Evaluation of Imputation Methods in Ovarian Tumor Diagnostic Models Using Generalized Linear Models and Support Vector Machines

ObjectiveTo compare the effects of accounting for different missing data mechanisms in an investigation of the role of lifecourse socio-economic position (SEP) on later-life verbal cognitive ability.DesignTwo UK prospective cohort...

Background . Properly constructed decision support systems (DSS) for modelling and forecasting behaviour of dynamic systems provide a possibility for taking into consideration uncertainties of probabilistic, statistical and structural types what results in higher quality of developed models and estimated forecasts. Objective . To consider general reasons for loosing (missing) data in statistical data analysis; to provide categorization of missing data into several groups, and identify the reasons for missing measurements; to provide stepwise system methodology for uncertainty analysis and selection of data imputation techniques; to give an insight into some popular missing values imputation techniques regarding their possible applications. Methods . To solve the problems mentioned the following methods have been used: data categorization approach from business or practical point of view that is necessary for discovering the reasons for availability of systemic and/or random missing values; the modern systemic methodology was hired for analysis of uncertainty causes and missing values imputation; the decision tree based imputation procedures; EM algorithm and regression model approach to forecasting missing data using forecasting functions. Results . The main results of the study are in categorization of the missing data into groups; development of systemic methodology for analysis of uncertainty causes and missing values imputation; providing an analysis for possibilities of missing values imputation with decision trees, EM algorithm and regression models; development of multistep forecasting functions on the basis of autoregression models; illustration of application of some selected perspective methods for missing data imputation. Conclusions . We proposed the six steps system methodology for data imputation which stresses that selection of correct method for imputation is tightly connected with the step-by-step analysis of the gaps causes and finding an appropriate technique for their imputation. The results of imputation sometimes are rather far from the existing data and should be smoothed or even broken from the sample due to their incorrectness. For such cases it should be proposed a new probabilistic-regression method which allows define parameters of the probability interval for the regression aiming missing data imputation. A series of computing experiments performed with EM algorithm, forecast regression based imputation technique and some other approaches shows that it is possible to reach high quality results regarding correct processing of data with missing values.

Based on this systematic review and meta-analysis of 31 randomized controlled trials including 8672 patients, moderate certainty evidence suggests that probiotics are effective for preventing CDAD (NNTB = 42 patients, 95% CI 32 to 58). Our post hoc subgroup analyses to explore heterogeneity indicated that probiotics are effective among trials with a CDAD baseline risk >5% (NNTB = 12; moderate certainty evidence), but not among trials with a baseline risk ≤5% (low to moderate certainty evidence). Although adverse effects were reported among 32 included trials, there were more adverse events among patients in the control groups. The short-term use of probiotics appears to be safe and effective when used along with antibiotics in patients who are not immunocompromised or severely debilitated. Despite the need for further research, hospitalized patients, particularly those at high risk of CDAD, should be informed of the potential benefits and harms of probiotics.

ObjectivesTo describe how systematic reviewers are reporting missing data for dichotomous outcomes, handling them in the analysis and assessing the risk of associated bias.MethodsWe searched MEDLINE and the Cochrane Database...

(1) Background: Gene-expression data usually contain missing values (MVs). Numerous methods focused on how to estimate MVs have been proposed in the past few years. Recent studies show that those imputation algorithms made little difference in classification. Thus, some scholars believe that how to select the informative genes for downstream classification is more important than how to impute MVs. However, most feature-selection (FS) algorithms need beforehand imputation, and the impact of beforehand MV imputation on downstream FS performance is seldom considered. (2) Method: A modified chi-square test-based FS is introduced for gene-expression data. To deal with the challenge of a small sample size of gene-expression data, a heuristic method called recursive element aggregation is proposed in this study. Our approach can directly handle incomplete data without any imputation methods or missing-data assumptions. The most informative genes can be selected through a threshold. After that, the best-first search strategy is utilized to find optimal feature subsets for classification. (3) Results: We compare our method with several FS algorithms. Evaluation is performed on twelve original incomplete cancer gene-expression datasets. We demonstrate that MV imputation on an incomplete dataset impacts subsequent FS in terms of classification tasks. Through directly conducting FS on incomplete data, our method can avoid potential disturbances on subsequent FS procedures caused by MV imputation. An experiment on small, round blue cell tumor (SRBCT) dataset showed that our method found additional genes besides many common genes with the two compared existing methods.

SummaryFinancial statement fraud is a global problem for investors, audit firms, regulators, and other stakeholders. Fraud detection can be regarded as a binary classification problem with a false negative being more expensive than a false positive. Although existing studies have made great efforts to detect fraud using various data‐mining techniques, the difference in misclassification costs is seldom considered. In this study, we propose a cost‐sensitive cascade forest (CSCF) for fraud detection, which places heavy penalty on false negative prediction and self‐adjusts the depth of a cascade forest according to the classifier’s recall (i.e. the classifier’s sensitivity). As missing values are ubiquitous in fraud research, we also explore the effect of selected missing data treatments on prediction performance, including complete case analysis, three selected classic statistical mechanisms (zero, mean, and modified mean imputation), and two machine learning (K‐nearest neighbor [KNN] and random forest [RF]) approaches. The experimental results show that the proposed CSCF significantly improves the fraud prediction in comparison with one of the latest fraud detection models using the RUSBoost algorithm. Comparing different missing value treatments, even though RUSBoost and CSCF perform well when using complete case analysis, we find that the best performance is achieved when CSCF is used with missing data imputed as zero. Such treatment further improves the performance, and results in an area under curve (AUC) score of 0.82 compared to the highest AUC (0.71) from the baseline model. Supplementary analysis further reveals that the low AUC of complete case analysis for the two examined models persists under different training sizes. Thus, our findings shed light on the potential benefits of missing value imputation for the model’s performance for fraud detection.

Objective: Data being missed is an unavoidable problem in cohort studies. This paper compares the imputation effect of eight common missing data imputation methods involved in cutting longitudinal data through simulation study to provide a valuable reference for the treatment of missing data in longitudinal studies. Methods: The simulation study is based on R language software and generates missing longitudinal data by the Monte Carlo method. By comparing the average absolute deviation, average relative deviation, and TypeⅠerror from the regression analysis of different imputation methods, the imputation effect of varying imputation methods on missing longitudinal data and the influence on subsequent multivariate analysis are evaluated. Results: The mean imputation, k nearest neighbor (KNN), regression imputation, and random forest all have a similar imputation effect, which is also steady. However, the hot deck is inferior to the above imputation methods. K-means clustering and expectation maximization (EM) algorithm are among the worst and unstable. Mean imputation, EM algorithm, random forest, KNN, and regression imputation can control TypeⅠerror. Still, multiple imputations, hot deck, and K-means clustering cannot effectively manage the TypeⅠerror. Conclusions: For missing data in longitudinal studies, mean imputation, KNN, regression imputation, and random forest can be used as better imputation methods under the mechanism of missing at random. When the missing ratio is not too large, multiple imputations and hot deck can also perform well, but K-means clustering and EM algorithm are not recommended.

Missing Data and Imputation: A Practical Illustration in a Prognostic Study on Low Back Pain

Dr. Rodu and colleagues have made several comments regarding our article1 that merit a response.

Although missing outcome data are an important problem in randomized trials and observational studies, methods to address this issue can be difficult to apply. Using simulated data, the authors compared 3 methods to handle missing outcome data: 1) complete case analysis; 2) single imputation; and 3) multiple imputation (all 3 with and without covariate adjustment). Simulated scenarios focused on continuous or dichotomous missing outcome data from randomized trials or observational studies. When outcomes were missing at random, single and multiple imputations yielded unbiased estimates after covariate adjustment. Estimates obtained by complete case analysis with covariate adjustment were unbiased as well, with coverage close to 95%. When outcome data were missing not at random, all methods gave biased estimates, but handling missing outcome data by means of 1 of the 3 methods reduced bias compared with a complete case analysis without covariate adjustment. Complete case analysis with covariate adjustment and multiple imputation yield similar estimates in the event of missing outcome data, as long as the same predictors of missingness are included. Hence, complete case analysis with covariate adjustment can and should be used as the analysis of choice more often. Multiple imputation, in addition, can accommodate the missing-not-at-random scenario more flexibly, making it especially suited for sensitivity analyses.

A massive amount of data is emerging continuously in the era of big data; the missing value is a common yet challenging problem. Missing data or missing value can be described as the data whose values in any given dataset are unknown. It is a recurrent phenomenon and can give biased results from the data to be observed and affect the value of the learning process. Therefore, it is mandatory to use missing value imputation techniques. Missing value imputation provides (MVI) optimal solutions for missing values in a dataset. There are diverse missing value imputation techniques such as statistical method and machine learning methods proposed till date, each having their own significances and flaws. Reasonably, good imputation results may be produced by machine learning MVI approaches but they take greater imputation times than statistical approaches usually. In this paper, we have reviewed some of the missing value imputation approaches proposed. In order to show the efficiency of their proposed approaches, they had compared their proposed methods with some baseline imputation algorithms like mode/median, k-nearest neighbor, Naive Bayes, support vector machine. The outcome of each proposed method is analyzed and their extension scopes from the perspectives of research focus. The main idea of this literature is to give a general review of missing value imputation approaches.

Missing data is a common problem in cancer research. While simple methods such as completecase (C-C) analysis are commonly employed for handling this problem, several studies have shown that these methods led to biased estimates. We aim to address the methodological issues in development of a prognostic model with missing data. Three hundred and ten breast cancer patients were enrolled. At first, patients with missing data on any of four candidate variables were omitted. Secondly, missing data were imputed 10 times. Cox regression model was fitted to the C-C and imputed data. Results were compared in terms of variables retained in the model, discrimination ability, and goodness of fit. Some variables lost their effect in complete-case analysis, due to loss in power, but reached significance level after imputation of missing data. Discrimination ability and goodness of fit of imputed data sets model was higher than that of complete-case model (C-index 76% versus 72%; Likelihood Ratio Test 51.19 versus 32.44). Our findings showed inappropriateness of ad hoc complete-case analysis. This approach led to loss in power and imprecise estimates. Application of multiple imputation techniques to avid such problems is recommended.

Propensity score analysis is a popular method to control for confounding in observational studies. A challenge in propensity methods is missing values in confounders. Several strategies for handling missing values exist, but guidance in choosing the best method is needed. In this simulation study, we compared four strategies of handling missing covariate values in propensity matching and propensity weighting. These methods include: complete case analysis, missing indicator method, multiple imputation and combining multiple imputation and missing indicator method. Concurrently, we aimed to provide guidance in choosing the optimal strategy. Simulated scenarios varied regarding missing mechanism, presence of effect modification or unmeasured confounding. Additionally, we demonstrated how missingness graphs help clarifying the missing structure. When no effect modification existed, complete case analysis yielded valid causal treatment effects even when data were missing not at random. In some situations, complete case analysis was also able to partially correct for unmeasured confounding. Multiple imputation worked well if the data were missing (completely) at random, and if the imputation model was correctly specified. In the presence of effect modification, more complex imputation models than default options of commonly used statistical software were required. Multiple imputation may fail when data are missing not at random. Here, combining multiple imputation and the missing indicator method reduced the bias as the missing indicator variable can be a proxy for unobserved confounding. The optimal way to handle missing values in covariates of propensity score models depends on the missing data structure and the presence of effect modification. When effect modification is present, default settings of imputation methods may yield biased results even if data are missing at random.

BackgroundObservational data on composite scores often comes with missing component information. When a complete-case (CC) analysis of composite scores is unbiased, preferable approaches of dealing with missing component information should also be unbiased and provide a more precise estimate. We assessed the performance of several methods compared to CC analysis in estimating the means of common composite scores used in axial spondyloarthritis research.MethodsIndividual mean imputation (IMI), the modified formula method (MF), overall mean imputation (OMI), and multiple imputation of missing component values (MI) were assessed either analytically or by means of simulations from available data collected across Europe. Their performance in estimating the means of the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), the Bath Ankylosing Spondylitis Functional Index (BASFI), and the Ankylosing Spondylitis Disease Activity Score based on C-reactive protein (ASDAS-CRP) in cases where component information was set missing completely at random was compared to the CC approach based on bias, variance, and coverage.ResultsLike the MF method, IMI uses a modified formula for observations with missing components resulting in modified composite scores. In the case of an unbiased CC approach, these two methods yielded representative samples of the distribution arising from a mixture of the original and modified composite scores, which, however, could not be considered the same as the distribution of the original score. The IMI and MF method are, thus, intrinsically biased. OMI provided an unbiased mean but displayed a complex dependence structure among observations that, if not accounted for, resulted in severe coverage issues. MI improved precision compared to CC and gave unbiased means and proper coverage as long as the extent of missingness was not too large.ConclusionsMI of missing component values was the only method found successful in retaining CC’s unbiasedness and in providing increased precision for estimating the means of BASDAI, BASFI, and ASDAS-CRP. However, since MI is susceptible to incorrect implementation and its performance may become questionable with increasing missingness, we consider the implementation of an error-free CC approach a valid and valuable option.Trial registrationNot applicable as study uses data from patient registries.

Uncertainty method improved on best–worst case analysis in a binary meta-analysis