Multiple Imputation Techniques Research Articles

Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis

Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study aimed to predict LIM in GIST patients by constructing machine learning (ML) algorithms to assist clinicians in the decision-making process for treatment. Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases from 2010 to 2015 were assigned to the developing sets, while cases from 2016 to 2017 were assigned to the testing set. Missing values were addressed using the multiple imputation technique. Four algorithms were utilized to construct the models, comprising traditional logistic regression (LR) and automated machine learning (AutoML) analysis such as gradient boost machine (GBM), deep neural net (DL), and generalized linear model (GLM). We evaluated the models' performance using LR-based metrics, including the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), as well as AutoML-based metrics, such as feature importance, SHapley Additive exPlanation (SHAP) Plots, and Local Interpretable Model Agnostic Explanation (LIME). A total of 6207 patients were included in this study, with 2683, 1780, and 1744 patients allocated to the training, validation, and test sets, respectively. Among the different models evaluated, the GBM model demonstrated the highest performance in the training, validation, and test cohorts, with respective AUC values of 0.805, 0.780, and 0.795. Furthermore, the GBM model outperformed other AutoML models in terms of accuracy, achieving 0.747, 0.700, and 0.706 in the training, validation, and test cohorts, respectively. Additionally, the study revealed that tumor size and tumor location were the most significant predictors influencing the AutoML model's ability to accurately predict LIM. The AutoML model utilizing the GBM algorithm for GIST patients can effectively predict the risk of LIM and provide clinicians with a reference for developing individualized treatment plans.

An application of LASSO and multiple imputation techniques to income dynamics with cross‐sectional data

This paper introduces, validates, and applies a Least Absolute Shrinkage and Selection Operator with multiple imputation by Predictive Mean Matching (LASSO‐PMM) method to estimate intra‐generational income dynamics from cross‐sectional data. We validate the method using 36 harmonized panel data sets in four Latin American countries and apply it to cross‐section data from 43 countries across the world. Results show that LASSO‐PMM predictions are statistically indistinguishable from actual household poverty rates, mobility indicators, and income or consumption changes. These findings suggest that estimating economic mobility using a LASSO‐PMM approach may accurately approximate actual income dynamics when panel data are unavailable.

#1062 Effect of intradialytic resistance exercise on patient survival: a multicenter non-randomized cohort study

Abstract Background and Aims Intradialytic exercise significantly benefits the physical function of patients undergoing hemodialysis. Prior research into its impact on patient longevity has been constrained by small participant numbers and the lack of a control group. This study investigates the influence of exercise therapy during hemodialysis on patient lifespan in a broad multicenter cohort. Method This forward-looking, multicenter, non-randomized clinical investigation included 17 outpatient clinics from the Kaikoukai Medical Group, recruited between April and December 2018. Participants were voluntarily enlisted in an exercise regimen during their dialysis sessions and segregated into exercising and non-exercising cohorts. The trial was not blinded. The intradialytic regimen comprised stretching and four types of resistance exercises utilizing elastic tubes, conducted thrice weekly. Baseline data encompassing age, gender, stature, baseline weight, duration of hemodialysis treatment, and concurrent diabetic conditions were collected. Additional data regarding hemodialysis, including Kt/V ratio, hemodialysis blood flow rate (QB), dialysate volume (QD), and duration of dialysis, were recorded. Laboratory parameters such as albumin (Alb), calcium (Ca), potassium (K), hemoglobin (Hb), C-reactive protein (CRP), and creatinine (Cr) levels were extracted from medical records. Patient outcomes were monitored until either the occurrence of death or the study's culmination in December 2021. In statistical analyses, missing values were addressed using multiple imputation techniques, and baseline characteristics between groups were compared using an unpaired t-test. Cox proportional hazards regression analysis was employed to examine the relationship between the exercise intervention and survival, adjusting for multiple confounding variables. Three models were constructed for analysis: Model 1 incorporated basic patient data, Model 2 included hemodialysis parameters, and Model 3 involved laboratory data. The study was approved by the Ethical Committee of Nagoya Kyoritsu Hospital. Results The study encompassed 2006 subjects (average age 70.1±0.3 years), with 512 (25.5%) in the exercise group and 1494 (74.5%) in the control group. Initial comparisons indicated that the exercise group had significantly lower average height (160.9±0.3 vs. 159.7±0.4 cm, p=0.03), baseline weight (57.2±0.3 vs. 55.9±0.5 kg, p=0.04), Kt/V ratio (1.59±0 vs. 1.64±0, p=0.01), and QD (494±0.9 vs. 486.8±1.9 ml, p<0.01). Over the median observation period of 25 months, 338 (16.9%) patients, comprising 58 (11.3%) from the exercise group and 280 (18.7%) from the control group, succumbed. Survival analysis revealed a significant association between exercise intervention and mortality reduction, adjusted for confounders in Model 1 (HR 0.6, 95%CI 0.4-0.9, p=0.01), Model 2 (HR 0.55, 95%CI 0.37-0.8, p=0.02), and Model 3 (HR 0.57, 95%CI 0.38-0.87, p=0.01). Conclusion This research is the first to demonstrate a significant association between intradialytic resistance training and improved survival in hemodialysis patients within a multicenter, non-randomized cohort. This relationship remained significant after adjusting for multiple confounding factors, suggesting that incorporating resistance training into standard hemodialysis care could markedly enhance patient survival outcomes.

Exploring the Role of Cognitive and Physical Limitations in Explaining Identity Theft Victimization

ABSTRACT We use data from the 2018 Identity Theft Supplement of the U.S. National Crime Victimization Survey to examine the relationship between cognitive or physical limitations and identity theft victimization from a Routine Activities theory perspective. Using the binary logistic regression and multiple imputation technique for handling the missing data, we regressed several outcomes of identity theft victimization on cognitive or physical limitations while controlling for online and offline routines including protective strategies, individual characteristics, and previous victimization. We also tested whether age moderates the relationship between cognitive or physical limitations and victimization outcomes. The results suggest that the relationship between cognitive or physical limitations and victimization is generally significant and positive except for credit card victimization Our findings also revealed that some of these relationships are moderated by age.

Prevalence of High-Risk Human Papillomavirus Infection, Associated Risk Factors, and Relationship With Cervical Precancerous Lesions in Perimenopausal and Older Women in an Area With High Cervical Cancer Incidence in China.

Purpose This study delves into the epidemiology of high-risk human papillomavirus (HR-HPV) infection and its link to precancerous lesions among perimenopausal (40-59 years) and elderly (60-65 years) women in a Chinese county with a notably high incidence of cervical cancer. By uniquely focusing on these age groups in underdeveloped regions, the research aims to offer novel strategies for the management and prevention of cervical cancer. It seeks to inform targeted interventions and public health policies that could significantly benefit women at heightened risk for HPV, addressing a critical gap in current prevention efforts in economically disadvantaged communities. Methods This observational study was conducted at the Maternal and Child Health and Family Planning Service Centre in Lueyang County, from September 2021 to January 2022. It assessed 2008 women aged 40-65 for HPV screening, with 342 undergoing further cytological examination. The study evaluated the prevalence of HPV infection across different age groups and risk categories. It utilized a questionnaire to collect participants' basic information, health behaviors, and other relevant data to analyze factors influencing HR-HPV infection. Statistical analyses comprised chi-square tests, trend analysis, logistic regression, and multiple imputation techniques to address missing data. Results The prevalence of HR-HPV infection among women aged 40-65 years in Lueyang County was 18.43%. Older women exhibited a higher incidence of HPV infection, abnormal ThinPrep Cytology Test (TCT) results (Shaanxi Fu'an Biotechnology Co.Ltd., Baoji City, China), and low/high-grade squamous intraepithelial lesions (LSIL/HSIL) (P<0.05). The most prevalent HR-HPV genotypes in the overall, perimenopausal, and elderly groups were HPV-52, -53, and -58; HPV-52, -53, and -16; and HPV-58, -52, and -53, respectively. The prevalent HR-HPV genotypes in the abnormal The Bethesda System (TBS) results were HPV-16, -52, -33, -58; -16, -52, -58; and-16, -33, and -52. HPV-16, -18, -33 prevalence increased with increasing lesion severity (P<0.05). In this study, factors affecting HR-HPV in the three age groups were found to be mainly related to sexual behavior and education level, including history of lower genital tract diseases, multiple pregnancies, contraceptive methods without tubal ligation, age at first marriage greater than 18 years, never washing the vulva after sex, abstinence from sex, education level of junior high school or above, and spouse's education level of high school or above. Conclusions These findings suggest that the elevated rate of abnormal TBS in the older age group may be attributed to the higher prevalence of persistent infection-prone HR-HPV genotypes (HPV-58, -52, and-53), multiple infections, and potent oncogenic HR-HPV genotypes (HPV-16 and -33). Additionally, the higher HR-HPV prevalence in older patients may be related to lower education attainment, reduced screening rate, and limited condom usage. Therefore, strategies targeting perimenopausal and older women should prioritize enhancing health awareness, increasing screening rates, and encouraging condom utilization.

Prevalence and determinants of insufficient vitamin D status in young Canadian Inuit children from Nunavik.

Background: Vitamin D deficiency has reached pandemic levels affecting over one billion people worldwide. However, limited data is available on the prevalence and determinants of vitamin D status of Canadian Indigenous children and no study has been reported in Inuit children from Nunavik. Aim: Therefore, using data collected between 2006 and 2010, we aimed to investigate the prevalence and main determinants of insufficient serum 25-hydroxyvitamin D (s25(OH)D) concentrations in Inuit children attending childcare centres in Nunavik. Methods: This study included 245 Inuit children aged 11 to 54 months. s25(OH)D concentrations were measured by radioimmunoassay. Dietary intakes were assessed using 24-hour recalls. Usual dietary intakes were estimated using the National Cancer Institute method. We used a multiple imputation technique to replace missing values when performing regression analysis. Results: Our findings revealed that 64.5% of children had a s25(OH)D concentration < 75 nmol/L, while 78.1% did not meet the estimated average requirement (EAR) for vitamin D. Vitamin D intake and fluid milk consumption were positively associated with s25(OH)D concentrations, while negative associations were observed with children' energy intake, non-alcoholic beverage consumption, body weight, breastfeeding duration and, biological/adoptive/foster parents' educational level. Conclusion: Vitamin D inadequacy was highly prevalent and closely aligned with levels observed over the years in non-Indigenous children. Breastfed children who do not receive vitamin D supplementation, overweight and obese children, and children with inadequate milk consumption were at high risk of vitamin D insufficiency. Eating vitamin D rich foods such as fluid milk and seafood along with vitamin D supplementation when needed are recommended.

Compare the marginal effects for environmental exposure and biomonitoring data with repeated measurements and values below the limit of detection.

Environmental exposure and biomonitoring data with repeated measurements from environmental and occupational studies are commonly right-skewed and in the presence of limits of detection (LOD). However, existing model has not been discussed for small-sample properties and highly skewed data with non-detects and repeated measurements. Marginal modeling provides an alternative to analyzing longitudinal and cluster data, in which the parameter interpretations are with respect to marginal or population-averaged means. We outlined the theories of three marginal models, i.e., generalized estimating equations (GEE), quadratic inference functions (QIF), and generalized method of moments (GMM). With these approaches, we proposed to incorporate the fill-in methods, including single and multiple value imputation techniques, such that any measurements less than the limit of detection are assigned values. We demonstrated that the GEE method works well in terms of estimating the regression parameters in small sample sizes, while the QIF and GMM outperform in large-sample settings, as parameter estimates are consistent and have relatively smaller mean squared error. No specific fill-in method can be deemed superior as each has its own merits. Marginal modeling is firstly employed to analyze repeated measures data with non-detects, in which only the mean structure needs to be correctly provided to obtain consistent parameter estimates. After replacing non-detects through substitution methods and utilizing small-sample bias corrections, in a simulation study we found that the estimating approaches used in the marginal models have corresponding advantages under a wide range of sample sizes. We also applied the models to longitudinal and cluster working examples.

Prediction of daily fine particulate matter (PM2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

AbstractThis study analyzed the temporal variation and prediction of fine particulate matter (PM2.5) concentrations in Aksaray, Turkey, a city in Central Anatolia. The relationship between PM2.5 and meteorological parameters such as temperature, humidity, wind speed, and wind direction was investigated. An artificial neural network (ANN) model was developed to predict PM2.5 levels based on meteorological data and air pollutant information. Seasonal and diurnal patterns of PM2.5 concentrations were observed, with higher values recorded during the winter and lower values during the summer. Additionally, higher levels were observed in the morning and evening, while lower levels were recorded in the afternoon. The variations in meteorological parameters, especially temperature and wind speed, significantly influenced PM2.5 levels. To predict hourly PM2.5 concentrations, single and multiple data imputation techniques were employed in combination with resilient back‐propagation (RPROP‐ANN). The neural network was applied, consisting of one input layer comprising 11 parameters, one hidden layer with 20 neurons, and an output layer. The results indicate that the best forecasting performance for PM2.5 was demonstrated by the combination of the missForest imputation technique with the RPROP neural network, as assessed by the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The proposed model is characterized by a low RMSE of 5.94 and a high R2 value of 0.88, demonstrating exceptional predictive performance in air quality.

Parental Involvement, Academic Self-Efficacy, and Depression on Academic Performance Among Chinese Students During COVID-19 Pandemic.

This study was conducted to identify the factors (especially parental involvement, academic self-efficacy, and depression) associated with academic performance among Chinese K-12 students during the COVID-19 pandemic. This cross-sectional study used data from the 2020 China Family Panel Studies (CFPS). The CFPS was conducted from July to December 2020 during the COVID-19 pandemic. A multistage probability sample proportional to size was used for the survey. The final sample consisted of 1747 K-12 students. This study used the 14-item Chinese Parental Involvement and Support Scale, the Responsibility Scale, and the 8-item Center for Epidemiologic Studies Depression Scale to measure parental involvement, academic self-efficacy, and depression, respectively. An ordered probit regression and structural equation models were employed to analyze the factors associated with academic performance. A multiple imputation technique was applied to compute missing values in selected variables. We found that parental involvement and academic self-efficacy were positively associated with good academic performance. In contrast, depression was negatively associated with good academic performance. Moreover, academic stress, male, rural residency, middle school, family size, high income, online gaming daily, reading, and intelligence quotient were statistically significant predictors on academic performance. The empirical findings suggested that parental involvement and academic self-efficacy were positively and significantly associated with good academic performance. However, depression was negatively and significantly associated with good academic performance. These results showed that policymakers and practitioners can help improve academic success and address educational inequalities among K-12 students by implementing a series of reforms.

An inflexible adherence to food rules mediates the longitudinal association between shape/weight overvaluation and binge eating

Understanding factors that account for the persistence of binge eating is necessary for developing effective early intervention and treatment approaches. The cognitive-behavioral model of eating disorders proposes that judgements of self-worth contingent upon weight and shape encourage adoption of multiple demanding rules related to food, eating and dieting, in turn increasing vulnerability to binge eating. However, there is limited longitudinal evidence supporting this hypothesized sequence of events. This study tested whether the longitudinal association between shape/weight overvaluation and binge eating is mediated by an inflexible adherence to food rules. Participants were 1760 adult women recruited from an online eating disorder platform who were invited to complete study measures at baseline (T1), 3-month follow-up (T2), and 6-month follow-up (T3). Path analysis was conducted to test the hypothesized associations. Missing data were handled using multiple imputation techniques. A significant indirect effect emerged, in that higher overvaluation scores at T1 predicted increased inflexible adherence to food rules at T2, which in turn predicted increased binge eating symptoms at T3. Findings offer support for the predictions outlined by the cognitive-behavioral model of eating disorders and highlight clear targets for early intervention and treatment.

An enhanced feature extraction based long short-term memory neural network for wind power forecasting via considering the missing data reconstruction

Wind power forecasting plays a significant role in regulating the peak and frequency of the power system, which can improve the wind power receiving capacity. Despite plenty of forecasting methods have been proposed to fortify the accuracy of forecasting, the existing forecasting models do not consider the reconstruction of missing data and can not extract the spatiotemporal features from the wind power data. To address these issues, this study proposes an improved long short-term memory (LSTM) network based forecasting method to reconstruct the missing data and capture the spatiotemporal features from wind power data. In order to fortify the wind power forecasting accuracy of the model, the multiple imputation technique (MIT) is first developed to fill up the missing samples with reconstructed data samples by analyzing the correlation among variables in the raw wind power data. Secondly, to exploit the spatial and temporal features and reduce the low computation complexity, a new parallel convolutional network involving dilated convolution and causal convolution is established for features extraction. Finally, to further improve the wind power forecasting performance, the LSTM is applied to extract the long-term trends and reveal the internal relations of the derived spatiotemporal features. The experimental results on a benchmark dataset and an experimental dataset both demonstrate that the proposed LSTM based wind power forecasting can obtain the better forecasting performance.

Factors affecting academic performance of college students in China during COVID-19 pandemic: a cross-sectional analysis.

Understanding the factors that affected academic performance of students during the COVID-19 pandemic will help design effective interventions for improving students' academic performance during emergency situations as well as during regular academic environment. This cross-sectional study aimed to identify the factors that explain academic performance of students in China during the pandemic. Data on college students from the 2020 China Family Panel Studies were used, and the final sample consisted of 728 students. Ordered probit regression models were estimated to explain students' relative performance in the semester when the in-person classes were suspended by using various student and household-related variables and characteristics. To compute missing values in selected variables, a multiple imputation technique was applied. The odds of poor academic performance declined with higher Internet use for academic purposes, but Internet use for entertainment increased the probability of being in the poor academic performance. College students who spent more time studying on college work were less likely to have poor academic performance. This study identified the factors (Internet use and study time) associated with academic performance among Chinese college students during the COVID-19 pandemic. These results can be used to design policies to improve educational outcomes and to address educational inequalities.

Gestational weight gain charts for Latin American adolescents.

Monitoring gestational weight gain (GWG) throughout pregnancy among adolescents is important for detecting individuals at risk and timely intervention. However, there are no specific tools or guidelines for GWG monitoring of this group. We aimed to construct GWG charts for pregnant adolescents (10-19 years old) according to pre-pregnancy body mass index (BMI) using a pooled dataset from nine Latin American countries. Datasets from Argentina, Brazil, Chile, Colombia, Mexico, Panama, Paraguay, Peru, and Uruguay collected between 2003 and 2021 were combined after data cleaning and harmonization. Adolescents free of diseases that could affect GWG and who gave birth to newborns weighing between 2,500-4,000 g and free of congenital malformations were included. Multiple imputation techniques were applied to increase the sample size available for underweight and obesity categories. Generalized Additive Models for Location, Scale, and Shape were used to construct the charts of GWG according to gestational age. Internal and external validation procedures were performed to ensure that models were not over-adjusted to the data. The cohort included 6,414 individuals and 29,414 measurements to construct the charts and 1,684 individuals and 8,879 measurements for external validation. The medians (and interquartile ranges) for GWG at 40 weeks according to pre-pregnancy BMI were: underweight, 14.9 (11.9-18.6); normal weight, 14.0 (10.6-17.7); overweight, 11.6 (7.7-15.6); obesity, 10.6 kg (6.7-14.3). Internal and external validation showed that the percentages above/below selected percentiles were close to those expected, except for underweight adolescents. These charts describe the GWG throughout pregnancy among Latin American adolescents and represent a significant contribution to the prenatal care of this group. GWG cut-offs based on values associated with lower risks of unfavorable outcomes for the mother-child binomial should be determined before implementing the charts in clinical practice.

Projected changes in sitting and physical activity among midlife and older men and women in Finland

Abstract Aim Population-based projections of sitting and physical activity (PA) help to guide PA programs. We aimed to project total and context specific sitting and PA until year 2028 in adults aged 46–74 years in Finland. Subject and methods The population based DILGOM Study in 2007 and 2014 provided longitudinal data on self-reported weekday sitting in five contexts (work, vehicle, at home in front of TV, at home by computer, elsewhere), total sitting, and PA in three domains (occupational, commuting and leisure time). Projections until 2028 were generated using a Markovian multistate model and multiple imputation techniques by gender, age and education. Results Total weekday sitting was projected to increase until 2028 only in the 64–74-year-olds and the low educated (+ 24 and + 32 min/day, p &lt; 0.05, respectively). Sitting at home by computer was projected to increase on average 30 min/weekday (p &lt; 0.05) and occupational PA decrease by 8 to 20%-units (p &lt; 0.05) in all midlife and older adults. Further, sitting at home by TV and sitting elsewhere were projected to decrease in many, although not all groups. Conclusion Projected changes suggest increase in sitting by computer and decrease in occupational PA, which indicate the growing importance of leisure-time as the potential mean to increase PA.

An investigation of the imputation techniques for missing values in ordinal data enhancing clustering and classification analysis validity

Missing data can significantly impact dataset integrity and suitability, leading to unreliable statistical results, distortions, and poor decisions. The presence of missing values in data introduces inaccuracies in clustering and classification and compromises the reliability and validity of such analyses. This study investigates multiple imputation techniques specifically designed for handling missing values in ordinal data commonly encountered in surveys and questionnaires. Quantitative approaches are used to evaluate different imputation methods on various datasets with varying missing value percentages. By comparing the performance of imputation techniques using clustering metrics and algorithms (e.g., k-means, Partitioning Around Medoids), the study provides valuable insights for selecting appropriate imputation methods for accurate data analysis. Furthermore, the study examines the impact of imputed values on classification algorithms, including k-Nearest Neighbors (kNN), Naive Bayes (NB), and Multilayer Perceptron (MLP). Results demonstrate that the decision tree method is the most effective approach, closely aligning with the original data and achieving high accuracy. In contrast, random number imputation performs poorly, indicating limited reliability. This study advances the understanding of handling missing values and emphasizes the need to address this issue to enhance data analysis integrity and validity.

High-Dimensional Imputation for the Social Sciences: A Comparison of State-of-The-Art Methods

Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant high-dimensional MI techniques that can handle a large number of predictors in the imputation models and general missing data patterns. We assessed the relative performance of seven high-dimensional MI methods with a Monte Carlo simulation study and a resampling study based on real survey data. The performance of the methods was defined by the degree to which they facilitate unbiased and confidence-valid estimates of the parameters of complete data analysis models. We found that using lasso penalty or forward selection to select the predictors used in the MI model and using principal component analysis to reduce the dimensionality of auxiliary data produce the best results.

Measuring bereavement prevalence in a complex sampling survey: the 2019 Georgia Behavioral Risk Factor Surveillance System (BRFSS)

BackgroundThe Behavioral Risk Factor Surveillance System (BRFSS) is an annual survey designed to identify trends in the public’s health. In its 2019 field survey, the U.S. state of GA tested a new 3 – item module to measure the numbers of bereaved, resident adults aged 18 years and older. Participants were eligible if they answered ‘Yes’ to the item ‘Have you experienced the death of a family member or close friend in the years 2018 or 2019?’. This analysis explores two research questions. Can estimates for bereavement prevalence be derived without large sampling errors, low precision, and small subsamples? Can multiple imputation techniques be applied to overcome non-response and missing data to support multivariate modeling?MethodsBRFSS is a non-institutionalized sample of adults aged 18 years and older living in the U.S. state of Georgia. Analyses in this study were conducted under two scenarios. Scenario 1 applies the complex sample weights created by the Centers for Disease Control and imputes values for missing responses. Scenario 2 treats the data as a panel – no weighting combined with removal of persons with missing data. Scenario 1 reflects the use of BRFSS data for public health and policy, while Scenario 2 reflects data as it is commonly used in social science research studies.ResultsThe bereavement screening item has a response rate (RR) of 69.1% (5206 of 7534 persons). Demographic subgroups and categories of health have RR of 55% or more. Under Scenario 1, the estimated prevalence of bereavement is 45.38%, meaning that 3,739,120 adults reported bereaved in 2018 or 2019. The estimated prevalence is 46.02% with Scenario 2 which removes persons with any missing data (4,289 persons). Scenario 2 overestimates the bereavement prevalence by 1.39%. An illustrative logistic model is presented to show the performance of exposure to bereavement under the two data scenarios.ConclusionsRecent bereavement can be ascertained in a surveillance survey accounting for biases in response. Estimating bereavement prevalence is needed for measuring population health. This survey is limited to one US state in a single year and excludes persons aged 17 years and younger.

Multiple imputation using chained equations for missing data in survival models: applied to multidrug-resistant tuberculosis and HIV data.

Missing data are a prevalent problem in almost all types of data analyses, such as survival data analysis. To evaluate the performance of multivariable imputation via chained equations in determining the factors that affect the survival of multidrug-resistant-tuberculosis (MDR-TB) and HIV-coinfected patients in KwaZulu-Natal. Secondary data from 1542 multidrug-resistant tuberculosis patients were used in this study. First, data from patients with some missing observations were deleted from the original data set to obtain the complete case (CC) data set. Second, missing observations in the original data set were imputed 15 times to obtain complete data sets using a multivariable imputation case (MIC). The Cox regression model was fitted to both the CC and MIC data, and the results were compared using the model goodness of fit criteria [likelihood ratio tests, Akaike information criterion (AIC), and Bayesian Information Criterion (BIC)]. The Cox regression model fitted the MIC data set better (likelihood ratio test statistic =76.88 on 10 df with P<0.01, AIC =1040.90, and BIC =1099.65) than the CC data set (likelihood ratio test statistic =42.68 on 10 df with P<0.01, AIC =1186.05 and BIC =1228.47). Variables that were insignificant when the model was fitted to the CC data set became significant when the model was fitted to the MIC data set. Correcting missing data using multiple imputation techniques for the MDR-TB problem is recommended. This approach led to better estimates and more power in the model.

Efficacy and Safety of Mifepristone in the Treatment of Male US Veterans With Posttraumatic Stress Disorder

To date, no psychopharmacologic treatment has been found to be uniformly effective in veterans with posttraumatic stress disorder (PTSD); novel targets and approaches are needed to treat this disabling disorder. To examine whether treatment with the glucocorticoid receptor antagonist mifepristone yields a signal for clinical efficacy in male veterans with PTSD. This phase 2a, double-blind, parallel-group randomized clinical trial was conducted from November 19, 2012 (accrual started), through November 16, 2016 (final follow-up), within the US Department of Veterans Affairs. Participants were male veterans with chronic PTSD and a screening Clinician-Administered PTSD Scale score of 50 or higher. A total of 181 veterans consented to participation. Statistical analysis was conducted between August 2014 and May 2017. Participants were randomized in a 1:1 ratio to mifepristone (600 mg) or matched placebo taken orally for 7 days. The clinical outcome was whether a veteran achieved a clinical response status (a reduction of ≥30% of total Clinician-Administered PTSD Scale score from baseline) at 4- and 12-week follow-up. On the basis of a binary statistical selection rule, a difference in the proportion of treatment vs control group responders of 15% would be a clinically relevant difference. Self-report measures of PTSD and associated symptoms were also obtained. Neuroendocrine outcomes and plasma levels of mifepristone were measured. Safety was assessed throughout the study. The primary analysis was based on a multiple imputation technique to address missing outcome data; thus, some participant numbers may not appear as whole numbers. A total of 81 veterans were enrolled and randomized. Excluding 1 participant randomized in error, 80 were included in the modified intention-to-treat analysis (41 randomized to mifepristone and 39 to placebo). The mean (SD) age was 43.1 (13.7) years. A total of 15.6 (38.1%) in the mifepristone group and 12.1 (31.1%) in the placebo group were clinical responders at 4 weeks in the analysis using the multiple imputation technique. The group difference in the proportion of clinical responders (7.0%) was less than the predefined margin of 15% indicating signal for clinical efficacy. In an exploratory analysis, the difference in response to mifepristone vs placebo in the subgroup with no lifetime history of traumatic brain injury (TBI) (7.0 [50.0%] vs 3.0 [27.3%]; difference, 22.7%) exceeded the efficacy margin at 4 weeks and was sustained at 12 weeks. In contrast, in veterans with PTSD and lifetime TBI, the response rate to mifepristone was lower than placebo at 12 weeks (7.4 [27.4%] vs 13.5 [48.3%]; difference, -20.9%). This study did not detect a signal for efficacy for mifepristone at 600 mg/d for 1 week in male veterans with chronic PTSD. Thus, this study does not support a phase 3 trial in this population. Future studies of mifepristone for the treatment of PTSD may be of interest in those without a history of TBI or in samples with a low base rate of lifetime head trauma. ClinicalTrials.gov Identifier: NCT01946685.

Association of climate factors with dengue incidence in Bangladesh, Dhaka City: A count regression approach

BackgroundIn Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease. MethodsA total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg−1), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values. ResultsThe mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall. ConclusionThe findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.