Multiple Imputation Methods Research Articles

Multiple Data Imputation Methods Advance Risk Analysis and Treatability of Co-occurring Inorganic Chemicals in Groundwater.

Accurately assessing and managing risks associated with inorganic pollutants in groundwater is imperative. Historic water quality databases are often sparse due to rationale or financial budgets for sample collection and analysis, posing challenges in evaluating exposure or water treatment effectiveness. We utilized and compared two advanced multiple data imputation techniques, AMELIA and MICE algorithms, to fill gaps in sparse groundwater quality data sets. AMELIA outperformed MICE in handling missing values, as MICE tended to overestimate certain values, resulting in more outliers. Field data sets revealed that 75% to 80% of samples exhibited no co-occurring regulated pollutants surpassing MCL values, whereas imputed values showed only 15% to 55% of the samples posed no health risks. Imputed data unveiled a significant increase, ranging from 2 to 5 times, in the number of sampling locations predicted to potentially exceed health-based limits and identified samples where 2 to 6 co-occurring chemicals may occur and surpass health-based levels. Linking imputed data to sampling locations can pinpoint potential hotspots of elevated chemical levels and guide optimal resource allocation for additional field sampling and chemical analysis. With this approach, further analysis of complete data sets allows state agencies authorized to conduct groundwater monitoring, often with limited financial resources, to prioritize sampling locations and chemicals to be tested. Given existing data and time constraints, it is crucial to identify the most strategic use of the available resources to address data gaps effectively. This work establishes a framework to enhance the beneficial impact of funding groundwater data collection by reducing uncertainty in prioritizing future sampling locations and chemical analyses.

Imputation methods for mixed datasets in bioarchaeology

Missing data is a prevalent problem in bioarchaeological research and imputation could provide a promising solution. This work simulated missingness on a control dataset (481 samples × 41 variables) in order to explore imputation methods for mixed data (qualitative and quantitative data). The tested methods included Random Forest (RF), PCA/MCA, factorial analysis for mixed data (FAMD), hotdeck, predictive mean matching (PMM), random samples from observed values (RSOV), and a multi-method (MM) approach for the three missingness mechanisms (MCAR, MAR, and MNAR) at levels of 5%, 10%, 20%, 30%, and 40% missingness. This study also compared single imputation with an adapted multiple imputation method derived from the R package “mice”. The results showed that the adapted multiple imputation technique always outperformed single imputation for the same method. The best performing methods were most often RF and MM, and other commonly successful methods were PCA/MCA and PMM multiple imputation. Across all criteria, the amount of missingness was the most important parameter for imputation accuracy. While this study found that some imputation methods performed better than others for the control dataset, each imputation method has advantages and disadvantages. Imputation remains a promising solution for datasets containing missingness; however when making a decision it is essential to consider dataset structure and research goals.

Federated Multiple Imputation for Variables that Are Missing Not At Random in Distributed Electronic Health Records.

Large electronic health records (EHR) have been widely implemented and are available for research activities. The magnitude of such databases often requires storage and computing infrastructure that are distributed at different sites. Restrictions on data-sharing due to privacy concerns have been another driving force behind the development of a large class of distributed and/or federated machine learning methods. While missing data problem is also present in distributed EHRs, albeit potentially more complex, distributed multiple imputation (MI) methods have not received as much attention. An important advantage of distributed MI, as well as distributed analysis, is that it allows researchers to borrow information across data sites, mitigating potential fairness issues for minority groups that do not have enough volume at certain sites. In this paper, we propose a communication-efficient and privacy-preserving distributed MI algorithms for variables that are missing not at random.

A multiple imputation method using population information

Multiple imputation (MI) is effectively used to deal with missing data when the missing mechanism is missing at random. However, MI may not be effective when the missing mechanism is not missing at random (NMAR). In such cases, additional information is required to obtain an appropriate imputation. Pham et al. (2019) proposed the calibrated-δ adjustment method, which is a multiple imputation method using population information. It provides appropriate imputation in two NMAR settings. However, the calibrated-δ adjustment method has two problems. First, it can be used only when one variable has missing values. Second, the theoretical properties of the variance estimator have not been provided. This article proposes a multiple imputation method using population information that can be applied when several variables have missing values. The proposed method is proven to include the calibrated-δ adjustment method. It is shown that the proposed method provides a consistent estimator for the parameter of the imputation model in an NMAR situation. The asymptotic variance of the estimator obtained by the proposed method and its estimator are also given.

Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions

BackgroundMissing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions (“missing completely at random”, “missing at random” [MAR], “missing not at random”) are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.MethodsWe searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.ResultsOf the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.ConclusionEffort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.

The effects of acupressure, laughter yoga, and a mindfulness-based stress reduction program applied to postmenopausal women for menopause symptoms and quality of life.

This research was conducted to determine the effects of acupressure, laughter yoga, and a mindfulness-based stress reduction program applied to postmenopausal women for menopause symptoms and quality of life. A randomized controlled design was used. The study was conducted with a total of 146 women, including 41 acupressure, 31 laughter yoga, 37 mindfulness-based stress reduction, and 37 control group women. The women in the acupressure group were administered acupressure twice a week, which accounted for 16 sessions in total. Women in the laughter yoga and mindfulness-based stress reduction groups received a total of eight sessions of the related intervention, which was performed once a week. Study data were collected using a participant information form, the Menopause Rating Scale (MRS), and the Menopause-Specific Quality of Life Scale (MENQOL). Descriptive statistics, paired-samples t test, χ 2 test, analysis of variance test, and intention to treat analysis were used to analyze the data. Before the intention to treat analysis was performed, the multiple imputation method was employed to deal with missing data. In the study, it was found that there was a decrease in the MRS total score after the intervention in women in all three intervention groups compared to the control group ( P < 0.05). In the laughter yoga group, total MRS scores decreased by 3.16 points ( P < 0.05). In the acupressure group, total MRS scores decreased by 5.46 points ( P < 0.05). In the mindfulness-based stress reduction (MBSR) program group, total MRS scores decreased by 4.65 points ( P < 0.05). It was determined that the mean scores of women in the laughter yoga and acupressure groups on all subscales of the MENQOL decreased after the intervention compared to the control group ( P < 0.05). The comparison of the MBSR group and the control group showed that there was a decrease only in the psychosocial domain subdimension of the MENQOL ( P < 0.05). In conclusion, it was determined that laughter yoga, acupressure, and MBSR could be effective in reducing menopause symptoms and improving quality of life, although findings require further testing in a rigorously controlled study and in a study with only a single prespecified outcome.

RNAseqCovarImpute: a multiple imputation procedure that outperforms complete case and single imputation differential expression analysis

Missing covariate data is a common problem that has not been addressed in observational studies of gene expression. Here, we present a multiple imputation method that accommodates high dimensional gene expression data by incorporating principal component analysis of the transcriptome into the multiple imputation prediction models to avoid bias. Simulation studies using three datasets show that this method outperforms complete case and single imputation analyses at uncovering true positive differentially expressed genes, limiting false discovery rates, and minimizing bias. This method is easily implemented via an R Bioconductor package, RNAseqCovarImpute that integrates with the limma-voom pipeline for differential expression analysis.

The performance of prognostic models depended on the choice of missing value imputation algorithm: a simulation study

ObjectiveThe development of clinical prediction models is often impeded by the occurrence of missing values in the predictors. Various methods for imputing missing values before modelling have been proposed. Some of them are based on variants of multiple imputation by chained equations, while others are based on single imputation. These methods may include elements of flexible modelling or machine learning algorithms, and for some of them user-friendly software packages are available. The aim of this study was to investigate by simulation if some of these methods consistently outperform others in performance measures of clinical prediction models. Study Design and SettingWe simulated development and validation cohorts by mimicking observed distributions of predictors and outcome variable of a real data set. In the development cohorts, missing predictor values were created in 36 scenarios defined by the missingness mechanism and proportion of non-complete cases. We applied three imputation algorithms that were available in R software: mice, aregImpute and missForest. These algorithms differed in their use of linear or flexible models, or random forests, the way of sampling from the predictive posterior distribution, and the generation of a single or multiple imputed data sets. For multiple imputation we also investigated the impact of the number of imputations. Logistic regression models were fitted with the simulated development cohorts before (full data analysis) and after missing value generation (complete case analysis), and with the imputed data. Prognostic model performance was measured by the scaled Brier score, c-statistic, calibration intercept and slope, and by the mean absolute prediction error evaluated in validation cohorts without missing values. Performance of full data analysis was considered as ideal. ResultsNone of the imputation methods achieved the model’s predictive accuracy that would be obtained in case of no missingness. In general, complete case analysis yielded the worst performance, and deviation from ideal performance increased with increasing percentage of missingness and decreasing sample size. Across all scenarios and performance measures, aregImpute and mice, both with 100 imputations, resulted in highest predictive accuracy. Surprisingly aregImpute outperformed full data analysis in achieving calibration slopes very close to 1 across all scenarios and outcome models. The increase of mice’s performance with 100 compared to 5 imputations was only marginal. The differences between the imputation methods decreased with increasing sample sizes and decreasing proportion of non-complete cases. ConclusionIn our simulation study, model calibration was more affected by the choice of the imputation method than model discrimination. While differences in model performance after using imputation methods were generally small, multiple imputation methods as mice and aregImpute that can handle linear or nonlinear associations between predictors and outcome are an attractive and reliable choice in most situations.

Diurnal variation of postoperative delirium in elderly patients undergoing esketamine anesthesia for elective noncardiac surgery: a randomized clinical trial.

Postoperative delirium (POD) is a serious and common complication. The aim of present study is to investigate the diurnal variation of POD and the effects of esketamine in elderly patients. A randomized, double-blind, placebo-controlled clinical trial with factorial design was conducted. Patients (aged 65 to 85 years) with normal Mini-Mental State Examination (MMSE) score were stratified by age (≤70 vs. >70) and American Society of Anesthesiologists physical status classification (Ⅱ vs. Ⅲ), then randomly assigned to either morning (08:00-12:00) or afternoon (14:00-18:00) noncardiac operation under general anesthesia with or without esketamine administration (0.2mg/kg). The primary outcome was the incidence of POD (3-Minute Diagnostic Interview for Confusion Assessment Method-defined Delirium, 3D-CAM) on postoperative days 1, 3, and 7. The secondary outcomes were the scores of MMSE and Hospital Anxiety and Depression Scale. The intention-to-treat analysis of the outcomes were performed by generalized estimating equation. Six patients who did not receive an intervention because of canceled operation were excluded after randomization. The datasets containing 426 cases were analyzed following the intention-to-treat principle after handling missing data via multiple imputation method. The incidence of POD declined from about 55% on postoperative day 1 to 31 and 18% on postoperative days 3 and 7, respectively. Afternoon operation [B=-0.583, OR (95% CI) 0.558 (0.319-0.976); P=0.041], but not esketamine, significantly decreased the incidence of POD. Both esketamine and operation time failed to significantly affect MMSE, HAD, and NRS score. There was no interaction among operation time, esketamine, and follow up time. Elderly patients undergoing elective noncardiac surgery in the afternoon displayed lower POD incidence than those operated in the morning. A single low-dose of esketamine before general anesthesia induction failed to significantly decrease the risk of POD but decrease the risk of intraoperative hypotension and emergence agitation.

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival

Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various analytical perspectives. Specifically, it focused on three distinct classes of performance metrics used to evaluate ML imputation methods: post-imputation bias of regression estimates, post-imputation predictive accuracy, and substantive model-free metrics. As an illustration, we applied data from a real-world breast cancer survival study. This comprehensive approach aimed to provide a thorough assessment of the effectiveness and reliability of ML imputation methods across various analytical perspectives. A simulated dataset with 30% Missing At Random (MAR) values was used. A number of single imputation (SI) methods - specifically KNN, missMDA, CART, missForest, missRanger, missCforest - and multiple imputation (MI) methods - specifically miceCART and miceRF - were evaluated. The performance metrics used were Gower’s distance, estimation bias, empirical standard error, coverage rate, length of confidence interval, predictive accuracy, proportion of falsely classified (PFC), normalized root mean squared error (NRMSE), AUC, and C-index scores. The analysis revealed that in terms of Gower’s distance, CART and missForest were the most accurate, while missMDA and CART excelled for binary covariates; missForest and miceCART were superior for continuous covariates. When assessing bias and accuracy in regression estimates, miceCART and miceRF exhibited the least bias. Overall, the various imputation methods demonstrated greater efficiency than complete-case analysis (CCA), with MICE methods providing optimal confidence interval coverage. In terms of predictive accuracy for Cox models, missMDA and missForest had superior AUC and C-index scores. Despite offering better predictive accuracy, the study found that SI methods introduced more bias into the regression coefficients compared to MI methods. This study underlines the importance of selecting appropriate imputation methods based on study goals and data types in time-to-event research. The varying effectiveness of methods across the different performance metrics studied highlights the value of using advanced machine learning algorithms within a multiple imputation framework to enhance research integrity and the robustness of findings.

An investigation into the effect of different missing data imputation methods on IRT-based differential item functioning

The purpose of this study is to examine the effect of missing data imputation methods, namely regression imputation (RI), multiple imputation (MI) and k-nearest neighbor (kNN) on differential item functioning (DIF). In this regard, the datasets used in the research were created by deleting some of the data via the missing completely at random mechanism from the complete datasets obtained from 600 students in Türkiye, the United Kingdom, the USA, New Zealand and Australia, who answered booklets 14 and 15 from the PISA 2018 science literacy test. Data imputation was applied to the datasets through missing data using RI, MI and kNN methods and DIF analysis was performed on all datasets in terms of language and gender variables via Lord’s χ2 method, Raju’s area measurement method and item response theory likelihood ratio method. DIF results from the complete datasets were taken as a reference and they were compared with the results from other datasets. As a result of the research, values close to 10% of accurate imputation were achieved in the RI method depending on language and gen-der variables. In MI and kNN methods, results closest to the complete datasets were obtained at a rate of 5% depending on the language variable. In the MI method, inaccurate results were obtained in all proportions in terms of the gender variable. For the gender variable, the kNN method gave accurate results at rates of 5% and 10%.

Probiotic Supplements For Depression In Parkinson’s Disease (Pro-Park): A Randomized, Triple-Blind, Placebo-Controlled, Phase IIb Trial Protocol

Introduction: Parkinson’s disease (PD) is the second most prevalent degenerative neurological disorder globally. Comorbid major depressive disorder (MDD) is a common occurrence which adds considerably to disease burden. This protocol presents a study designed to investigate the effect of add-on probiotic supplementation on MDD in patients with PD. Methods: We designed a phase IIb, randomized, triple-blind, placebo-controlled study to evaluate the efficacy and safety of probiotics as an adjuvant treatment to selective serotonin reuptake inhibitors (SSRI) for patients with mild to moderate PD and MDD. Participants will be randomized to probiotics (n=62) or placebo (n=62) arms using a permuted block randomization approach. Co-primary outcomes include changes in Montgomery-Åsberg Depression Rating Scale (MADRS)scores and SSRI equivalent doses, which will be compared between groups using a bivariate joint model. Secondary outcomes will include changes in both Beck Depression Inventory-II (BDI-II) score and levodopa dose, which will be analyzed using t-tests or Mann-Whitney tests as appropriate. Concurrent validity between MADRS and BDI-II will be assessed using Pearson’s or Spearman’s correlation tests. Safety will be evaluated by comparing discontinuation rates and adverse events between groups using chi-square. Data will be analyzed using intention-to-treat and per-protocol analyses, and missing data will be addressed using multiple imputation methods. Discussion: Probiotics represent a potential new approach to managing depression in patients with PD by targeting the gut-brain axis. The results will offer crucial information about the safety and efficacy of a low-cost and readily available supplement that may alleviate depression in this vulnerable population.

Benchmarking imputation methods for categorical biological data

Abstract Trait datasets are at the basis of a large share of ecology and evolutionary research, being used to infer ancestral morphologies, quantify species extinction risks, or evaluate the functional diversity of biological communities. These datasets, however, are often plagued by missing data, for instance, due to incomplete sampling, limited data and resource availability. Several imputation methods exist to predict missing values and recent studies have explored their performance for continuous traits in biological datasets. However, less is known about the accuracy of these methods for categorical traits. Here we explore the performance of different imputation methods on categorical biological traits combining phylogenetic comparative methods, machine learning and deep learning models. To this end, we develop an open‐source R package, to impute trait data while integrating a simulation framework to evaluate their performance on synthetic datasets. We run a range of simulations under different missing rates, mechanisms, biases and evolutionary models. We propose an integration between phylogenetic comparative methods and machine learning imputation, and an ensemble approach, in which selected imputation methods are combined. Our simulations show that this approach provides the most robust and accurate predictions. We applied our imputation pipeline to an incomplete trait dataset of 1015 elasmobranch species (i.e. sharks, rays and skates) and found a high imputation accuracy of the predictions based on an expert‐based assessment of the missing traits. Overall, our R package facilitates the comparison of multiple imputation methods and allows robust predictions of missing trait values. Our study highlights the benefits of coupling phylogenetic evolutionary models with machine learning inference to augment incomplete biological datasets.

Should regression calibration or multiple imputation be used when calibrating different devices in a longitudinal study?

In longitudinal studies, the devices used to measure exposures can change from visit to visit. Calibration studies, wherein a subset of participants is measured using both devices at follow-up, may be used to assess between-device differences (i.e., errors). Then, statistical methods are needed to adjust for between-device differences and the missing measurement data that often appear in calibration studies. Regression calibration and multiple imputation are two possible methods. We compared both methods in linear regression with a simulation study, considering various real-world scenarios for a longitudinal study of pulse wave velocity. Regression calibration and multiple imputation were both essentially unbiased, but correctly estimating the standard errors posed challenges. Multiple imputation with predicted mean matching produced close agreement with the empirical standard error. Fully stochastic multiple imputation underestimated the standard error by up to 50%, and regression calibration with bootstrapped standard errors performed slightly better than fully stochastic multiple imputation. Regression calibration was slightly more efficient than either multiple imputation method. The results suggest use of multiple imputation with predictive mean matching over fully stochastic imputation or regression calibration in longitudinal studies where a new device at follow-up might be error-prone compared to the device used at baseline.

Order‐restricted hypothesis tests for nonlinear mixed‐effects models with measurement errors in covariates

AbstractOrder‐restricted hypothesis testing problems frequently arise in practice, including studies involving regression models for longitudinal data. These tests are known to be more powerful than tests that ignore such restrictions. In this article, we consider order‐restricted tests for nonlinear mixed‐effects models with measurement errors in time‐dependent covariates. We propose to use a multiple imputation method to address measurement errors, since this approach allows us to use existing complete‐data methods for order‐restricted tests. Some theoretical results are presented. We evaluate our proposed methods via simulation studies that demonstrate they are more powerful than either a competing naive method or a two‐step approach to testing hypotheses. We illustrate the use of our proposed approach by analyzing data from an HIV/AIDS study.

The Relationship of Waist Circumference with the Morbidity of Cardiovascular Diseases and All-Cause Mortality in Metabolically Healthy Individuals: A Population-Based Cohort Study.

This study explores the relationship between waist circumference and morbidity of cardiovascular diseases (CVD) and all-cause mortality in metabolically healthy individuals. A cohort of 5775 metabolically healthy participants from the 2001-2014 US National Health and Nutrition Examination Survey and National Death Index database was tracked over a median period of 81 months. These participants were divided into quartiles (Q1, Q2, Q3, Q4) based on increasing waist circumference. To compensate for missing covariates, multivariate multiple imputation methods were used. Adjusted logistic regression models were employed to examine the correlation between waist circumference and cardiovascular disease prevalence. Furthermore, Kaplan-Meier curves and multivariable Cox regression analysis were utilized to evaluate the association between waist circumference and all-cause mortality, both qualitatively and quantitatively. The adjusted logistic regression model indicated that a 10 cm increase in waist circumference was associated with a 1.45 times higher prevalence of CVD. As a categorical variable, there was a significant upward trend in CVD incidence across quartiles of waist circumference. The adjusted odds ratios (95% confidence intervals) were 2.41 (1.13-5.53) for Q2, 2.65 (1.18-6.39) for Q3, and 2.53 (0.9-7.44) for Q4, compared to Q1. Notably, individuals with high waist circumference showed significantly poorer survival compared to those with low waist circumference (p = 0.008). The Cox regression analysis revealed that each 10 cm increase in waist circumference contributed to an ~8% increase in all-cause mortality. This study underscores a positive correlation between waist circumference and both CVD morbidity and all-cause mortality in metabolically healthy individuals. The findings highlight the significance of routinely monitoring waist circumference for effective CVD risk management, regardless of metabolic health status.

#1874 Comparative analysis of rehabilitation outcomes in hospitalized hemodialysis vs. non-hemodialysis patients: a retrospective cohort study

Abstract Background and Aims The hospitalization rate of patients undergoing hemodialysis (HD) is high, due to complications related to renal failure and hemodialysis treatment. Therefore, it is crucial to provide rehabilitation for hospitalized HD patients to improve their activities of daily living (ADL) and physical functions, so as to enable them to be discharged home, resume outpatient dialysis, and avoid re-hospitalization. However, the effectiveness of rehabilitation to improve the ADL and physical functions may differ between hospitalized patients undergoing HD (HD patients) and hospitalized patients not undergoing HD (non-HD patients), depending on their pathological conditions and influence of the HD treatment. The aim of the present study was to compare the outcomes of rehabilitation between hospitalized HD patients and non-HD patients. Method This was a retrospective cohort study. The inclusion criteria were hospitalized patients who received rehabilitation between April 2017 and February 2023. The exclusion criteria were patients who discontinued rehabilitation, were transferred to other hospitals, or died during the rehabilitation period. Rehabilitation consisted of physical therapy and/or occupational therapy provided six times a week, for both hospitalized HD and non-HD patients. The rehabilitation program comprised balance training, aerobic exercises, resistance training, and ADL training to prepare the patients for discharge to their homes. We measured several outcomes in the study, including the daily duration of rehabilitation (min/day), length of hospital stay (days), Barthel index (BI), as the ADL index, at the time of admission and discharge, grip strength and isometric knee extension strength (IKES) as muscle strength indices, and the 10-meter walking speed, timed up and go test (TUG), and short physical performance battery (SPPB) as mobility function indices. For the statistical analysis, missing values were completed by using multiple imputation methods. The inverse probability weighting (IPTW) method was used to match the HD group and non-HD group to adjust for confounding factors. The outcomes between the groups were then compared using a generalized linear model analysis. All procedures in the present study were conducted with the approval of the ethics committee of Seirei Christopher University. Results Of 1002 patients who were registered for the study, 100 patients who discontinued rehabilitation, were transferred to other hospitals, or died were excluded, and the data of the remaining 902 patients (765 of the non-HD group and 137 of the HD group) were included in the analysis in this study. The results of the generalized linear model analysis revealed a lower daily duration of rehabilitation [43.3(0.6) vs. 38.8(1.2) min/day, p=0.0003] and longer length of hospital stay [48.5(0.5) vs. 58.1(2.3) days, p=0.0108] in the HD group. In addition, the 10-meter walking speed [0.75(0.02) vs. 0.66(0.03) m/s, p=0.003] and scores on the TUG [20.8(0.7) vs. 24.3(1.0) sec, p=0.0021] and SPPB [6.3(4.57) vs. 4.7(0.6) points, p=0.0415] at discharge were also significantly lower in the HD group as compared with the non-HD group. There was no difference in the ability for ADL or muscle strength at discharge between the two groups. Conclusion This study was the first to demonstrate a lower daily duration of rehabilitation and longer hospital stay in hospitalized HD patients. Furthermore, rehabilitation had a lower impact on the mobility indices in HD patients as compared with non-HD patients. Although there was no difference in the ability for ADL or muscle strength at discharge between the two groups, HD patients exhibited a significantly lower ability for mobility functions, including a lower gait speed and lower scores on the TUG, and SPPB assessments. Therefore, rehabilitation efforts in HD patients need to be improved by securing more time for inpatient rehabilitation and promoting mobility function improvement.

Advanced machine learning for missing petrophysical property imputation applied to improve the characterization of carbonate reservoirs

Missing data is a common cause of uncertainty in reservoir characterization, especially in core analysis of porosity and permeability. This is due to the high cost and time required to gather core samples while drilling through the entire reservoir thickness. Therefore, the ability to collect core samples and directly assess petrophysical parameters is restricted by the small proportion of the reservoir thickness that can be measured by the retrieved cores. As a result, the absence of data has the potential to lead to inconsistent and imprecise classification of reservoirs and geomodeling, thereby increasing reservoir uncertainty. Therefore, imputation algorithms are needed to estimate petrophysical properties for incomplete intervals. This study comprehensively compares seven imputation techniques for predicting missing horizontal and vertical core permeability and core porosity data in a well drilled through a carbonate reservoir in a southern Iraqi gas field. The performance of each method was assessed using relative bias (RB) and reasonable descriptive statistical distributions (histogram, log view, and box plots of data ratios to compare the imputed and original data), and robustness to outliers (RtO). The results revealed that the random imputation of missing data (RIMD) technique is the most effective algorithm in addressing the 30.5% missing data (vertical permeability), resulting in the attainment of the RB value closest to zero (0.04076191) compared to other algorithms. Principal component analysis (PCA) and random forest (RF) yielded the most favorable results in terms of handling missing values and outliers for the 15% missing ratio dataset (core porosity and horizontal permeability). The advantages of these approaches are related to their capacity to independently and accurately impute the missing data without requiring extra information, such as well logging records. Additionally, the multiple imputation and machine learning algorithms fill missing values with adequate numbers based on dataset variable distributions and correlations. Multiple plausible values quantify the uncertainty in estimating missing data, avoiding false precision (as with single imputation). Also, the novel workflow involving multiple imputation methods accurately take into account the correlations and variances between the variables. The novel workflow developed for missing data imputation has the potential to be applied to other clastic and carbonate reservoirs in oil and gas fields. The entire data imputation workflow was conducted, validated, visualized by codes in R, a robust open-source statistical computing software.

ESTIMATING THE COSTS, HEALTH-RELATED QUALITY OF LIFE, AND REVISION RISK IN THE YEAR AFTER ELECTIVE PRIMARY AND REVISION HIP ARTHROPLASTY PROCEDURES BY IMPLANT BEARING MATERIALS: AN ANALYSIS OF LINKED DATA FROM THE NATIONAL JOINT REGISTRY (NJR), HOSPITAL EPISODE STATISTICS (HES), AND PATIENT-REPORTED OUTCOME MEASURES (PROMS) FOR ENGLISH PATIENTS

Implants in total hip replacement (THR) are associated with different clinical and cost-effectiveness profiles,. We estimate the costs and outcomes for NHS patients in the year after THR associated with implant bearing materials using linked routinely collected data.We linked NJR primary elective THR patients for osteoarthritis to HES and National PROMs. We estimated health care costs, health-related quality of life indices, and revision risks, in the year after primary and revision THRs overall. We used generalised linear models adjusting for patient and hospital characteristics and estimated 10-year cumulative probability of revision. We imputed utilities using chained equations for half the sample with missing PROMS.We linked 577,973 elective primary THRs and 11,812 subsequent revisions. One year after primary THR, patients with the cemented THRs using cobalt chrome or stainless steel head with HCLPE liner/cup cost the NHS, on average, £13,101 (95%CI £13,080,£13,122), had an average quality-of-life score of 0.788 (95%CI 0.787,0.788), and a 10-year revision probability of 1.9% (95%CI 1.6,2.3). Compared to the reference, patients receiving a cemented THR with delta ceramic head and HCLPE liner/cup, hybrid THR with delta ceramic head and HCLPE liner/cup, and hybrid THR with alumina head and HCLPE liner/cup had lower 1-year costs (-£572 \[95% CI -£775,-£385\], -£346 \[-£501,-£192\], -£371 \[-£574,-£168\] respectively), better quality of life (0.007 \[95% CI 0.003,0.011\], 0.013 \[0.010,0.016\], 0.009 \[0.005,0.013\] respectively), and lower 10-year revision probabilities (1.4% \[1.03,2.0\], 1.5 \[1.3,1.7\], 1.6%\[1.2,2.1\] respectively).Implant bearing materials are associated with varying mean costs and health outcomes after primary THR. Ours is the first study to derive costs and health outcomes from large, linked databases using multiple imputation methods to deal with bias. Our findings are useful for commissioning and procurement decisions and to inform a subsequent cost-effectiveness model with more granular detail on THR implant types.

Special issue PRO] A demonstration of estimands and sensitivity analyses for time-to-deterioration of patient reported outcomes

ABSTRACT In oncology trials, health-related quality of life (HRQoL), specifically patient-reported symptom burden and functional status, can support the interpretation of survival endpoints, such as progression-free survival. However, applying time-to-event endpoints to patient-reported outcomes (PRO) data is challenging. For example, in time-to-deterioration analyses clinical events such as disease progression are common in many settings and are often handled through censoring the patient at the time of occurrence; however, disease progression and HRQoL are often related leading to informative censoring. Special consideration to the definition of events and intercurrent events (ICEs) is necessary. In this work, we demonstrate time-to-deterioration of PRO estimands and sensitivity analyses to answer research questions using composite, hypothetical, and treatment policy strategies applied to a single endpoint of disease-related symptoms. Multiple imputation methods under both the missing-at-random and missing-not-at-random assumptions are used as sensitivity analyses of primary estimands. Hazard ratios ranged from 0.52 to 0.66 over all the estimands and sensitivity analyses modeling a robust treatment effect favoring the treatment in time to disease symptom deterioration or death. Differences in the estimands include how people who experience disease progression or discontinue the randomized treatment due to AEs are accounted for in the analysis. We use the estimand framework to define interpretable and principled approaches for different time-to-deterioration research questions and provide practical recommendations. Reporting the proportions of patient events and patient censoring by reason helps understand the mechanisms that drive the results, allowing for optimal interpretation.