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

Rheumatology ◽  
2012 ◽  
Vol 52 (2) ◽  
pp. 331-336 ◽  
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

scholarly journals Likelihood Approach for Bayesian Logistic Weighted Model

2020 ◽  
Vol 4 (2) ◽  
pp. 9-12
Author(s):  
Dler H. Kadir
Keyword(s):  
Cognitive Development ◽  
Case Analysis ◽  
Inverse Probability Weighting ◽  
Complete Case Analysis ◽  
Parameter Estimates ◽  
Probability Weighting ◽  
Complete Case ◽  
Inverse Probability ◽  
Very Preterm ◽  
Weighted Model

Increasing the response rate and minimizing non-response rates represent the primary challenges to researchers in performing longitudinal and cohort research. This is most obvious in the area of paediatric medicine. When there are missing data, complete case analysis makes findings biased. Inverse Probability Weighting (IPW) is one of many available approaches for reducing the bias using a complete case analysis. Here, a complete case is weighted by probability inverse of complete cases. The data of this work is collected from the neonatal intensive care unit at Erbil maternity hospital for the years 2012 to 2017. In total, 570 babies (288 male and 282 females) were born very preterm. The aim of this paper is to use inverse probability weighting on the Bayesian logistic model developmental outcome. The Mental Development Index (MDI) approach is used for assessing the cognitive development of those born very preterm. Almost half of the information for the babies was missing, meaning that we do not know whether they have cognitive development issues or they have not. We obtained greater precision in results and standard deviation of parameter estimates which are less in the posterior weighted model in comparison with frequent analysis.

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

2019 ◽  
Vol 12 (1) ◽  
pp. 45-55
Author(s):  
Mwiche Musukuma ◽  
Brian Sonkwe ◽  
Isaac Fwemba ◽  
Patrick Musonda
Keyword(s):  
Spinal Cord ◽  
Multiple Imputation ◽  
Missing Values ◽  
Spinal Cord Injuries ◽  
Case Analysis ◽  
Complete Case Analysis ◽  
Complete Case ◽  
Clinical Complication ◽  
Clinical Complications ◽  
Frankel Grade

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.

Missing data reporting in clinical pharmacy research

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.

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

2019 ◽  
Vol 7 ◽  
pp. 205031211882291 ◽  
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.

The performance of multiple imputation for missing covariates relative to complete case analysis

2010 ◽  
Vol 29 (12) ◽  
pp. 1357-1357 ◽  
Author(s):  
Nicholas J. Horton ◽  
Ian R. White ◽  
James Carpenter
Keyword(s):  
Multiple Imputation ◽  
Case Analysis ◽  
Complete Case Analysis ◽  
Missing Covariates ◽  
Complete Case

scholarly journals Using Multiple Imputation and Inverse Probability Weighting to Adjust for Missing Data in HIV Prevalence Estimates: A Cross-Sectional Study in Mwanza, North Western Tanzania.

2021 ◽  
Author(s):  
TINASHE MHIKE ◽  
Jim Todd ◽  
Mark Urassa ◽  
Neema Mosha
Keyword(s):  
Missing Data ◽  
Case Analysis ◽  
Hiv Prevalence ◽  
Complete Case Analysis ◽  
Probability Weighting ◽  
Population Surveys ◽  
Complete Case ◽  
Inverse Probability ◽  
Multiple Imputations ◽  
Prevalence Estimates

Abstract Background Population surveys and demographic studies are the gold standard for estimating HIV prevalence. However, non-response in these surveys is of major concern especially if it is not random and complete case analysis becomes an inappropriate method to analyse the data. Therefore, a comprehensive analysis that will account for the missing data must be used to obtain unbiased HIV prevalence estimates. MethodsSerological samples were collected from participants who were resident in a Demographic Surveillance System (DSS) in Kisesa, Tanzania. HIV prevalence was estimated using three methods. Firstly, using the Complete case analysis (CCA), assuming data were Missing Completely at Random (MCAR). The other two methods, multiple imputations (MI) and inverse probability weighting (IPW), assumed that non-response was missing at random (MAR). For MI, a logistic regression model adjusting for age, sex, residence, and marital status was used to impute 20 datasets to re-estimate the HIV prevalence. Propensity for participating in the sero-survey and being tested for HIV given age, sex, residence, and marital status were generated using logistic regression models. Using the propensity scores, inverse probability weights were derived for participants who were tested for HIV.ResultsThe overall CCA HIV prevalence estimate was 6.6% (95% CI: 6.0-7.2), with 5.4% (95% CI: 4.6-6.3) in males and 7.3% (95% CI: 6.6-8.1) in females. Using MI, the overall HIV prevalence was 6.8% (95% CI: 6.2-7.5), 6.2% (95% CI: 5.1-7.3) in males and 7.4% (95% CI: 6.6-8.2) in females. Using IPW the overall HIV prevalence was 6.7% (95% CI: 6.1-7.4), with 5.5% (95% CI: 4.7-6.5) in males and 7.7% (95% CI: 7.0 - 8.6) in females. HIV prevalence differed significantly between age groups (p<0.001), with the highest estimate in males aged 35-39 and females aged 40-44, and lowest in both males and females aged 15-19 years.DiscussionThe results showed that both MI and IPW are reliable methods for estimating HIV prevalence in the presence of missing data. MI is superior to CCA and the IPW approaches as it had smaller standard errors and narrower 95% confidence intervals. Therefore, we recommend use of MI in estimating HIV prevalence to address the problem of varied types of missing data. However, further research is needed to determine the bias in estimates from MI and IPW.ConclusionsComplete case analysis underestimates HIV prevalence compared to methods that adjust for missing data. The best method to adjust for missing data in population surveys is through the use of multiple imputations.

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