioning

confidence: 99%

“…[2]) and imputation approaches (e.g. [3-5]). Likelihood based approaches generally require problem-specific programmes and therefore are not generally readily available.…”

Section: Introductionmentioning

confidence: 99%

“…A simulation study [6] comparing imputation procedures suggested that performing multiple imputation (MI) with regression switching (MICE) and using predictive mean matching (PMM) [5] may be preferred over other MI approaches or single imputation (SI) with highly skewed incomplete continuous covariates. In addition, MICE was found to produce similar results to MI using data augmentation and assuming a joint multivariate normal model or a general location model [5]. It is not clear whether MICE with PMM would remain beneficial in other populations, where the data may be closer to the underlying assumptions of the imputation methods.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study

Marshall

Altman

Holder

2010

BMC Med Res Methodol

111

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BackgroundThe appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model.MethodsObserved data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using regression switching with predictive mean matching (MICE-PMM) and e) multiple imputation using flexible additive imputation models. A Cox proportional hazards model was fitted to each dataset and estimates for the regression coefficients and model performance measures obtained.ResultsCC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness. The underestimated SE after SI resulted in poor coverage with 25% or more missingness. Of the MI approaches investigated, MI using MICE-PMM produced the least biased estimates and better model performance measures. However, this MI approach still produced biased regression coefficient estimates with 75% missingness.ConclusionsVery few differences were seen between the results from all missing data approaches with 5% missingness. However, performing MI using MICE-PMM may be the preferred missing data approach for handling between 10% and 50% MAR missingness.

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Multiple imputation of missing blood pressure covariates in survival analysis

Cited by 1,805 publications

References 15 publications

Cost effectiveness of guided Internet-based interventions for depression in comparison with control conditions: An individual-participant data meta-analysis

Cost effectiveness of guided Internet-based interventions for depression in comparison with control conditions: An individual-participant data meta-analysis

Prediction of thrombo‐embolic risk in patients with hypertrophic cardiomyopathy (HCMRisk‐CVA)

Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study

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