Multiple Imputation Methods Research Articles

Comparison of methods for handling outliers in Cox regression model

The Cox regression analysis is used to determine the relationship between a dependent variable and covariates in survival analysis involving censored data. The proportional hazards assumption is one of the most important assumptions of Cox regression. Outliers may have a strong influence on the Cox regression model’s parameter estimates and lead to violation of the proportional hazard assumption. Therefore, having outliers in the data set is a problem for researchers. In this case, robust estimations are commonly used to infer the parameters in a more robust way. However, we explore a new approach consisting of considering an outlier as missing data and replacing it by the multiple imputation method. The aim of this study is to compare these two methods through simulation. Furthermore, an analysis of a lung cancer data set is considered for illustration. According to the results of the study carried out based on simulated data sets and a real data set, the multiple imputation method, which is a missing data analysis method, solves the problem of outliers better than the robust estimation method, as the outcome is closer to the results obtained through original data.

Data variability in the imputation quality of missing data

Imputation methods were developed to define estimates for missing data and hence solve possible problems generated by the loss of this information. This study aims to assess whether data variability influences the results obtained after applying an imputation method. Incomplete databases were generated from complete real databases of experiments of tomato plants conducted using the randomized block design with three replications and 12 treatments by removing different amounts of data. The evaluated variables consisted of fruit weight per plant, number of fruits per plant, and average fruit length and width, forming eight balanced databases. Subsequently, the distribution-free multiple imputation method was applied, generating complete databases from imputation. The number of missing information influenced the accuracy measures for the data in this study. Data imputation was inadequate when there was high variability but more precise and accurate in cases of low variability. It confirmed the importance of assessing data variability before choosing to apply the imputation method.

Strategies for Imputing Missing Values and Removing Outliers in the Dataset for Machine Learning-Based Construction Cost Prediction

Accurately predicting construction costs during the initial planning stages is crucial for the successful completion of construction projects. Recent advancements have introduced various machine learning-based methods to enhance cost estimation precision. However, the accumulation of authentic construction cost data is not straightforward, and existing datasets frequently exhibit a notable presence of missing values, posing challenges to precise cost predictions. This study aims to analyze diverse substitution methods for addressing missing values in construction cost data. Additionally, it seeks to evaluate the performance of machine learning models in cost prediction through the removal of conditional outliers. The primary goal is to identify and propose optimal strategies for handling missing value in construction cost records, ultimately improving the reliability of cost predictions. According to the analysis results, among single imputation methods, median imputation emerges as the most suitable, while among multiple imputation methods, lasso regression imputation produces the most superior outcomes. This research contributes to enhancing the trustworthiness of construction cost predictions by presenting a pragmatic approach to managing missing data in construction cost performance records, thereby facilitating more precise project planning and execution.

On the search for efficient face recognition algorithm subject to multiple environmental constraints

From literature, majority of face recognition modules suffer performance challenges when presented with test images acquired under multiple constrained environments (occlusion and varying expressions). The performance of these models further deteriorates as the degree of degradation of the test images increases (relatively higher occlusion level). Deep learning-based face recognition models have attracted much attention in the research community as they are purported to outperform the classical PCA-based methods. Unfortunately their application to real-life problems is limited because of their intensive computational complexity and relatively longer run-times. This study proposes an enhancement of some PCA-based methods (with relatively lower computational complexity and run-time) to overcome the challenges posed to the recognition module in the presence of multiple constraints. The study compared the performance of enhanced classical PCA-based method (HE-GC-DWT-PCA/SVD) to FaceNet algorithm (deep learning method) using expression variant face images artificially occluded at 30% and 40%. The study leveraged on two statistical imputation methods of MissForest and Multiple Imputation by Chained Equations (MICE) for occlusion recovery. From the numerical evaluation results, although the two models achieved the same recognition rate (85.19%) at 30% level of occlusion, the enhanced PCA-based algorithm (HE-GC-DWT-PCA/SVD) outperformed the FaceNet model at 40% occlusion rate, with a recognition rate of 83.33%. Although both Missforest and MICE performed creditably well as de-occlusion mechanisms at higher levels of occlusion, MissForest outperforms the MICE imputation mechanism. MissForest imputation mechanism and the proposed HE-GC-DWT-PCA/SVD algorithm are recommended for occlusion recovery and recognition of multiple constrained test images respectively.

Effect of Spesolimab on Achieving Sustained Disease Remission in Patients with Generalized Pustular Psoriasis: Results from the Effisayil 2 Study

Introduction: Generalized pustular psoriasis (GPP) is a chronic, rare disease characterized by flares of widespread skin pustulation. Intravenous (IV) spesolimab, an anti-interleukin-36 receptor monoclonal antibody, is approved for GPP flare treatment; however, the optimal dosing and long-term efficacy of subcutaneous (SC) spesolimab treatment in patients with GPP has not yet been reported. We report the effect of SC spesolimab on achieving sustained disease remission in Effisayil 2 (NCT04399837), a 48-week trial that evaluated the efficacy and safety of SC spesolimab in preventing GPP flares.
 Methods: Patients with a history of GPP were randomized (1:1:1:1) to receive placebo (n=31), low- (n=31, 300 mg loading dose [LD]; 150 mg every 12 weeks [q12w]), medium- (n=31, 600 mg LD; 300 mg q12w) or high-dose (n=30, 600 mg LD; 300 mg every 4 weeks [q4w]) SC spesolimab over 48 weeks. Sustained remission was defined as GPP Physician Global Assessment (GPPGA) total score of 0 or 1 at all visits up to Week 48, or GPPGA total score of 0 or 1 and all GPPGA subscores ≤2 at all visits up to Week 48. Sustained pustular clearance was defined as GPPGA pustulation subscore of 0 at all visits from Week 4 to Week 48. Any use of IV spesolimab or another standard of care for GPP worsening was considered a failure of remission. Missing data were imputed by a sequential logistic regression multiple imputation method. Adjusted risk differences (RD) were calculated by the Mantel−Haenszel type-weighted average of differences.
 Results: 31 patients received placebo, and 30 patients received high-dose spesolimab. Compared to placebo, more patients had sustained remission in the high-dose arm using both GPPGA total score 0 or 1 (63.3% vs 29.0%; RD [95% CI] 0.35 [0.10–0.59]), and GPPGA total score 0 or 1 and all GPPGA subscores ≤2 (60.4% vs 29.0%; RD [95% CI] 0.32 [0.07–0.56]). Compared to placebo, more patients in the high-dose arm had sustained pustular clearance over 48 weeks (63.6% vs 25.8%; RD [95% CI] 0.38 [0.14–0.62]).
 Conclusion: Overall, high-dose SC spesolimab q4w is effective for the long-term management of GPP skin symptoms.

Survival analysis under imperfect record linkage using historic census data

BackgroundAdvancements in linking publicly available census records with vital and administrative records have enabled novel investigations in epidemiology and social history. However, in the absence of unique identifiers, the linkage of the records may be uncertain or only be successful for a subset of the census cohort, resulting in missing data. For survival analysis, differential ascertainment of event times can impact inference on risk associations and median survival.MethodsWe modify some existing approaches that are commonly used to handle missing survival times to accommodate this imperfect linkage situation including complete case analysis, censoring, weighting, and several multiple imputation methods. We then conduct simulation studies to compare the performance of the proposed approaches in estimating the associations of a risk factor or exposure in terms of hazard ratio (HR) and median survival times in the presence of missing survival times. The effects of different missing data mechanisms and exposure-survival associations on their performance are also explored. The approaches are applied to a historic cohort of residents in Ambler, PA, established using the 1930 US census, from which only 2,440 out of 4,514 individuals (54%) had death records retrievable from publicly available data sources and death certificates. Using this cohort, we examine the effects of occupational and paraoccupational asbestos exposure on survival and disparities in mortality by race and gender.ResultsWe show that imputation based on conditional survival results in less bias and greater efficiency relative to a complete case analysis when estimating log-hazard ratios and median survival times. When the approaches are applied to the Ambler cohort, we find a significant association between occupational exposure and mortality, particularly among black individuals and males, but not between paraoccupational exposure and mortality.DiscussionThis investigation illustrates the strengths and weaknesses of different imputation methods for missing survival times due to imperfect linkage of the administrative or registry data. The performance of the methods may depend on the missingness process as well as the parameter being estimated and models of interest, and such factors should be considered when choosing the methods to address the missing event times.

Predicting absolute risk for a person with missing risk factors.

We compared methods to project absolute risk, the probability of experiencing the outcome of interest in a given projection interval accommodating competing risks, for a person from the target population with missing predictors. Without missing data, a perfectly calibrated model gives unbiased absolute risk estimates in a new target population, even if the predictor distribution differs from the training data. However, if predictors are missing in target population members, a reference dataset with complete data is needed to impute them and to estimate absolute risk, conditional only on the observed predictors. If the predictor distributions of the reference data and the target population differ, this approach yields biased estimates. We compared the bias and mean squared error of absolute risk predictions for seven methods that assume predictors are missing at random (MAR). Some methods imputed individual missing predictors, others imputed linear predictor combinations (risk scores). Simulations were based on real breast cancer predictor distributions and outcome data. We also analyzed a real breast cancer dataset. The largest bias for all methods resulted from different predictor distributions of the reference and target populations. No method was unbiased in this situation. Surprisingly, violating the MAR assumption did not induce severe biases. Most multiple imputation methods performed similarly and were less biased (but more variable) than a method that used a single expected risk score. Our work shows the importance of selecting predictor reference datasets similar to the target population to reduce bias of absolute risk predictions with missing risk factors.

Multi-omics Integrative Analysis for Incomplete Data Using Weighted p-Value Adjustment Approaches

AbstractThe advancements in high-throughput technologies provide exciting opportunities to obtain multi-omics data from the same individuals in a biomedical study, and joint analyses of data from multiple sources offer many benefits. However, the occurrence of missing values is an inevitable issue in multi-omics data because measurements such as mRNA gene expression levels often require invasive tissue sampling from patients. Common approaches for addressing missing measurements include analyses based on observations with complete data or multiple imputation methods. In this paper, we propose a novel integrative multi-omics analytical framework based on p-value weight adjustment in order to incorporate observations with incomplete data into the analysis. By splitting the data into a complete set with full information and an incomplete set with missing measurements, we introduce mechanisms to derive weights and weight-adjusted p-values from the two sets. Through simulation analyses, we demonstrate that the proposed framework achieves considerable statistical power gains compared to a complete case analysis or multiple imputation approaches. We illustrate the implementation of our proposed framework in a study of preterm infant birth weights by a joint analysis of DNA methylation, mRNA, and the phenotypic outcome. Supplementary materials accompanying this paper appear online.

551 - Continuous tralokinumab treatment over 4 years in adults with moderate-to-severe atopic dermatitis provides long-term disease control

Abstract Introduction/Background There is a need for additional long-term treatment options for patients with moderate-to-severe atopic dermatitis (AD) that provide sustained disease control with a favorable safety profile. Tralokinumab, a monoclonal antibody that specifically neutralizes interleukin-13, is approved for the treatment of moderate-to-severe AD in multiple countries. Clinical trials of up to 52-wk duration showed that tralokinumab was effective and well tolerated as monotherapy and in combination with topical therapy. ECZTEND (NCT03587805) is an ongoing open-label, 5-yr extension trial investigating the long-term safety and efficacy of tralokinumab ± optional topical corticosteroids (TCS). Objectives To assess the efficacy of long-term tralokinumab treatment, we conducted a post hoc interim subgroup analysis restricted to the largest, most homogenous patient population, with the longest treatment duration. Methods Adult patients with moderate-to-severe AD who were continuously treated with tralokinumab ± optional TCS for 52 wks in the parent phase 3 trials ECZTRA 1 (NCT03131648) or ECZTRA 2 (NCT03160885) and for up to 152 wks in ECZTEND as of data cutoff April 30, 2022, were included. Endpoints included proportion of patients achieving Investigator’s Global Assessment (IGA) score of 0/1 (clear/almost clear skin), at least 75% or 90% improvement in Eczema Area and Severity Index (EASI) relative to parent trial baseline (EASI-75 or EASI-90), EASI ≤7, worst weekly pruritus Numeric Rating Scale (NRS) ≤4, and Dermatology Life Quality Index (DLQI) ≤5. Results are presented using observed data. Sensitivity analyses on patients who completed Wk 152 in ECZTEND (or who withdrew, but were enrolled in the study at least 152 wks prior to the study cutoff) were performed using modified non-responder imputation (mNRI) with discontinuations due to AEs or lack of efficacy imputed as non-responders and a 2-step multiple imputation (MI) for other missing data [Step 1: Missing data up to 1 year in ECZTEND were imputed using a standard regression-based MI method; Step 2: Missing data post 1 year imputed by carrying forward the within-subject area under the curve of days in-response]. Results 347 adult patients with a mean age (SD) of 42.2 (14.5) yrs and a mean EASI (SD) of 30.8 (13.7) at parent trial baseline were included in this analysis. After 4 yrs of total tralokinumab treatment (at Wk 152 in ECZTEND), IGA 0/1 [% (n/N)] was observed in 52.6% (92/175) of patients. EASI-75 {% (n/N) [95% CI]} was achieved in 84.5% (147/174) [78.4, 89.1] of patients as observed, and in 73.4% (254.8/347) [68.6, 78.3] using mNRI. EASI-90 was achieved in 64.4% (112/174) [57.0, 71.1] of patients as observed, and in 53.4% (185.2/347) [47.8, 59.0] using mNRI. Additionally, EASI ≤7 (mild disease) was observed in 84.5% (147/174) of patients, worst weekly pruritus NRS ≤4 (no to mild itch) in 68.0% (119/175), and DLQI ≤5 (no to small effect of AD on quality of life) in 79.0% (128/162) of patients. The safety profile was favorable and consistent with earlier analyses, with no new safety signals arising with continued tralokinumab use. Conclusions Continuous use of tralokinumab ± optional TCS provided long-term disease control over 4 yrs in adult patients with moderate-to-severe AD.

Periconceptional maternal homocysteine and birth weight outcomes in offspring: a prospective cohort study

Objective: To quantify the associations between periconceptional maternal homocysteine (HCY) and offspring's birth weight and risk of small for gestational age (SGA) infant. Methods: The 19 984 mother-child pairs in this prospective cohort study were recruited from the Shanghai preconception cohort; the infants were delivered from 1st September 2016 to 11th November 2022. A standardized questionnaire was used to collect the mothers' demographic information, medical history, dietary supplement use, and maternal complications during pregnancy, and their serum samples were collected. Serum HCY, folate, and vitamin B12 were measured using chemiluminescent microparticle immunoassay based on serum sample drawn at enrollment. Birth weight data were obtained from medical records. Multiple imputation methods were applied to handle missing data in key variables. Multivariable linear regression and Poisson regression models were used to analyze the relationship between maternal HCY concentration during the periconceptional period and the birth weight and SGA risk of the offspring. Results: A total of 9 452 pairs were enrolled preconceptionally and the remaining 10 532 pairs were enrolled in early pregnancy. The proportion of mothers whose pregnancy age was greater than 35 years was 9.2% (1 832/19 984), the proportion of primiparous women was 76.5% (15 283/19 984), the proportion of pre-pregnancy overweight and obesity was 14.0% (2 804/19 984), the proportion of using folic acid supplements before pregnancy was 21.4% (4 272/19 984), and the proportion of those who supplemented with folic acid during early pregnancy was 85.2% (8 976/10 532); gestational diabetes mellitus was in 6.2% (1 245/19 984), gestational hypertensive syndrome in 3.6% (711/19 984). The birth weight of the offspring was (3 297±468) g, and there were 1 962 SGA children (9.8%). The HCY concentration in the overall population in appropriate for gestational age during the periconceptional period was (7.9±3.2) μmol/L, with (8.3±3.7) μmol/L in the preconception subgroup and (7.3±2.4) μmol/L in the early pregnancy subgroup. After adjustment for the covariates of perinatal demographic information, adverse pregnancy outcomes, serum folate and vitamin B12, increased maternal periconceptional HCY was significantly associated with lower offspring birth weight (β=-2.30, 95%CI -4.43--0.16, P=0.035). Only the early pregnancy subgroup was significantly associated with lower offspring birth weight (β=-7.39, 95%CI-11.50--3.21, P<0.001). No association was found between peripregnancy HCY and offspring SGA risk. However, elevated HCY in early pregnancy was associated with an increased risk of SGA in the offspring (RR=1.05, 95%CI 1.01-1.08, P=0.002). Periconceptional vitamin B12 was a mediator of the association between HCY and offspring birth weight, accounting for 16.5%, 41.2% and 5.4% of its total effect in the overall periconceptional population, the pre-pregnancy subgroup and the early pregnancy subgroup, respectively. Conclusions: Maternal periconceptional HCY level is associated with lower birth weight in offspring, but not with the risk of SGA. Elevated maternal HCY in early pregnancy subgroup may be associated with increased risk of SGA in offspring.

An ARCAD database nomogram for prognostic prediction of treatment refractory metastatic colorectal cancer.

107 Background: The "No Placebo Initiative" demonstrated that the survivals of patients (pts) receiving placebo in a salvage-line setting, exhibited no significant difference across trials (CORRECT, RECOURSE, CONCUR and TERRA). Therefore, we believe that a nomogram can be generated using the ARCAD database to predict survival of pts who completed standard therapy. Methods: Of 40,889 Individual patient data (IPD) from 59 studies, 723 pts received placebo from 4 randomized studies were pooled. The potential prognostic factors considered include BMI and Age. Imputed datasets were constructed by a multiple imputation method since the original dataset contains some missing values. Overall survival (OS) was defined as the time from randomization to death from any cause. Multivariate Cox proportional hazards models for OS were estimated based on the variables showing clinically and statistically significant by univariate analysis. Prediction performance of the final model was evaluated using the concordance index for survival data and calibration plots. Results: From univariate and multivariate analysis, ECOG performance status, Royal Marsden Hospital score (calculated by albumin level, LDH level, and number of metastatic site), primary site location, liver meta, peritoneal meta were selected as variables. We successfully developed a highly accurate nomogram to predict post-standard treatment prognosis for pts with mCRC. Concordance index for OS was 0.66. Conclusions: Although further validation in other cohorts is needed, this nomogram could be very useful to estimate the survival for clinical practice and enrolling in clinical trials. [Table: see text]

Spatial classification in the presence of measurement error

In recent decades, spatial classification has received considerable attention in a wide array of disciplines. In practice, binary response variable is often subject to measurement error, misclassification. To account for the misclassified response in spatial classification, we proposed validation data-based adjustment methods that use interval validation data to rectify misclassified responses. Regression calibration and multiple imputation methods are utilized to correct the misclassified outcomes at the locations where the gold-standard device is not available. Generalized linear mixed model and indicator Kriging are applied for spatial classification at unsampled locations. Simulation studies are performed to compare the proposed methods with naive methods that ignore the misclassification. It was found that the proposed models significantly improve prediction accuracy. Additionally, the proposed models are applied for precipitation detection in South Korea.

SF-6D Normative Values Among Patients Undergoing Bariatric Surgery: Results Based on Real-World Evidence from the Scandinavian Obesity Surgery Registry (SOReg)

BackgroundThe SF-6D index can be used to calculate quality-adjusted life years in economic evaluations, which is required by reimbursement agencies and national advisory bodies, including the Swedish ones. However, despite that SF-36 has been largely applied among patients undergoing bariatric surgery, almost no study has accessed the short form six-dimensions (SF-6D) after bariatric surgery.AimTo establish normative values for the SF-6D index among patients undergoing bariatric surgery.Materials and MethodsAll patients who received bariatric surgery in Sweden between 2011–01-01 and 2019–03-31 were obtained from the Scandinavian Obesity Surgery Registry (SOReg). Information includes patients’ sociodemographic characteristics, details regarding the procedure, and postsurgical conditions. The SF-36 is applied at baseline and at follow-up years 1, 2, and 5. The multiple sequential imputation method was applied to handle missingness on SF-6D items. Based on the UK tariff, the SF-6D preference scores were calculated. The normative values for the mean (SD) SF-6D index were reported by timepoint and surgical complications for men and women, respectively. Multivariate analyses were applied to investigate how the SF-6D index is associated with timepoint, controlling for age, sex, BMI, and comorbidities in a stepwise manner.ResultsThe SF-6D index increased at 1 year relative to baseline and was roughly maintained at the same level at 2 years. The normative value of the SF-6D index can be used in economic evaluations for bariatric surgery.Graphical

Active Learning for Handling Missing Data.

Recently, the massive growth of IoT devices and Internet data, which are widely used in many applications, including industry and healthcare, has dramatically increased the amount of free unlabeled data collected. However, this unlabeled data is useless if we want to learn supervised machine learning models. The expensive and time-consuming cost of labeling makes the problem even more challenging. Here, the active learning (AL) technique provides a solution by labeling small but highly informative and representative data, which guarantees a high degree of generalizability over space and improves classification performance with data we have never seen before. The task is more difficult when the active learner has no predefined knowledge, such as initial training data, and when the obtained data is incomplete (i.e., contains missing values). In previous studies, the missing data should first be imputed. Then, the active learner selects from the available unlabeled data, regardless of whether the points were originally observed or imputed. However, selecting inaccurate imputed data points would negatively affect the active learner and prevent it from selecting informative and/or representative points, thus reducing the overall classification performance of the prediction models. This motivated us to introduce a novel query selection strategy that accounts for imputation uncertainty when querying new points. For this purpose, we first introduce a novel multiple imputation method that considers feature importance in selecting the most promising feature groups for missing values estimation. This multiple imputation method provides the ability to quantify the imputation uncertainty of each imputed data point. Furthermore, in each of the two phases of the proposed active learner (exploration and exploitation), imputation uncertainty is taken into account to reduce the probability of selecting points with high imputation uncertainty. We tested the effectiveness of the proposed active learner on different binary and multiclass datasets with different missing rates.

Evaluation and continuous improvement of magnetic components material design skills in interdisciplinary switched-mode power supply

The design of switched-mode power supplies (SMPSs) is a material-electrical engineering interdisciplinary problem. The design of magnetic component materials can significantly affect SMPSs’ performances. In engineering education, the design and development of solutions is an important skill for engineering students. However, the traditional engineering experiments curriculum is shown as validation and single-disciplinary experiments as well as one-sided assessment results. To foster open-ended exploration, enhance students’ material design skills, and improve skills adaptation, students must tackle real engineering problems and develop practical design solutions. This paper proposes an exploratory factor analysis and knowledge graph-based method for evaluating and continuously improving magnetic components material design skills in the context of SMPS design tasks. First, we used the multiple imputation method to address the missing data. Each imputed data was analyzed to extract factors through parallel analysis, ordinary least squares estimation, and target rotation. Then, we identified four sub-skills: efficiency design skill, passive device design skill, power magnetic reduction design skill, and power economy design skill. The RMSEA for the four-factor model is 0.042, suggesting a good fit to the data. We established the relationships between these sub-skills and SMPS performance metrics. Furthermore, the average of the factor scores from each of the imputed datasets and the SMPS design constraints were combined to obtain the cut-off scores to evaluate engineering students’ achievements in these sub-skills. Finally, we constructed an SMPS magnetic components material knowledge graph, which could recommend specific experimental tasks, relevant knowledge areas, and SMPS performance metrics, providing personalized guidance to designers.

To retrieve values of albuminuria to define the size of CKD patients eligible to SGLT-2Is: An explorative analysis using a primary care database.

To address missingness of albuminuria values, which establish the eligibility to SGLT-2Is for patients with CKD, using the multiple imputation (MI) method. We selected patients aged 18 or older and diagnosed with CKD in a primary care database. Those with severe CKD and/or previously treated with SGLT-2Is were excluded. Then, we collected all available information on albuminuria within 90 days the measure of GFR. A value of albumin-creatinine ratio (ACR) ranging 200-5000 mg/g or otherwise was the response variable on which we ran MI. Using logistic regression, odds ratios (OR) and related 95% confidence intervals (CI) were estimated for each covariate toward the response variable for both full and imputed dataset. The determinants showed consistent estimates between the full and imputed datasets as demonstrated by the overlaps of the CIs and the similar point estimates. As expected, there were some exceptions, such as diabetes (OR of 1.2 vs. 0.5) and use of diabetic medications (OR of 1.0 vs. 2.1) and/or statins (OR of 1.1 vs. 1.8). Besides being a reminder for GPs to prescribe and register albuminuria in certain patients' categories, these determinants might be translated into an operational algorithm to input ACR values in administrative data sources. Scenarios for the reimbursement criteria regulating SGLT-2Is to treat CKD would be therefore simulated on more inferable estimates.

Association between Plant-based Diet and Risk of Chronic Diseases and All-Cause Mortality in Centenarians in China: A Cohort Study

BackgroundNumerous studies have suggested the health benefits of a plant-based dietary pattern. However, whether this dietary pattern is associated with health benefits for centenarians remains unexplored. Our study aimed to investigate the correlation between 16 widely consumed Chinese food items and the incidence rates of chronic diseases and all-cause mortality among centenarians. MethodsWe conducted a dietary survey on 3372 centenarians with an average age of 102.33 y in China. After rigorous screening, we identified 2675 centenarians, who underwent a 10-y follow-up study with all-cause mortality as the primary outcome. We developed 6 dietary patterns on the basis of the food consumption frequency of each participant. To model the impact of missing values, we employed multiple imputation methods, verifying the robustness of models. ResultsThe overall plant-based diet index (PDI), healthy plant-based diet index (hPDI), unhealthy plant-based diet index (uPDI), healthy plant-based foods index (HPF), unhealthy plant-based foods index (uHPF), and animal-based foods index (AF) scores among centenarians in China were 46.95 ± 6.29, 44.43 ± 5.76, 51.09 ± 6.26, 21.63 ± 4.79, 9.91 ± 2.41, and 14.59 ± 3.58, respectively. High scores of PDI, hPDI, and HPF were associated with a lower risk of chronic diseases. In the 10-y follow-up study, 92.90% of centenarians have died. The high scores of the PDI (HRPDI = 0.81), hPDI (HRhPDI = 0.79), and HPF (HRHPF = 0.81) scores were significantly associated with a lower risk of death compared with the low scores. Conversely, the high AF score (HRAF = 1.17) was significantly associated with a higher risk of death compared with the low scores. ConclusionDespite the fact that a higher score in both a predominantly plant-based dietary pattern and a healthy dietary pattern can decrease the death among centenarians, not all HPFs have this effect. A higher AF predicted a higher risk of mortality, whereas higher PDI, hPDI, and HPF were associated with a lower risk of mortality among Chinese centenarians.

FaceNet recognition algorithm subject to multiple constraints: Assessment of the performance

Literature has it that the performance of most face recognition algorithms still decline in multiple constrained environments (Occlusions and Expressions), despite the achieved successes of deep learning face recognition algorithms. Using expression variant test face images synthetically occluded at 30% and 40% rates, the study evaluated the performance of FaceNet deep learning model for face recognition under the aforementioned constraints and when three (3) statistical multiple imputation methods (Multivariable Imputation using Chain Equations (MICE), MissForest and Regularized Expectation Maximization (RegEM)) are adopted for occlusion recovery. Results of the study showed improved recognition rates of the study algorithm when the imputation-based recovered faces were used for recognition compared with using their multiple constrained counterparts. However, test faces reconstructed with the MissForest imputation method were more accurately recognized using the FaceNet deep learning algorithm. Furthermore, the study demonstrated that some simple augmentation schemes sufficed to further enhance the performance of the FaceNet model. Specifically, the FaceNet algorithms gave the highest average recognition rates (85.19% and 79.5% for 30% and 40% occlusion levels respectively) under augmentation scheme IV (slight rotations, horizontal flipping, shearing, brightness adjustments, and stretching) using MissForest as the de-occlusion mechanism. The study also found that, no disparity existed in its performance with the choice of either Support Vector Machines (SVM) or City Block (CB) for classification under augmentation scheme IV. The study recommends using the MissForest imputation method in dealing with moderately high occluded test faces with varying expressions to enhance the performance of the FaceNet face recognition model.

Evaluating Imputation Methods for rainfall data under high variability in Johor River Basin, Malaysia

Missing values in rainfall records might result in erroneous predictions and inefficient management practices with significant economic, environmental, and social consequences. This is particularly important for rainfall datasets in Peninsular Malaysia (PM) due to the high level of missingness that can affect the inherent pattern in the highly variable time series. In this work, 21 target rainfall stations in the Johor River Basin (JRB) with daily data between 1970 and 2015 were used to examine 19 different multiple imputation methods that were carried out using the Multivariate Imputation by Chained Equations (MICE) package in R. For each station, artificial missing data were added at rates of up to 5%, 10%, 20%, and 30% for different types of missingness, namely, Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR), leaving the original missing data intact. The imputation quality was evaluated based on several statistical performance metrics, namely mean absolute error (MAE), root mean square error (RMSE), normalized root mean square error (NRMSE), Nash-Sutcliffe efficiency (NSE), modified degree of agreement (MD), coefficient of determination (R2), Kling-Gupta efficiency (KGE), and volumetric efficiency (VE), which were later ranked and aggregated by using the compromise programming index (CPI) to select the best method. The results showed that linear regression predicted values (norm.predict) consistently ranked the highest under all types and levels of missingness. For example, under MAR, MNAR, and MCAR, this method showed the lowest MAE values, ranging between 0.78 and 2.25, 0.93–2.57, and 0.87–2.43, respectively. It also consistently shows higher NSE and R2 values of 0.71–0.92, 0.6–0.92, and 0.66–0.91, and 0.77–0.92, 0.71–0.93, and 0.75–0.92 under MAR, MCAR, and MNAR, respectively. The methods of mean, rf, and cart also appear to be efficient. The incorporation of the compromise programming index (CPI) as a decision-support tool has enabled an objective assessment of the output from the multiple performance metrics for the ranking and selection of the top-performing method. During validation, the Probability Density Function (PDF) demonstrated that even with up to 30% missingness, the shape of the distribution was retained after imputation compared to the actual data. The methodology proposed in this study can help in choosing suitable imputation methods for other tropical rainfall datasets, leading to improved accuracy in rainfall estimation and prediction.

Effect of Spesolimab on Achieving Sustained Disease Remission in Patients with Generalized Pustular Psoriasis: Results from the Effisayil 2 Study

Introduction: Generalized pustular psoriasis (GPP) is a chronic, rare and potentially life-threatening disease characterized by flares of widespread skin pustulation. Sustained skin clearance is a key treatment objective. Intravenous (IV) spesolimab, an anti-interleukin-36 receptor monoclonal antibody, is approved for GPP flare treatment; however, the optimal dosing and long-term efficacy of subcutaneous (SC) spesolimab treatment in patients with GPP has not yet been reported. We report the effect of SC spesolimab on achieving sustained disease remission over 48 weeks in Effisayil 2 (NCT04399837), a trial that evaluated the efficacy and safety of SC spesolimab in preventing GPP flares.&#x0D; Methods: Patients with a history of GPP were randomized (1:1:1:1) to receive placebo (n=31), low- (n=31, 300 mg loading dose [LD]; 150 mg every 12 weeks [q12w]), medium- (n=31, 600 mg LD; 300 mg q12w) or high-dose (n=30, 600 mg LD; 300 mg every 4 weeks [q4w]) SC spesolimab over 48 weeks. Sustained remission was defined as GPP Physician Global Assessment (GPPGA) total score of 0 or 1 at all visits up to Week 48, or GPPGA total score of 0 or 1 and all GPPGA subscores ≤2 at all visits up to Week 48. Sustained pustular clearance was defined as GPPGA pustulation subscore of 0 at all visits from Week 4 to Week 48. Any use of IV spesolimab or another standard of care for GPP worsening was considered a failure of remission. Missing data were imputed by a sequential logistic regression multiple imputation method. Adjusted risk differences (RD) were calculated by the Mantel−Haenszel type-weighted average of differences.&#x0D; Results: 31 patients received placebo, and 30 patients received high-dose spesolimab. Compared to placebo, more patients had sustained remission in the high-dose arm using both GPPGA total score 0 or 1 (63.3% vs 29.0%; RD [95% CI] 0.35 [0.10–0.59]), and GPPGA total score 0 or 1 and all GPPGA subscores ≤2 (60.4% vs 29.0%; RD [95% CI] 0.32 [0.07–0.56]). Compared to placebo, more patients in the high-dose arm had sustained pustular clearance over 48 weeks (63.6% vs 25.8%; RD [95% CI] 0.38 [0.14–0.62]).&#x0D; Conclusion: Overall, high-dose SC spesolimab q4w is effective for the long-term management of GPP skin symptoms.