Impact Of Imputation Research Articles

Impact of Imputation on Performance of Goodness-of-Fit Tests for the Logistic Panel Data Model

Goodness-of-fit tests aim at discerning model misspecification and identifying a model which is poorly fitting a given data set. They are methods used to determine the suitability of the fitted model. The subject of assessment of goodness-of-fit in logistic regression model has attracted the attention of many scientists and researchers. Several methods for assessing how well observed data can fit into logistic regression models have been proposed and discussed where test statistics are functions of the observed data values and their corresponding estimated values after parameter estimation. Considering a correctly specified panel data model with balanced data set, the conditional maximum likelihood estimates of the parameters are less biased and the estimated response variable values are actually in the neighborhood of the observed values. Relative to the induced biases of the parameter estimates resulting from imputation of missing covariates, the performances of the goodness-of-fit tests may be misjudged. This study looks at the susceptibility of the goodness-of-fit tests for logistic panel data models with imputed covariates. Simulation results show that Bayesian imputation impacts less on the goodness-of-fit test statistics and therefore stands out as the better technique against other classical imputation methods. An increased proportion of missingness however appeared to reduce the confidence interval of the test statistics which in turn reduces the chances of adopting the model under study.

Economic evaluation of remote monitoring of patients with an implantable cardiac defibrillator (REMOTE-CIED study)

Introduction Remote patient monitoring (RPM) of heart failure patients has the potential to reduce healthcare resource use and costs, but current evidence has been inconclusive. This study aims assess the impact of RPM of heart failure patients with an implantable cardioverter defibrillator on medical resource use, direct medical costs, quality-adjusted life years (QALYs), and travel time of patients, and to estimate its commercial headroom in the Netherlands and Germany. Methods Data from the REMOTE-CIED randomized controlled trial were used to calculate differences in length of hospital stay, outpatient clinic visits, telephone consults, emergency room visits, and travel time between patients on in-clinic follow-up and RPM in the Netherlands, Germany, and France. Incremental cardiac-related healthcare costs and QALYs were calculated and used to calculate the commercial headroom of RPM in the Netherlands and Germany. The impact of imputation, parameter, and case-mix uncertainty on these outcomes was explored using probabilistic analysis. Results Length of hospitalization, number of unscheduled admissions, and number of outpatient visits were lower in the remote monitoring group in all three countries. Number of hospital admissions was higher, and number of calls was lower in the Netherlands and Germany but not in France. Costs were lower in both the Netherlands (−€1041, 95% confidence interval (CI): −€3308, €1005) and Germany (−€2865, 95% CI: −€7619, €1105), while incremental effectiveness differed: −0.003 (95% CI: −0.114, 0.107) QALY in the Netherlands and +0.086 (95% CI: −0.083, 0.256) in Germany. Commercial headroom was estimated at €881 (95% CI: −€5430, €7208) in the Netherlands and €5005 (95% CI: −€1339, €11,960) in Germany. Discussion RPM was found to result in reduced medical resource use and travel time. Whether it is cost saving or cost effective strongly depends on the costs of remote monitoring. Trial registration number and trial register ClinicalTrials.gov: NCT01691586.

Combining information from linkage and association mapping for next-generation sequencing longitudinal family data.

In this analysis, we investigate the contributions that linkage-based methods, such as identical-by-descent mapping, can make to association mapping to identify rare variants in next-generation sequencing data. First, we identify regions in which cases share more segments identical-by-descent around a putative causal variant than do controls. Second, we use a two-stage mixed-effect model approach to summarize the single-nucleotide polymorphism data within each region and include them as covariates in the model for the phenotype. We assess the impact of linkage disequilibrium in determining identical-by-descent states between individuals by using markers with and without linkage disequilibrium for the first part and the impact of imputation in testing for association by using imputed genome-wide association studies or raw sequence markers for the second part. We apply the method to next-generation sequencing longitudinal family data from Genetic Association Workshop 18 and identify a significant region at chromosome 3: 40249244-41025167 (p-value = 2.3 × 10−3).

The Negative Impact of Missing Value Imputation in Classification of Diabetes Dataset and Solution for Improvement

The common problem for data quality is missing data. The real datasets have lot of missing values. Missing values imputation is a challenging issue in machine learning and data mining. Missing data should be carefully handled; otherwise it affects the quality of the mining process or the performance of classification algorithms. Mean method of imputation is the most common method to replace the missing values. In this paper, we address the negative impact of missing value imputation and solution for improvement while evaluating the performance of kNN algorithm for classification of Diabetes data. We selected diabetes dataset because it contains lot of missing values and the impact of imputation is very obvious. To measure the performance, we used Accuracy and Error rate as the metrics.