Missing Data Approach Research Articles

On Modeling Missing Data in Structural Investigations Based on Tetrachoric Correlations With Free and Fixed Factor Loadings

In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at exploring the consequences of switching between models with free and fixed factor loadings. In a simulation study, confirmatory factor analysis (CFA) models with and without a missing data latent variable were used for investigating the structure of data with and without missing data. In addition, the numbers of columns of data sets with missing data and the amount of missing data were varied. The root mean square error of approximation (RMSEA) results revealed that an additional missing data latent variable recovered the degree-of-model fit characterizing complete data when tetrachoric correlations served as input while comparative fit index (CFI) results showed overestimation of this degree-of-model fit. Whereas the results for fixed factor loadings were in line with the assumptions of modeling missing data, the other results showed only partial agreement. Therefore, modeling missing data with fixed factor loadings is recommended.

Supplemental Material for Correcting Bias in Extreme Groups Design Using a Missing Data Approach

Supplemental Material for Correcting Bias in Extreme Groups Design Using a Missing Data Approach

Correcting bias in extreme groups design using a missing data approach.

Extreme groups design (EGD) refers to the use of a screening variable to inform further data collection, such that only participants with the lowest and highest scores are recruited in subsequent stages of the study. It is an effective way to improve the power of a study under a limited budget, but produces biased standardized estimates. We demonstrate that the bias in EGD results from its inherent missing at random mechanism, which can be corrected using modern missing data techniques such as full information maximum likelihood (FIML). Further, we provide a tutorial on computing correlations in EGD data with FIML using R. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Using observational study data as an external control group for a clinical trial: an empirical comparison of methods to account for longitudinal missing data

BackgroundObservational data are increasingly being used to conduct external comparisons to clinical trials. In this study, we empirically examined whether different methodological approaches to longitudinal missing data affected study conclusions in this setting.MethodsWe used data from one clinical trial and one prospective observational study, both Norwegian multicenter studies including patients with recently diagnosed rheumatoid arthritis and implementing similar treatment strategies, but with different stringency. A binary disease remission status was defined at 6, 12, and 24 months in both studies. After identifying patterns of longitudinal missing outcome data, we evaluated the following five approaches to handle missingness: analyses of patients with complete follow-up data, multiple imputation (MI), inverse probability of censoring weighting (IPCW), and two combinations of MI and IPCW.ResultsWe found a complex non-monotone missing data pattern in the observational study (N = 328), while missing data in the trial (N = 188) was monotone due to drop-out. In the observational study, only 39.0% of patients had complete outcome data, compared to 89.9% in the trial. All approaches to missing data indicated favorable outcomes of the treatment strategy in the trial and resulted in similar study conclusions. Variations in results across approaches were mainly due to variations in estimated outcomes for the observational data.ConclusionsFive different approaches to handle longitudinal missing data resulted in similar conclusions in our example. However, the extent and complexity of missing observational data affected estimated comparative outcomes across approaches, highlighting the need for careful consideration of methods to account for missingness in this setting. Based on this empirical examination, we recommend using a prespecified advanced missing data approach to account for longitudinal missing data, and to conduct alternative approaches in sensitivity analyses.

Multiple imputation to quantify misclassification in observational studies of the cognitively impaired: an application for pain assessment in nursing home residents

BackgroundDespite experimental evidence suggesting that pain sensitivity is not impaired by cognitive impairment, observational studies in nursing home residents have observed an inverse association between cognitive impairment and resident-reported or staff-assessed pain. Under the hypothesis that the inverse association may be partially attributable to differential misclassification due to recall and communication limitations, this study implemented a missing data approach to quantify the absolute magnitude of misclassification of pain, pain frequency, and pain intensity by level of cognitive impairment.MethodsUsing the 2016 Minimum Data Set 3.0, we conducted a cross-sectional study among newly admitted US nursing home residents. Pain presence, severity, and frequency is assessed via resident-reported measures. For residents unable to communicate their pain, nursing home staff document pain based on direct resident observation and record review. We estimate a counterfactual expected level of pain in the absence of cognitive impairment by multiply imputing modified pain indicators for which the values were retained for residents with no/mild cognitive impairment and set to missing for residents with moderate/severe cognitive impairment. Absolute differences (∆) in the presence and magnitude of pain were calculated as the difference between documented pain and the expected level of pain.ResultsThe difference between observed and expected resident reported pain was greater in residents with severe cognitive impairment (∆ = -10.2%, 95% Confidence Interval (CI): -10.9% to -9.4%) than those with moderate cognitive impairment (∆ = -4.5%, 95% CI: -5.4% to -3.6%). For staff-assessed pain, the magnitude of apparent underreporting was similar between residents with moderate impairment (∆ = -7.2%, 95% CI: -8.3% to -6.0%) and residents with severe impairment (∆ = -7.2%, 95% CI: -8.0% to -6.3%). Pain characterized as “mild” had the highest magnitude of apparent underreporting.ConclusionsIn residents with moderate to severe cognitive impairment, documentation of any pain was lower than expected in the absence of cognitive impairment. This finding supports the hypothesis that an inverse association between pain and cognitive impairment may be explained by differential misclassification. This study highlights the need to develop analytic and/or procedural solutions to correct for recall/reporter bias resulting from cognitive impairment.

A Multiple Imputation Approach for Handling Missing Data in Classification and Regression Trees

Decision trees (DTs) is a machine learning technique that searches the predictor space for the variable and observed value that leads to the best prediction when the data are split into two nodes based on the variable and splitting value. The algorithm repeats its search within each partition of the data until a stopping rule ends the search. Missing data can be problematic in DTs because of an inability to place an observation with a missing value into a node based on the chosen splitting variable. Moreover, missing data can alter the selection process because of its inability to place observations with missing values. Simple missing data approaches (e.g., listwise deletion, majority rule, and surrogate split) have been implemented in DT algorithms; however, more sophisticated missing data techniques have not been thoroughly examined. We propose a modified multiple imputation approach to handling missing data in DTs, and compare this approach with simple missing data approaches as well as single imputation and a multiple imputation with prediction averaging via Monte Carlo Simulation. This study evaluated the performance of each missing data approach when data were MAR or MCAR. The proposed multiple imputation approach and surrogate splits had superior performance with the proposed multiple imputation approach performing best in the more severe missing data conditions. We conclude with recommendations for handling missing data in DTs.

Planned missing data in early literacy interventions: A replication study with an additional gold standard.

IntroductionIn a digital early literacy intervention RCT, children born late preterm fell behind peers when in a control condition, but outperformed them when assigned to the intervention. Results did however not replicate previous findings. Replication is often complicated by resource quality. Gold Standard measures are generally time-intensive and costly, while they closely align with, and are more sensitive to changes in, early literacy and language performance. A planned missing data approach, leaving these gold standard measures incomplete, might aid in addressing the origin(s) of non-replication.MethodsParticipants after consent were 695 p Dutch primary school pupils of normal and late preterm birth. The high-quality measures, in additional to simpler but complete measures, were intentionally administered to a random subsample of children. Five definitions of gold standard alignment were evaluated.ResultsTwo out of five gold standard levels improved precision compared to the original results. The lowest gold standard level did not lead to improvement: precision was actually diminished. In two gold standard definitions, an alphabetical factor and a writing-only factor the model estimates were comparable to the original results. Only the most precise definition of the gold standard level replicated the original results.ConclusionGold standard measures could only be used to improve model efficiency in RCT-designs under sufficiently high convergent validity.

R&D in Europe: Sector Decomposition of Sources of (in)Efficiency

Measuring the efficiency of research and development (R&D) expenditure and innovation policy has gained attention in recent years. This research examines the efficiency of 29 selected European countries for the period ranging from 2007 to 2017 in achieving and obtaining R&D goals. The methodology applied is the data envelopment analysis approach with the inclusion of the missing data approach. The contributions of this research include the following: dynamic analysis is conducted to track changes of (in)efficiencies over time; the decomposition of the efficiency is done by separating the main variables of interest into the private, higher education, and government sectors; and the robustness of the results is evaluated, which is often ignored in the literature. The results of the analysis are discussed with possible directions for inefficient countries. The rankings provided in the empirical part of the study confirm previous findings on disparities between the European countries with respect to innovation and the R&D sector.

SLE clinical trials: impact of missing data on estimating treatment effects

ObjectiveA common problem in clinical trials is missing data due to participant dropout and loss to follow-up, an issue which continues to receive considerable attention in the clinical research community....

Large-scale plant-wide process modeling and hierarchical monitoring: A distributed Bayesian network approach

In this work, a systematic distributed Bayesian network approach is proposed for modeling and monitoring large-scale plant-wide processes. First, to deal with the large-scale process modeling issue, the entire plant-wide process is decomposed into blocks and Bayesian networks are constructed for different blocks. Subsequently, distributed Bayesian network blocks are fused into a global Bayesian network with a proper designed algorithm. For fault detection, a missing data approach is proposed for state estimation, based on which the T2 and Q statistics are constructed. Finally, a Bayesian decision fusion mechanism is established for hierarchical monitoring of variables, unit blocks and the global industrial plant. For fault isolation, a Bayesian contribution index is further developed and the corresponding isolation scheme is proposed. Simulation results on the plant-wide Tennessee Eastman process show that the distributed Bayesian network approach can be feasible for modeling large-scale process. Furthermore, the proposed hierarchical monitoring scheme provides informative multi-level reference results for further diagnosis and isolation.

Stop or Continue Data Collection: A Nonignorable Missing Data Approach for Continuous Variables

Abstract We present an approach to inform decisions about nonresponse follow-up sampling. The basic idea is (i) to create completed samples by imputing nonrespondents’ data under various assumptions about the nonresponse mechanisms, (ii) take hypothetical samples of varying sizes from the completed samples, and (iii) compute and compare measures of accuracy and cost for different proposed sample sizes. As part of the methodology, we present a new approach for generating imputations for multivariate continuous data with nonignorable unit nonresponse. We fit mixtures of multivariate normal distributions to the respondents’ data, and adjust the probabilities of the mixture components to generate nonrespondents’ distributions with desired features. We illustrate the approaches using data from the 2007 U.S. Census of Manufactures.

A Missing Data Approach to Data-Driven Filtering and Control

In filtering, control, and other mathematical engineering areas, it is common to use a model-based approach, which splits the problem into two steps: 1) model identification and 2) model-based design. Despite its success, the model-based approach has the shortcoming that the design objective is not taken into account at the identification step, i.e., the model is not optimized for its intended use. This technical note proposes an approach for data-driven filtering and control that combines the identification and the model-based design into one joint problem. The signal of interest is modeled as a missing part of a trajectory of the data generating system. Subsequently, the missing data estimation problem is reformulated as a mosaic-Hankel structured matrix low-rank approximation/completion problem. A local optimization method, based on the variable projections principle, is then used for its numerical solution. The missing data estimation approach and the solution method proposed are illustrated on filtering and smoothing examples.

A Missing Data Approach to Correct for Direct and Indirect Range Restrictions with a Dichotomous Criterion: A Simulation Study.

A recurring methodological problem in the evaluation of the predictive validity of selection methods is that the values of the criterion variable are available for selected applicants only. This so-called range restriction problem causes biased population estimates. Correction methods for direct and indirect range restriction scenarios have widely studied for continuous criterion variables but not for dichotomous ones. The few existing approaches are inapplicable because they do not consider the unknown base rate of success. Hence, there is a lack of scientific research on suitable correction methods and the systematic analysis of their accuracies in the cases of a naturally or artificially dichotomous criterion. We aim to overcome this deficiency by viewing the range restriction problem as a missing data mechanism. We used multiple imputation by chained equations to generate complete criterion data before estimating the predictive validity and the base rate of success. Monte Carlo simulations were conducted to investigate the accuracy of the proposed correction in dependence of selection ratio, predictive validity, and base rate of success in an experimental design. In addition, we compared our proposed missing data approach with Thorndike’s well-known correction formulas that have only been used in the case of continuous criterion variables so far. The results show that the missing data approach is more accurate in estimating the predictive validity than Thorndike’s correction formulas. The accuracy of our proposed correction increases as the selection ratio and the correlation between predictor and criterion increase. Furthermore, the missing data approach provides a valid estimate of the unknown base rate of success. On the basis of our findings, we argue for the use of multiple imputation by chained equations in the evaluation of the predictive validity of selection methods when the criterion is dichotomous.

Bayesian modelling of health insurance losses

The purpose of this paper is to build a model for aggregate losses which constitutes a crucial step in evaluating premiums for health insurance systems. It aims at obtaining the predictive distribution of the aggregate loss within each age class of insured persons over the time horizon involved in planning employing the Bayesian methodology. The model proposed using the Bayesian approach is a generalization of the collective risk model, a commonly used model for analysing risk of an insurance system. Aggregate loss prediction is based on past information on size of loss, number of losses and size of population at risk. In modelling the frequency and severity of losses, the number of losses is assumed to follow a negative binomial distribution, individual loss sizes are independent and identically distributed exponential random variables, while the number of insured persons in a finite number of possible age groups is assumed to follow the multinomial distribution. Prediction of aggregate losses is based on the Gibbs sampling algorithm which incorporates the missing data approach.

Testing for the fairness and predictive validity of research funding decisions: A multilevel multiple imputation for missing data approach using ex‐ante and ex‐post peer evaluation data from the Austrian science fund

It is essential for research funding organizations to ensure both the validity and fairness of the grant approval procedure. The ex‐ante peer evaluation (EXANTE) of N = 8,496 grant applications submitted to the Austrian Science Fund from 1999 to 2009 was statistically analyzed. For 1,689 funded research projects an ex‐post peer evaluation (EXPOST) was also available; for the rest of the grant applications a multilevel missing data imputation approach was used to consider verification bias for the first time in peer‐review research. Without imputation, the predictive validity of EXANTE was low (r = .26) but underestimated due to verification bias, and with imputation it was r = .49. That is, the decision‐making procedure is capable of selecting the best research proposals for funding. In the EXANTE there were several potential biases (e.g., gender). With respect to the EXPOST there was only one real bias (discipline‐specific and year‐specific differential prediction). The novelty of this contribution is, first, the combining of theoretical concepts of validity and fairness with a missing data imputation approach to correct for verification bias and, second, multilevel modeling to test peer review‐based funding decisions for both validity and fairness in terms of potential and real biases.

Cost-Effectiveness of Radioguided Occult Lesion Localization (ROLL) Versus Wire-Guided Localization (WGL) in Breast Conserving Surgery for Nonpalpable Breast Cancer: Results from a Randomized Controlled Multicenter Trial

Accurate preoperative localization of nonpalpable breast cancer is essential to achieve complete resection. Radioguided occult lesion localization (ROLL) has been introduced as an alternative for wire-guided localization (WGL). Although efficacy of ROLL has been established in a randomized controlled trial, cost-effectiveness of ROLL compared with WGL is not yet known. The objective of this study was to determine whether ROLL has acceptable cost-effectiveness compared with WGL. An economic evaluation was performed along with a randomized controlled trial (ClinicalTrials.gov, No. NCT00539474). Women (>18 years) with histologically proven nonpalpable breast cancer and eligible for breast conserving treatment with sentinel node procedure were randomized to ROLL (n = 162) or WGL (n = 152). Empirical data on direct medical costs were collected, and changes in quality of life were measured over a 6-month period. Bootstrapping was used to assess uncertainty in cost-effectiveness estimates, and sensitivity of the results to the missing data approach was investigated. In total, 314 patients with 316 invasive breast cancers were enrolled. On average ROLL required the same time as WGL for the surgical procedure (119 vs 118 min), resulted in a 7 % higher reinterventions risk, and 13 % more complications. Quality of life effects were similar (difference 0.00 QALYs 95 % CI (-0.04-0.05). Total costs were also similar for ROLL and WGL (+<euro>26 per patient 95 % CI <euro>-250-311). ROLL is comparable to WGL with respect to both costs and quality of life effects as measured with the EQ5D and will therefore not lead to more cost-effective medical care.

Abstract P5-15-03: Cost-effectiveness of ‘radioguided occult lesion localization’ (ROLL) versus ‘wire-guided localization’ (WGL) in breast conserving surgery for non-palpable breast cancer: results from a randomized controlled multicentre trial

Abstract Background: Accurate pre-operative localization of non-palpable breast cancer is essential to achieve complete resection. Radio-guided occult lesion localization (ROLL) has been introduced as an alternative for wire-guided localization (WGL). Although efficacy of ROLL has been established in a RCT, cost-effectiveness of ROLL compared with WGL is not yet known. Objective. To determine whether ROLL has acceptable cost-effectiveness compared with WGL. Methods. An economic evaluation was performed alongside a randomized controlled trial (ClinicalTrials.gov, number NCT00539474). Women (&amp;gt;18 years) with histologically proven non-palpable breast cancer and eligible for breast conserving treatment with sentinel node procedure were randomized to ROLL(n = 162) or WGL (n = 152). Empirical data on direct medical costs was collected, and changes in quality of life were measured over a 6 months period. Bootstrapping was used to assess uncertainty in cost-effectiveness estimates, and sensitivity of the results to the missing data approach was investigated. Results. In total, 314 patients with 316 invasive breast cancers were enrolled. On average ROLL required the same time as WGL for the initial procedure (119 vs. 118 min.), resulted in a 7% higher re-interventions risk (27% vs. 20%, p = 0.163), a 13% higher complication risk (30% vs. 17%, p = 0.006) but showed similar health effects (difference 0.00 QALYs 95%CI (−0.04 to 0.05). Total costs were also similar for ROLL and WGL (+ €26 per patient 95% CI-250 to 311). Conclusion. ROLL is comparable to WGL with respect to both costs and quality of life, and will therefore not lead to more cost-effective medical care. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P5-15-03.

Latent demographic profile estimation in hard-to-reach groups

The sampling frame in most social science surveys excludes members of certain groups, known as hard-to-reach groups. These groups, or sub-populations, may be difficult to access (the homeless, e.g.), camouflaged by stigma (individuals with HIV/AIDS), or both (commercial sex workers). Even basic demographic information about these groups is typically unknown, especially in many developing nations. We present statistical models which leverage social network structure to estimate demographic characteristics of these subpopulations using Aggregated relational data (ARD), or questions of the form "How many X's do you know?" Unlike other network-based techniques for reaching these groups, ARD require no special sampling strategy and are easily incorporated into standard surveys. ARD also do not require respondents to reveal their own group membership. We propose a Bayesian hierarchical model for estimating the demographic characteristics of hard-to-reach groups, or latent demographic profiles, using ARD. We propose two estimation techniques. First, we propose a Markov-chain Monte Carlo algorithm for existing data or cases where the full posterior distribution is of interest. For cases when new data can be collected, we propose guidelines and, based on these guidelines, propose a simple estimate motivated by a missing data approach. Using data from McCarty et al. [Human Organization60 (2001) 28-39], we estimate the age and gender profiles of six hard-to-reach groups, such as individuals who have HIV, women who were raped, and homeless persons. We also evaluate our simple estimates using simulation studies.

Fault diagnosis using contribution plots without smearing effect on non-faulty variables

Isolating faulty variables to provide additional information about a process fault is a crucial step in the diagnosis of a process fault. There are two types of data-driven approaches for isolating faulty variables. One is the supervised method, which requires the datasets of known faults to define a fault subspace or an abnormal operating region for each faulty mode. This type of approach is not practical for an industrial process, since the known event lists might not exist for some industrial processes. The counterpart is to isolate faulty variables without a priori knowledge, using, for example, a contribution plot, which is a popular tool in the unsupervised category. However, it is well known that this approach suffers from the smearing effect, which may mislead the faulty variables of the detected faults. In the presented work, a contribution plot without the smearing effect on non-faulty variables was derived based on missing data analysis. Two benchmark examples, the continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) process, were provided to compare the fault isolation performances of the alternatives using the missing data approach.

Mask estimation and imputation methods for missing data speech recognition in a multisource reverberant environment

We present an automatic speech recognition system that uses a missing data approach to compensate for challenging environmental noise containing both additive and convolutive components. The unreliable and noise-corrupted (“missing”) components are identified using a Gaussian mixture model (GMM) classifier based on a diverse range of acoustic features. To perform speech recognition using the partially observed data, the missing components are substituted with clean speech estimates computed using both sparse imputation and cluster-based GMM imputation. Compared to two reference mask estimation techniques based on interaural level and time difference-pairs, the proposed missing data approach significantly improved the keyword accuracy rates in all signal-to-noise ratio conditions when evaluated on the CHiME reverberant multisource environment corpus. Of the imputation methods, cluster-based imputation was found to outperform sparse imputation. The highest keyword accuracy was achieved when the system was trained on imputed data, which made it more robust to possible imputation errors.