Multiple imputation by predictive mean matching in cluster-randomized trials

Bailey, Brittney E.; Andridge, Rebecca; Shoben, Abigail B.

doi:10.1186/s12874-020-00948-6

Cited by 19 publications

(15 citation statements)

References 22 publications

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“…These predictive means are then used to match complete and incomplete observations during imputation process. The non-parametric stage applies the method of Nearest Neighbour Donor to produce original data value from non-missing observation having nearest predictive mean distance close to missing one so as to impute a missing data value [ 34 , 35 ]. The function and package ‘mice’ in R statistical software [ 33 ] was used to perform the PMM imputation five times, storing results from five complete datasets, and combining the results from five analysed datasets.…”

Section: Methodsmentioning

confidence: 99%

Classification of breast cancer recurrence based on imputed data: a simulation study

Abassi

Msengwa

2022

BioData Mining

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Several studies have been conducted to classify various real life events but few are in medical fields; particularly about breast recurrence under statistical techniques. To our knowledge, there is no reported comparison of statistical classification accuracy and classifiers’ discriminative ability on breast cancer recurrence in presence of imputed missing data. Therefore, this article aims to fill this analysis gap by comparing the performance of binary classifiers (logistic regression, linear and quadratic discriminant analysis) using several datasets resulted from imputation process using various simulation conditions. Our study aids the knowledge about how classifiers’ accuracy and discriminative ability in classifying a binary outcome variable are affected by the presence of imputed numerical missing data. We simulated incomplete datasets with 15, 30, 45 and 60% of missingness under Missing At Random (MAR) and Missing Completely At Random (MCAR) mechanisms. Mean imputation, hot deck, k-nearest neighbour, multiple imputations via chained equation, expected-maximisation, and predictive mean matching were used to impute incomplete datasets. For each classifier, correct classification accuracy and area under the Receiver Operating Characteristic (ROC) curves under MAR and MCAR mechanisms were compared. The linear discriminant classifier attained the highest classification accuracy (73.9%) based on mean-imputed data at 45% of missing data under MCAR mechanism. As a classifier, the logistic regression based on predictive mean matching imputed-data yields the greatest areas under ROC curves (0.6418) at 30% missingness while k-nearest neighbour tops the value (0.6428) at 60% of missing data under MCAR mechanism.

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Section: Methodsmentioning

confidence: 99%

Classification of breast cancer recurrence based on imputed data: a simulation study

Abassi

Msengwa

2022

BioData Mining

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“…Analysis was conducted in R (R Core Team, 2019) with RStudio (RStudio Team, 2016). Missing data were quantified by cohort (Figure S1) and imputed from the observed data by predictive mean matching (Bailey et al, 2020;De Silva et al, 2019;Morris et al, 2014). The Multivariate Imputation by Chained Equations (MICE) (Van Buuren and Groothuis-Oudshoorn, 2011) algorithm was applied independently to each cohort under fully conditional specification.…”

Section: Variables and Missing Datamentioning

confidence: 99%

Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential

et al. 2022

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“…Missing data mechanisms are categorized into; Missing Completely at Random (MCAR), Missing at Random (MAR) and Not Missing at Random (NMAR). Assumptions behind these mechanisms can affect imputation methods and their results if they are not properly checked [10]. The patterns of missing data show how the missing values are distributed over variables containing missing data.…”

Section: Missing Data Mechanism and Patternmentioning

confidence: 99%

“…The Bayesian approach is used to draw the 𝑃 acceptable values from 'conditional predictive distribution' containing missing values [17]. The algorithm for MI involves the three steps according to [10]. a) Missing data are filled-in 𝑃 times to yield the 𝑃 completed datasets.…”

Section: The Multiple Imputations (Mi)mentioning

confidence: 99%

“…These predictive means are then used to match complete and incomplete observations. The non-parametric stage applies the method of Nearest Neighbour Donor to produce original data value from non-missing observation having nearest predictive mean distance close to missing one so as to impute a missing data value [9] and [10]. The PMM is robust to model miss-specification and ensures to yield more plausible imputed values than the regression method when the assumption of normality is violated [20].…”

Section: Predictive Mean Matching (Pmm)mentioning

confidence: 99%

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Imputation methods on retrospective breast cancer data in Tanzania: A comparative study

Abassi¹,

Msengwa²,

Akarro³

2022

JWHCI

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Background: Clinical datasets are at risk of having missing data for several reasons including patients’ failure to attend clinical measurements and measurement recorder’s defects. Missing data can significantly affect the analysis and results might be doubtful due to bias caused by omission incomplete records during analysis especially if a dataset is small. This study aims to compare several imputation methods in terms of efficiency in filling-in missing data so as to increase prediction and classification accuracy in breast cancer dataset. Methodology: Five imputation methods namely series mean, k-nearest neighbour, hot deck, predictive mean matching, expected maximisation via bootstrapping, and multiple imputation by chained equations were applied to replace the missing values to the real breast cancer dataset. The efficiency of imputation methods was compared by using the Root Mean Square Errors and Mean Absolute Errors to obtain a suitable complete dataset. Binary logistic regression and linear discrimination classifiers were applied to the imputed dataset to compare their efficacy on classification and discrimination. Results: The evaluation of imputation methods revealed that the predictive mean matching method was better off compared to other imputation methods. In addition, the binary logistic regression and linear discriminant analyses yield almost similar values on overall classification rates, sensitivity and specificity. Conclusion: The predictive mean matching imputation showed higher accuracy in estimating and replacing missing data values in a real breast cancer dataset under the study. It is a more effective and good approach to handle missing data. We recommend replacing missing data by using predictive mean matching since it is a plausible approach toward multiple imputations for numerical variables. It improves estimation and prediction accuracy over the use complete-case analysis especially when percentage of missing data is not very small.

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Multiple imputation by predictive mean matching in cluster-randomized trials

Cited by 19 publications

References 22 publications

Classification of breast cancer recurrence based on imputed data: a simulation study

Classification of breast cancer recurrence based on imputed data: a simulation study

Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential

Imputation methods on retrospective breast cancer data in Tanzania: A comparative study

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