The Use of Multiple Imputation Techniques on Short-Term Clinical Complications of Patients Presenting with Traumatic Spinal Cord Injuries

Background: With the increase in the use of secondary data in epidemiological studies, the inquiry of how to manage missing data has become more relevant. Our study applied imputation techniques on traumatic spinal cord injuries data; a medical problem where data is generally sporadic. Traumatic spinal cord injuries due to blunt force cause widespread physiological impairments, medical and non-medical problems. The effects of spinal cord injuries are a burden not only to the victims but to their families and to the entire health system of a country. This study also evaluated the causes of traumatic spinal cord injuries in patients admitted to the University Teaching Hospital and factors associated with clinical complications in these patients. Methods: The study used data from medical records of patients who were admitted to the University Teaching Hospital in Lusaka, Zambia. Patients presenting with traumatic spinal cord injuries between 1st January 2013 and 31st December 2017 were part of the study. The data was first analysed using complete case analysis, then multiple imputation techniques were applied, to account for the missing data. Thereafter, both descriptive and inferential analyses were performed on the imputed data. Results: During the study period of interest, a total of 176 patients were identified as having suffered from spinal cord injuries. Road traffic accidents accounted for 56% (101) of the injuries. Clinical complications suffered by these patients included paralysis, death, bowel and bladder dysfunction and pressure sores among other things. Eighty-eight (50%) patients had paralysis. Patients with cervical spine injuries compared to patients with thoracic spine injuries had 87% reduced odds of suffering from clinical complications (OR=0.13, 95% CI{0.08, 0.22}p<.0001). Being paraplegic at discharge increased the odds of developing a clinical complication by 8.1 times (OR=8.01, 95% CI{2.74, 23.99}, p<.001). Under-going an operation increased the odds of having a clinical complication (OR=3.71, 95% CI{=1.99, 6.88}, p<.0001). A patient who presented with Frankel Grade C or E had a 96% reduction in the odds of having a clinical complication (OR=.04, 95% CI{0.02, 0.09} and {0.02, 0.12} respectively, p<.0001) compared to a patient who presented with Frankel Grade A. Conclusion: A comparison of estimates obtained from complete case analysis and from multiple imputations revealed that when there are a lot of missing values, estimates obtained from complete case analysis are unreliable and lack power. Efforts should be made to use ideas to deal with missing values such as multiple imputation techniques. The most common cause of traumatic spinal cord injuries was road traffic accidents. Findings suggest that paralysis had the greatest negative effect on clinical complications. When the category of Frankel Grade increased from A-E, the less likely a patient was likely to succumb to clinical complications. No evidence of an association was found between age, sex and developing a clinical complication.

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Tackling missing radiographic progression data: multiple imputation technique compared with inverse probability weights and complete case analysis

Rheumatology ◽

10.1093/rheumatology/kes245 ◽

2012 ◽

Vol 52 (2) ◽

pp. 331-336 ◽

Cited By ~ 5

Author(s):

M. A. Descalzo ◽

V. V. Garcia ◽

I. Gonzalez-Alvaro ◽

J. Carbonell ◽

A. Balsa ◽

...

Keyword(s):

Multiple Imputation ◽

Radiographic Progression ◽

Case Analysis ◽

Complete Case Analysis ◽

Complete Case ◽

Inverse Probability ◽

Inverse Probability Weights

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Missing data reporting in clinical pharmacy research

American Journal of Health-System Pharmacy ◽

10.1093/ajhp/zxz245 ◽

2019 ◽

Vol 76 (24) ◽

pp. 2048-2052

Author(s):

Sujita W Narayan ◽

Kar Yu Ho ◽

Jonathan Penm ◽

Barbara Mintzes ◽

Ardalan Mirzaei ◽

...

Keyword(s):

Missing Data ◽

Multiple Imputation ◽

Clinical Pharmacy ◽

Case Analysis ◽

The United States ◽

Complete Case Analysis ◽

Case Control Studies ◽

Complete Case ◽

Cross Sectional ◽

Indicator Method

Abstract Purpose This study aimed to document the ways by which missing data were handled in clinical pharmacy research to provide an insight into the amount of attention paid to the importance of missing data in this field of research. Methods Our cross-sectional descriptive report evaluated 10 journals affiliated with pharmacy organizations in the United States, Canada, the United Kingdom, and Australia. Randomized controlled trials, cohort studies, case-control studies, and cross-sectional studies published in 2018 were included. The primary outcome measure was the proportion of studies that reported the handling of missing data in their methods or results. Results A total of 178 studies were included in the analysis. Of these, 19.7% (n = 35) mentioned missing data either in their methods (3.4%, n = 6), results (15.2%, n = 27), or in both sections (1.1%, n = 2). Only 4.5% (n = 8) of the studies mentioned how they handled missing data, the most common method being multiple imputation (n = 3), followed by indicator (n = 2), complete case analysis (n = 2), and simple imputation (n = 1). One study using multiple imputation and both studies using an indicator method also combined other strategies to account for missing data. One study only used complete case analysis for subgroup analysis, and the other study only used this method if a specific baseline variable was missing. Conclusions Very few studies in clinical pharmacy literature report any handling of missing data. This has the potential to lead to biased results. We advocate that researchers should report how missing data were handled to increase the transparency of findings and minimize bias.

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Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: A clinical example

Journal of Clinical Epidemiology ◽

10.1016/j.jclinepi.2006.01.015 ◽

2006 ◽

Vol 59 (10) ◽

pp. 1102-1109 ◽

Cited By ~ 323

Author(s):

Geert J.M.G. van der Heijden ◽

A. Rogier T. Donders ◽

Theo Stijnen ◽

Karel G.M. Moons

Keyword(s):

Missing Values ◽

Case Analysis ◽

Complete Case Analysis ◽

Complete Case ◽

Indicator Method ◽

Diagnostic Research

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How handling missing data may impact conclusions: A comparison of six different imputation methods for categorical questionnaire data

SAGE Open Medicine ◽

10.1177/2050312118822912 ◽

2019 ◽

Vol 7 ◽

pp. 205031211882291 ◽

Cited By ~ 7

Author(s):

Marianne Riksheim Stavseth ◽

Thomas Clausen ◽

Jo Røislien

Keyword(s):

Missing Data ◽

Regression Model ◽

Sample Size ◽

Multiple Imputation ◽

Random Forests ◽

Multiple Correspondence Analysis ◽

Case Analysis ◽

Complete Case Analysis ◽

Complete Case ◽

Imputation Methods

Objectives: Missing data is a recurrent issue in many fields of medical research, particularly in questionnaires. The aim of this article is to describe and compare six conceptually different multiple imputation methods, alongside the commonly used complete case analysis, and to explore whether the choice of methodology for handling missing data might impact clinical conclusions drawn from a regression model when data are categorical. Methods: In addition to the commonly used complete case analysis, we tested the following six imputation methods: multiple imputation using expectation–maximization with bootstrapping, multiple imputation using multiple correspondence analysis, multiple imputation using latent class analysis, multiple hot deck imputation and multivariate imputation by chained equations with two different model specifications: logistic regression and random forests. The methods are tested on real data from a questionnaire-based study in the Norwegian opioid maintenance treatment programme. Results: All methods performed relatively well when the sample size was large (n = 1000). For a smaller sample size (n = 200), the regression estimates depend heavily on the level of missing. When the amount of missing was ⩾20%, in particular, complete case analysis, hot deck and random forests had biased estimates with too low coverage. Multiple imputation using multiple correspondence analysis had the best performance all over. Conclusion: The choice of missing handling methodology has a significant impact on the clinical interpretation of the accompanying statistical analyses. With missing data, the choice of whether to impute or not, and choice of imputation method, can influence clinical conclusion drawn from a regression model and should therefore be given sufficient consideration.

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Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?

Trials ◽

10.1186/s13063-016-1473-3 ◽

2016 ◽

Vol 17 (1) ◽

Cited By ~ 15

Author(s):

Mavuto Mukaka ◽

Sarah A. White ◽

Dianne J. Terlouw ◽

Victor Mwapasa ◽

Linda Kalilani-Phiri ◽

...

Keyword(s):

Randomized Controlled Trials ◽

Multiple Imputation ◽

Case Analysis ◽

Risk Difference ◽

Complete Case Analysis ◽

Controlled Trials ◽

Binary Outcome ◽

Complete Case ◽

Randomized Controlled ◽

Better Than

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Improving upon the efficiency of complete case analysis when covariates are MNAR

Biostatistics ◽

10.1093/biostatistics/kxu023 ◽

2014 ◽

Vol 15 (4) ◽

pp. 719-730 ◽

Cited By ~ 20

Author(s):

Jonathan W. Bartlett ◽

James R. Carpenter ◽

Kate Tilling ◽

Stijn Vansteelandt

Keyword(s):

Missing Values ◽

Case Analysis ◽

Missing At Random ◽

Complete Case Analysis ◽

Complete Case ◽

Independence Assumption ◽

Health And Nutrition ◽

Conditional Independence Assumption ◽

Partially Observed ◽

The Us

Abstract Missing values in covariates of regression models are a pervasive problem in empirical research. Popular approaches for analyzing partially observed datasets include complete case analysis (CCA), multiple imputation (MI), and inverse probability weighting (IPW). In the case of missing covariate values, these methods (as typically implemented) are valid under different missingness assumptions. In particular, CCA is valid under missing not at random (MNAR) mechanisms in which missingness in a covariate depends on the value of that covariate, but is conditionally independent of outcome. In this paper, we argue that in some settings such an assumption is more plausible than the missing at random assumption underpinning most implementations of MI and IPW. When the former assumption holds, although CCA gives consistent estimates, it does not make use of all observed information. We therefore propose an augmented CCA approach which makes the same conditional independence assumption for missingness as CCA, but which improves efficiency through specification of an additional model for the probability of missingness, given the fully observed variables. The new method is evaluated using simulations and illustrated through application to data on reported alcohol consumption and blood pressure from the US National Health and Nutrition Examination Survey, in which data are likely MNAR independent of outcome.

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