Diagnostics for multivariate imputations

Association of severe postpartum hemorrhage and development of psychological disorders: Results from the prospective and multicentre HELP MOM study

OBJECTIVE

Post-partum hemorrhage (PPH) is the leading preventable cause of worldwide maternal morbidity and mortality. Risk factors for psychological disorders following PPH are currently unknown. HELP-MOM study aimed to determine the incidence and identify risk factors for psychological disorders following PPH.

METHODS

HELP-MOM study was a prospective, observational, national, and multicentre study including patients who experienced severe PPH requiring sulprostone. The primary endpoint was the occurrence of psychological disorders (anxiety and/or post-traumatic disorder and/or depression) following PPH, assessed at 1, 3, and 6 months after delivery using HADS, IES-R, and EPDS scales.

RESULTS

Between November 2014 and November 2016, 332 patients experienced a severe PPH and 236 (72%) answered self-questionnaires at 1, 3, and 6 months. A total of 161 (68%) patients declared a psychological disorder following severe PPH (146 (90.1%) were screened positive for anxiety, 96 (58.9%) were screened positive for post-traumatic stress disorder, and 94 (57.7%) were screened positive for post-partum depression). In multivariable analysis, the use of intra-uterine tamponnement balloon was associated with a lower risk to be screened positive for psychological disorder after severe PPH (OR = 0.33 [IC95% 0.15-0.69], p = 0.004, and after propensity score matching (OR=0.34 [IC95% 0.12-0.94], p = 0.04)). Low hemoglobin values during severe PPH management were associated with a higher risk of being screened positive for psychological disorders. Finally, we did not find differences in desire or pregnancy between patients without or with psychological disorders occurring in the year after severe PPH.

DISCUSSION

Severe PPH was associated with significant psychosocial morbidity including anxiety, post-traumatic disorder, and depression. This should engage a psychological follow-up. Large cohorts are urgently needed to confirm our results.

REGISTRATION

ClinicalTrials.gov under number NCT02118038.

Toward a standardized evaluation of imputation methodology

Developing new imputation methodology has become a very active field. Unfortunately, there is no consensus on how to perform simulation studies to evaluate the properties of imputation methods. In part, this may be due to different aims between fields and studies. For example, when evaluating imputation techniques aimed at prediction, different aims may be formulated than when statistical inference is of interest. The lack of consensus may also stem from different personal preferences or scientific backgrounds. All in all, the lack of common ground in evaluating imputation methodology may lead to suboptimal use in practice. In this paper, we propose a move toward a standardized evaluation of imputation methodology. To demonstrate the need for standardization, we highlight a set of possible pitfalls that bring forth a chain of potential problems in the objective assessment of the performance of imputation routines. Additionally, we suggest a course of action for simulating and evaluating missing data problems. Our suggested course of action is by no means meant to serve as a complete cookbook, but rather meant to incite critical thinking and a move to objective and fair evaluations of imputation methodology. We invite the readers of this paper to contribute to the suggested course of action.

Understanding suicidality in Pacific adolescents in New Zealand using network analysis

INTRODUCTION

Pacific adolescents in New Zealand (NZ) are three to four times more likely than NZ European adolescents to report suicide attempts and have higher rates of suicidal plans. Suicidal thoughts, plans, and attempts, termed suicidality in this study, result from a complex dynamic interplay of factors, which emerging methodologies like network analysis aim to capture.

METHODS

This study used cross-sectional network analysis to model the relationships between suicidality, self-harm, and individual depression symptoms, whilst conditioning on a multi-dimensional set of variables relevant to suicidality. A series of network models were fitted to data from a community sample of New Zealand-born Pacific adolescents (n = 550; 51% male; Mean age (SD) = 17 (0.35)).

RESULTS

Self-harm and the depression symptom measuring pessimism had the strongest associations with suicidality, followed by symptoms related to having a negative self-image about looks and sadness. Nonsymptom risk factors for self-harm and suicidality differed markedly.

CONCLUSIONS

Depression symptoms varied widely in terms of their contribution to suicidality, highlighting the valuable information gained from analysing depression at the symptom-item level. Reducing the sources of pessimism and building self-esteem presented as potential targets for alleviating suicidality amongst Pacific adolescents in New Zealand. Suicide prevention strategies need to include risk factors for self-harm.

Graphical and numerical diagnostic tools to assess multiple imputation models by posterior predictive checking

Problem

The congenial of the imputation model is crucial for valid statistical inferences. Hence, it is important to develop methodologies for diagnosing imputation models.

Aim

We propose and evaluate a new diagnostic method based on posterior predictive checking to diagnose the congeniality of fully conditional imputation models. Our method applies to multiple imputation by chained equations, which is widely used in statistical software.

Methods

The proposed method compares the observed data with their replicates generated under the corresponding posterior predictive distributions to diagnose the performance of imputation models. The method applies to various imputation models, including parametric and semi-parametric approaches and continuous and discrete incomplete variables. We studied the validity of the method through simulation and application.

Results

The proposed diagnostic method based on posterior predictive checking demonstrates its validity in assessing the performance of imputation models. The method can diagnose the consistency of imputation models with the substantive model and can be applied to a broad range of research contexts.

Conclusion

The diagnostic method based on posterior predictive checking provides a valuable tool for researchers who use fully conditional specification to handle missing data. By assessing the performance of imputation models, our method can help researchers improve the accuracy and reliability of their analyzes. Furthermore, our method applies to different imputation models. Hence, it is a versatile and valuable tool for researchers identifying plausible imputation models.

Selection of statistical technique for imputation of single site-univariate and multisite-multivariate methods for particulate pollutants time series data with long gaps and high missing percentage

Monitoring air contaminants has become essential to exposure science, toxicology, and public health research. However, missing values are common while monitoring air contaminants, especially in resource-constrained settings such as power cuts, calibration, and sensor failure. In contaminants monitoring, evaluating existing imputation techniques for dealing with recurrent periods of missing and unobserved data are limited. The proposed study aims to perform a statistical evaluation of six univariate and four multivariate time series imputation methods. The univariate methods are based on inter-time correlation characteristics, and the multivariate approach considers muti-site to impute missing data. The present study retrieved data from 38 ground-based monitoring stations for particulate pollutants in Delhi for 4 years. For univariate methods, missing values were simulated under 0-20% (5%, 10%, 15%, and 20%), and high 40%, 60%, and 80% missing levels having long gaps. Before evaluating multivariate methods, input data underwent pre-processing steps: selecting the target station to be imputed, choosing covariates based on the spatial correlation between multiple sites, and framing a combination of target and neighbouring stations (covariates) under 20%, 40%, 60%, and 80%. Next, the particulate pollutants data of 1480 days is provided as input to four multivariate techniques. Finally, the performance of each algorithm was evaluated using error metrics. The results show that the long interval time series data and spatial correlation of multiple stations significantly improved outcomes for univariate and multivariate time series methods. The univariate Kalman_arima performs well for long-missing gaps and all missing levels (except for 60-80%), yielding low error and high R² and d values. In contrast, multivariate MIPCA performed better than Kalman-arima for all target stations with the highest missing percentage.

Data processing pipeline for cardiogenic shock prediction using machine learning

Introduction

Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS.

Methods

We mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)-based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction.

Results

We achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization.

Conclusion

We believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.

A Method of Pruning and Random Replacing of Known Values for Comparing Missing Data Imputation Models for Incomplete Air Quality Time Series

The data obtained from air quality monitoring stations, which are used to carry out studies using data mining techniques, present the problem of missing values. This paper describes a research work on missing data imputation. Among the most common methods, the method that best imputes values to the available data set is analysed. It uses an algorithm that randomly replaces all known values in a dataset once with imputed values and compares them with the actual known values, forming several subsets. Data from seven stations in the Silesian region (Poland) were analyzed for hourly concentrations of four pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particles of 10 μm or less (PM10) and sulphur dioxide (SO2) for five years. Imputations were performed using linear imputation (LI), predictive mean matching (PMM), random forest (RF), k-nearest neighbours (k-NN) and imputation by Kalman smoothing on structural time series (Kalman) methods and performance evaluations were performed. Once the comparison method was validated, it was determine that, in general, Kalman structural smoothing and the linear imputation methods best fitted the imputed values to the data pattern. It was observed that each imputation method behaves in an analogous way for the different stations The variables with the best results are NO2 and SO2. The UMI method is the worst imputer for missing values in the data sets.

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics-including transcriptomic, methylation, metabolomic-data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure.

Blocking and being blocked on gay dating apps among MSM attending a sexual health clinic: an observational study

Background

There are limited studies on blocking and men who have sex with men (MSM) health outcomes. We need such data in China, to better understand the relationship between Chinese MSM gay app use and health outcomes, thus providing insight on risky sexual behaviors and HIV transmission among Chinese MSM - one of the world’s largest MSM communities. Blocking someone is when users select a function on an app to prevent another user from contacting them and being blocked is when someone is prevented from contacting another user. We studied the correlates of blocking on the world’s largest gay dating app among Chinese MSM (N = 208).

Methods

We conducted a cross-sectional survey as part of an HIV testing intervention in Guangzhou, China, May–December 2019. Using logistic regression models, we estimated the correlates of blocking (e.g. sociodemographic characteristics, sexual behavior, HIV testing history, social network data).

Results

MSM had a mean age of 27.9 years (SD = 7.1) and median of one sexual partner in the last 3 months. About 62% had blocked someone in their lifetime and 46% had been blocked in their lifetime. Each additional male partner was associated with an 87% (aOR = 1.87, 95%CI = 1.03, 3.40) increased chance of being blocked. Reporting a versatile sexual role was related with a 90% (aOR = 0.10, 95%CI = 0.02, 0.45) decreased likelihood of blocking behavior and an 86% (aOR = 0.14, 95%CI = 0.04, 0.46) reduced chance of being blocked.

Conclusions

Number of male partners may be associated with blocking behavior, with implications for the design of online sexual health interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12182-w.

Predicting Depression and Post-Traumatic Stress Symptoms Following Burn Injury: A Risk Scoring System

Depression and post-traumatic stress are common psychiatric comorbidities following burn injury. The purpose of this study was to develop an admission scoring system that assesses the risk of development of depression or post-traumatic symptoms in the burn population. This study is a retrospective review of the prospectively collected Burn Model System National Database. Adult burn survivors enrolled from 2014-2018 (n=486) were included. The primary outcome was the presence of depression or post-traumatic stress symptoms at 6, 12, or 24 months post-injury. Logistic regression analysis was used to identify demographic and clinical predictors of depression and post-traumatic stress symptoms. A risk scoring system was then created based on assigning point values to relevant predictor factors. The study population had a mean age of 46.5±15.8 years, mean burn size of 18.3±19.7%, and was 68.3% male. Prior to injury, 71.3% of the population was working, 47.9% were married, and 50.8% had completed more than a high school education. An 8-point risk scoring system was developed using the following predictors of depression or post-traumatic stress symptom development: gender, psychiatric treatment in the past year, graft size, head/neck graft, etiology of injury, and education level. This study is the first to develop a depression and post-traumatic stress symptom risk scoring system for burn injury. This scoring system will aid in identifying burn survivors at high risk of long-term psychiatric symptoms that may be used to improve screening, monitoring, timely diagnosis and interventions.

Resilience and mental health in individuals with spinal cord injury during the COVID-19 pandemic

Study design

Cross-sectional, observational study.

Objectives

To understand how resilience, access to personal care attendants (PCAs) and medical supplies, and concerns about medical rationing, finances, and social isolation are related to overall and mental health in individuals with spinal cord injury (SCI) in the context of the COVID-19 pandemic.

Setting

Community dwelling adults (N = 187) with SCI.

Methods

Data were collected online between May 1, 2020 and August 31, 2020. Outcomes were overall and mental health, depression and anxiety symptoms, and quality of life (QoL). Predictors were resilience, access to PCAs and medical supplies, and concerns about medical rationing, finances, and social isolation.

Results

Incomplete injury, concern about medical rationing, medical supply disruption, and social isolation predicted a greater perceived impact of the pandemic on overall heath. Younger age, decreased resilience, and concern about medical rationing and social isolation predicted greater perceived impact of the pandemic on mental health. Decreased resilience and concern about medical rationing and finances predicted increased anxiety symptoms. Incomplete injury, believing that medical rationing was occurring, decreased resilience, and concern about finances and social isolation predicted increased depressive symptoms. Decreased resilience and concern about finances, medical rationing, and social isolation predicted lower QoL.

Conclusions

The negative effects of the pandemic on the overall and mental health of individuals with SCI may be ameliorated by resilience. In future crises, it may be beneficial to screen individuals for resilience so that those with decreased resilience are offered the appropriate resources to enhance resilience and improve overall wellbeing.

Global abundance estimates for 9,700 bird species

Quantifying the abundance of species is essential to ecology, evolution, and conservation. The distribution of species abundances is fundamental to numerous longstanding questions in ecology, yet the empirical pattern at the global scale remains unresolved, with a few species' abundance well known but most poorly characterized. In large part because of heterogeneous data, few methods exist that can scale up to all species across the globe. Here, we integrate data from a suite of well-studied species with a global dataset of bird occurrences throughout the world-for 9,700 species (∼92% of all extant species)-and use missing data theory to estimate species-specific abundances with associated uncertainty. We find strong evidence that the distribution of species abundances is log left skewed: there are many rare species and comparatively few common species. By aggregating the species-level estimates, we find that there are ∼50 billion individual birds in the world at present. The global-scale abundance estimates that we provide will allow for a line of inquiry into the structure of abundance across biogeographic realms and feeding guilds as well as the consequences of life history (e.g., body size, range size) on population dynamics. Importantly, our method is repeatable and scalable: as data quantity and quality increase, our accuracy in tracking temporal changes in global biodiversity will increase. Moreover, we provide the methodological blueprint for quantifying species-specific abundance, along with uncertainty, for any organism in the world.

Practical strategies for handling breakdown of multiple imputation procedures

Multiple imputation is a recommended method for handling incomplete data problems. One of the barriers to its successful use is the breakdown of the multiple imputation procedure, often due to numerical problems with the algorithms used within the imputation process. These problems frequently occur when imputation models contain large numbers of variables, especially with the popular approach of multivariate imputation by chained equations. This paper describes common causes of failure of the imputation procedure including perfect prediction and collinearity, focusing on issues when using Stata software. We outline a number of strategies for addressing these issues, including imputation of composite variables instead of individual components, introducing prior information and changing the form of the imputation model. These strategies are illustrated using a case study based on data from the Longitudinal Study of Australian Children.

Fully synthetic neuroimaging data for replication and exploration

Scientific transparency, data exploration, and education are advanced through data sharing. However, risk for disclosure of personal information and institutional data sharing regulations can impede human subject/patient data sharing and thus limit open science initiatives. Sharing fully synthetic data is an alternative when it is not possible to share real or observed data. Here we describe a data sharing approach that borrows principles and methods from multiple imputation to replace observed values with synthetic values, thereby creating a fully synthetic neuroimaging dataset that accurately represents the covariance structure of the observed dataset. Predictor tables composed of demographic, site, behavioral and total intracranial volume (ICV) variables from 264 pediatric cases were used to create synthetic predictor tables, which were then used to synthesize gray matter images derived from T1-weighted data. The synthetic predictor tables demonstrated pooled variance and statistical estimates that closely approximated the observed data, as reflected in measures of efficiency and statistical bias. Similarly, the synthetic gray matter data accurately represented the variance and voxel-level associations with predictor variables (age, sex, verbal IQ, and ICV). The magnitude and spatial distribution of gray matter effects in the observed imaging data were replicated in the pooled results from the synthetic datasets. This approach for generating fully synthetic neuroimaging data has widespread potential for data sharing, including replication, new discovery, and education. Fully synthetic neuroimaging datasets can enable data-sharing because it accurately represents patterns of variance in the original data, while diminishing the risk of privacy disclosures that can accompany neuroimaging data sharing.

[Predictive Mean Matching as an alternative imputation method to hot deck in Vigitel]

This study aimed to describe the estimated means for weight, height, and body mass index (BMI) according to two imputation methods, using data from Vigitel (Risk and Protective Factors Surveillance System for Chronic Non-Communicable Diseases Through Telephone Interview). This was a cross-sectional study that used secondary data from the Vigitel survey from 2006 to 2017. The two imputation methods used in the study were hot deck and Predictive Mean Matching (PMM). The weight and height variables imputed by hot deck were provided by Vigitel. Two models were conducted with PMM: (i) explanatory variables - city, sex, age in years, race/color, and schooling; (ii) explanatory variables - city, sex, and age in years. Weight and height were the outcome variables in the two models. PMM combines linear regression and random selection of the value for imputation. Linear prediction is used as a measure of distance between the missing value and the possible donors, thereby creating the virtual space with the candidate cases for yielding the value for imputation. One of the candidates from the pool is randomly selected, and its value is assigned to the missing unit. BMI was calculated by dividing weight in kilograms by height squared. The result shows the means and standard deviations for weight, height, and BMI according to imputation method and year. The estimates used the survey module from Stata, which considers the sampling effects. The mean values for weight, height, and BMI estimated by hot deck and PMM were similar. The results with the Vigitel data suggest the applicability of PMM to the set of health surveys.

Cooperation behaviour of primary care paediatricians: facilitators and barriers to multidisciplinary obesity management

BACKGROUND

Multidisciplinary management of obesity by primary care paediatricians, providing a promising approach to tackle childhood obesity includes cooperation with other health care professionals. However, facilitators for and barriers to multidisciplinary cooperation in ambulatory care are not yet well understood and are investigated in the present study.

METHODS

A nationwide, cross-sectional survey of 83% of German primary care paediatricians was conducted, using a questionnaire based on qualitative expert interviews. Frequency of paediatricians' cooperation with external partners (i.e. nutrition counsellors; sports groups; interdisciplinary obesity centres; inpatient rehabilitation centres; and endocrinologists) was assessed. Individual and structural factors were associated with cooperation patterns. Missing values were addressed using multiple imputation.

RESULTS

Out of the 6081 primary care paediatricians approached, 2024 (33.3%) responded. Almost half of the respondents (40.8%) stated that they disengaged in the field of obesity prevention due to perceived inefficacy. Lack of financial reimbursement for consultation was agreed on by most of the respondents (90.4%). Identified barriers to cooperation included: higher proportion of patients with migration background, lack of time and available services. A more comprehensive conception of the professional role regarding overweight prevention, higher age, female gender, higher proportion of overweight/obese patients and practice location in urban or socially strained areas surfaced as facilitators for cooperation.

CONCLUSION

Low-perceived self-efficacy in obesity management and insufficient financial reimbursement for consultation are commonly stated among German paediatricians. For cooperation behaviour, however, other individual and structural factors seem to be relevant, which provide indications on how multidisciplinary childhood obesity management can be improved.

Weekend Admission to Inpatient Rehabilitation Facilities Is Associated With Transfer to Acute Care in a Nationwide Sample of Patients With Stroke

OBJECTIVE

The aim of the study was to determine the impact of weekend versus weekday admission to an inpatient rehabilitation facility on the risk of acute care transfer in patients with stroke.

DESIGN

This was a retrospective analysis using the Uniform Data System for Medical Rehabilitation, a national database comprising data from 70% of US inpatient rehabilitation facilities. A total of 1,051,436 adult (age ≥18 yrs) stroke cases were identified between 2002 and 2014 that met inclusion criteria. Logistic regression models were developed to test for associations between weekend (Friday-Sunday) versus weekday (Monday-Thursday) inpatient rehabilitation facility admission and transfer to acute care (primary outcome) and inpatient rehabilitation facility length of stay (secondary outcome), adjusting for relevant patient, medical, and facility variables. A secondary analysis examined acute care transfer from 2002 to 2009 before passage of the Affordable Care Act (ACA), 2010 to 2012 post-Affordable Care Act, and 2013 to 2014 after implementation of the Hospital Readmissions Reduction Program.

RESULTS

Weekend inpatient rehabilitation facility admission was associated with increased odds of acute care transfer (odds ratio = 1.06, 95% confidence interval = 1.04-1.08) and slightly shorter inpatient rehabilitation facility length of stay (P < 0.001). Overall, the risk of acute care transfer decreased after the ACA and Hospital Readmissions Reduction Program.

CONCLUSIONS

Weekend admission to inpatient rehabilitation facility may pose a modest increase in the risk of transfer to acute care in patients with stroke.

TO CLAIM CME CREDITS

Complete the self-assessment activity and evaluation online at http://www.physiatry.org/JournalCME CME OBJECTIVES: Upon completion of this article, the reader should be able to: (1) Understand disparities in obesity rates among adolescents with mobility disabilities; (2) Describe limitations of current clinical screening methods of obesity in children with mobility disabilities; and (3) Identify potential alternatives for obesity screening in children with mobility disabilities.

LEVEL

Advanced ACCREDITATION: The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.The Association of Academic Physiatrists designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering

Amyotrophic lateral sclerosis (ALS) is a complex progressive neurodegenerative disorder with an estimated prevalence of about 5 per 100,000 people in the United States. In this study, the ALS disease progression is measured by the change of Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) score over time. The study aims to provide clinical decision support for timely forecasting of the ALS trajectory as well as accurate and reproducible computable phenotypic clustering of participants. Patient data are extracted from DREAM-Phil Bowen ALS Prediction Prize4Life Challenge data, most of which are from the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) archive. We employed model-based and model-free machine-learning methods to predict the change of the ALSFRS score over time. Using training and testing data we quantified and compared the performance of different techniques. We also used unsupervised machine learning methods to cluster the patients into separate computable phenotypes and interpret the derived subcohorts. Direct prediction of univariate clinical outcomes based on model-based (linear models) or model-free (machine learning based techniques - random forest and Bayesian adaptive regression trees) was only moderately successful. The correlation coefficients between clinically observed changes in ALSFRS scores relative to the model-based/model-free predicted counterparts were 0.427 (random forest) and 0.545(BART). The reliability of these results were assessed using internal statistical cross validation and well as external data validation. Unsupervised clustering generated very reliable and consistent partitions of the patient cohort into four computable phenotypic subgroups. These clusters were explicated by identifying specific salient clinical features included in the PRO-ACT archive that discriminate between the derived subcohorts. There are differences between alternative analytical methods in forecasting specific clinical phenotypes. Although predicting univariate clinical outcomes may be challenging, our results suggest that modern data science strategies are useful in clustering patients and generating evidence-based ALS hypotheses about complex interactions of multivariate factors. Predicting univariate clinical outcomes using the PRO-ACT data yields only marginal accuracy (about 70%). However, unsupervised clustering of participants into sub-groups generates stable, reliable and consistent (exceeding 95%) computable phenotypes whose explication requires interpretation of multivariate sets of features. HIGHLIGHTS: • Used a large ALS data archive of 8,000 patients consisting of 3 million records, including 200 clinical features tracked over 12 months. • Employed model-based and model-free methods to predict ALSFRS changes over time, cluster patients into cohorts, and derive computable phenotypes. • Research findings include stable, reliable, and consistent (95%) patient stratification into computable phenotypes. However, clinical explication of the results requires interpretation of multivariate information. Graphical Abstract ᅟ.

Growth in early life and physical and intellectual development at school age: a cohort study

The associations between growth during early life and subsequent cognitive development and physical outcomes are not widely known in low-resource settings. We examined postnatal weight and height gain through early life and related these measurements to the nutritional status and intellectual development of the same children when they were between 7 and 9 years old. Mothers had enrolled in an randomised controlled trial to evaluate the effect of prenatal micronutrient supplementation on birth weight. Their children were born in 2004, their height and weight were measured at 6, 12, 18 and 24 months of age and were followed up between October 2012 and September 2013 (at ages 7-9 years, n 650). Height-for-age, weight-for-age and BMI-for-age were used to describe the nutritional status, and the Wechsler Intelligence Scale for Children fourth edition was used to measure the intellectual function. Multilevel linear and logistic modelling was used to estimate the association between early growth and subsequent growth and intellectual function. After adjustment, weight gain from 6 to 12 months of age was associated with Full-scale Intelligence Quotient, Verbal Comprehension Index, Working Memory Index and Perceptual Reasoning Index. Weight gain during early life was associated with subsequent nutritional status. For every 1 kg increase in weight during the 0- to 6-month period, the OR for underweight, thinness and stunting at 7-9 years of age were 0·19 (95 % CI 0·09, 0·37), 0·34 (95 % CI 0·19, 0·59) and 0·40 (95 % CI 0·19, 0·83), respectively. Weight gain during the periods of 6-12 months of age and 18-24 months of age was also associated with a lower risk of being underweight. Weight gain during early life was associated with better growth outcomes and improved intellectual development in young school-aged children.

Weight suppression uniquely predicts body fat gain in first-year female college students

Identifying predictors of increases in weight (or in fat mass) is important for understanding the genesis of obesity and for the design of prevention programs. We examined the predictive utility of 6 variables that have been predictive of weight gain in past research: depression, disinhibition, family history of overweight, body dissatisfaction, self-reported dieting and weight suppression (the difference between highest past and current weight). Percentage fat gain was evaluated with DEXA. We tested these variables as predictors of fat gain two years later in 294 female first-year students who were selected to have characteristics associated with future weight gain. Participants were categorized as weight stable or weight gainers at the two-year follow-up and logistic regression was used to evaluate the independent predictive ability of the 6 variables. Baseline body fat was entered as a covariate and predicted fat gain, as expected. The only significant predictor of the 6 tested was weight suppression, with those gaining weight showing greater weight suppression at baseline. Previous research has supported weight suppression as a robust predictor of future weight gain mostly among individuals with eating disorders. The current study indicates that weight suppression is a predictor of long-term fat gain among nonclinical female first-year students who were overwhelmingly in a healthy weight range.

Hospitals by day, dispensaries by night: Hourly fluctuations of maternal mortality within Mexican health institutions, 2010-2014

BACKGROUND

Quality of obstetric care may not be constant within clinics and hospitals. Night shifts and weekends experience understaffing and other organizational hurdles in comparison with the weekday morning shifts, and this may influence the risk of maternal deaths.

OBJECTIVE

To analyze the hourly variation of maternal mortality within Mexican health institutions.

METHODS

We performed a cross-sectional multivariate analysis of 3,908 maternal deaths and 10,589,444 births that occurred within health facilities in Mexico during the 2010-2014 period, using data from the Health Information Systems of the Mexican Ministry of Health. We fitted negative binomial regression models with covariate adjustment to all data, as well as similar models by basic cause of death and by weekdays/weekends. The outcome was the Maternal Mortality Ratio (MMR), defined as the number of deaths occurred per 100,000 live births. Hour of day was the main predictor; covariates were day of the week, c-section, marginalization, age, education, and number of pregnancies.

RESULTS

Risk rises during early morning, reaching 52.5 deaths per 100,000 live births at 6:00 (95% UI: 46.3, 62.2). This is almost twice the lowest risk, which occurred at noon (27.1 deaths per 100,000 live births [95% U.I.: 23.0, 32.0]). Risk shows peaks coinciding with shift changes, at 07:00, and 14:00 and was significantly higher on weekends and holidays.

CONCLUSIONS

Evidence suggests strong hourly fluctuations in the risk of maternal death with during early morning hours and around the afternoon shift change. These results may reflect institutional management problems that cause an uneven quality of obstetric care.

Model validation and influence diagnostics for regression models with missing covariates

Missing covariate values are prevalent in regression applications. While an array of methods have been developed for estimating parameters in regression models with missing covariate data for a variety of response types, minimal focus has been given to validation of the response model and influence diagnostics. Previous research has mainly focused on estimating residuals for observations with missing covariates using expected values, after which specialized techniques are needed to conduct proper inference. We suggest a multiple imputation strategy that allows for the use of standard methods for residual analyses on the imputed data sets or a stacked data set. We demonstrate the suggested multiple imputation method by analyzing the Sleep in Mammals data in the context of a linear regression model and the New York Social Indicators Status data with a logistic regression model.

Plasma olfactomedin 4 level in peripheral blood and its association with clinical features of breast cancer

The present study aimed to investigate the expression of olfactomedin 4 (OLFM4) in plasma of patients with breast cancer and its association with diagnosis, metastasis and prognosis of breast cancer. OLFM4 gene expression level of peripheral blood plasma in 60 patients with breast cancer and 26 healthy donors was examined by ELISA. The expression of OLFM4 in tumor tissues of patients with breast cancer was evaluated by immunohistochemistry (protein expression) and reverse transcription-quantitative polymerase chain reaction (mRNA expression), respectively. Circulating tumor cells (CTCs) were detected in a certain set of patients. The expression of OLFM4 in plasma of the overall healthy people was higher compared with patients with breast cancer. The plasma OLFM4 level in patients with breast cancer was consistent with the expression of OLFM4 protein in tumor tissues (R²=1), indicating that the level of plasma OLFM4 expression may represent the expression of OLFM4 in breast cancer tissues. The plasma OLFM4 level in patients with histological grade I was significantly lower compared with grade III (P<0.05). Breast cancer patients with positive CTC were associated with low level of plasma OLFM4. These results suggest that low OLFM4 expression in plasma or tissue specimens of breast cancer patients is more likely to represent low histological differentiation and decreased invasive/metastatic capabilities. Taken together, plasma OLFM4 level may be considered as a biomarker for diagnosis and prognosis of breast cancer for cases where there are difficulties in obtaining tumor tissue samples.

Model checking in multiple imputation: an overview and case study

Background

Multiple imputation has become very popular as a general-purpose method for handling missing data. The validity of multiple-imputation-based analyses relies on the use of an appropriate model to impute the missing values. Despite the widespread use of multiple imputation, there are few guidelines available for checking imputation models.

Analysis

In this paper, we provide an overview of currently available methods for checking imputation models. These include graphical checks and numerical summaries, as well as simulation-based methods such as posterior predictive checking. These model checking techniques are illustrated using an analysis affected by missing data from the Longitudinal Study of Australian Children.

Conclusions

As multiple imputation becomes further established as a standard approach for handling missing data, it will become increasingly important that researchers employ appropriate model checking approaches to ensure that reliable results are obtained when using this method.

Electronic supplementary material

The online version of this article (doi:10.1186/s12982-017-0062-6) contains supplementary material, which is available to authorized users.

Identifying the Rehabilitative Potential of Electronically Monitored Release Preparation: A Randomized Controlled Study in Germany

As one of many fields of application, electronic monitoring (EM) of offenders can be used-in conjunction with other measures-for release preparation. Using such measures, policymakers expect an alleviation of the negative effects of imprisonment and the promotion of positive rehabilitative effects by adding structure and social support. At the same time, policymakers are willing to maintain community safety through the close supervision provided by EM. The present study examines participants' psychological and psychosocial changes during two measures of electronically monitored release preparation, namely, home detention and early work release. These findings are compared with a randomized group of participants who remained in custody. In sum, we found no distinctive positive effects of the tested measures. Because most participants already displayed functional psychological characteristics at pretest, there was only a small margin for improvement through electronically monitored release preparation. We conclude that if rehabilitation is sought by the use of such measures, it is important to select a target group that is actually in need of rehabilitative support and equally important to conduct further research on the rehabilitative potential of EM measures.

Multiple imputation to evaluate the impact of an assay change in national surveys

National health surveys, such as the National Health and Nutrition Examination Survey, are used to monitor trends of nutritional biomarkers. These surveys try to maintain the same biomarker assay over time, but there are a variety of reasons why the assay may change. In these cases, it is important to evaluate the potential impact of a change so that any observed fluctuations in concentrations over time are not confounded by changes in the assay. To this end, a subset of stored specimens previously analyzed with the old assay is retested using the new assay. These paired data are used to estimate an adjustment equation, which is then used to 'adjust' all the old assay results and convert them into 'equivalent' units of the new assay. In this paper, we present a new way of approaching this problem using modern statistical methods designed for missing data. Using simulations, we compare the proposed multiple imputation approach with the adjustment equation approach currently in use. We also compare these approaches using real National Health and Nutrition Examination Survey data for 25-hydroxyvitamin D. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Examining Bidirectional Pathways Between Exposure to Political Violence and Adolescent Adjustment in Northern Ireland

Research on social ecologies of political violence has been largely confined to cross-sectional tests of the impact of political violence on child adjustment, limiting perspectives on more nuanced causal pathways, including tests of reciprocal relations between exposure to political violence and child adjustment. Based on a four-wave longitudinal study, this research breaks new ground in assessing bidirectional relations between exposure to political violence in the form of experience with sectarian antisocial behavior and adolescents' adjustment problems. The study included 999 mother-adolescent dyads selected from working-class neighborhoods in Belfast ranked in the bottom quartile in terms of social deprivation in Northern Ireland, with approximately 35-40 families recruited to participate from each neighborhood. Across the four annual waves of data, adolescents (52% female) were 12.18 (SD = 1.82), 13.24 (SD = 1.83), 13.62 (SD = 1.99), and 14.66 (SD = 1.96) years old. Cross-lagged path models were tested through R package lavaan with full information maximum likelihood. Reflecting a reciprocal pathway, adjustment problems related to higher reports of experience with sectarian antisocial behavior 1 year later. Boys' experience with sectarian antisocial behavior related to greater adjustment problems 1 year later, but this reciprocal path did hold for the girls. These findings offer promising directions toward better modeling of dynamic relations between exposure to political violence and adolescent adjustment over time.

Is structural stigma's effect on the mortality of sexual minorities robust? A failure to replicate the results of a published study

BACKGROUND

The study of stigma's influence on health has surged in recent years. Hatzenbuehler et al.'s (2014) study of structural stigma's effect on mortality revealed an average of 12 years' shorter life expectancy for sexual minorities who resided in communities thought to exhibit high levels of anti-gay prejudice, using data from the 1988-2002 administrations of the US General Social Survey linked to mortality outcome data in the 2008 National Death Index.

METHODS

In the original study, the key predictor variable (structural stigma) led to results suggesting the profound negative influence of structural stigma on the mortality of sexual minorities. Attempts to replicate the study, in order to explore alternative hypotheses, repeatedly failed to generate the original study's key finding on structural stigma. Efforts to discern the source of the disparity in results revealed complications in the multiple imputation process for missing values of the components of structural stigma. This prompted efforts at replication using 10 different imputation approaches.

RESULTS

Efforts to replicate Hatzenbuehler et al.'s (2014) key finding on structural stigma's notable influence on the premature mortality of sexual minorities, including a more refined imputation strategy than described in the original study, failed. No data imputation approach yielded parameters that supported the original study's conclusions. Alternative hypotheses, which originally motivated the present study, revealed little new information.

CONCLUSION

Ten different approaches to multiple imputation of missing data yielded none in which the effect of structural stigma on the mortality of sexual minorities was statistically significant. Minimally, the original study's structural stigma variable (and hence its key result) is so sensitive to subjective measurement decisions as to be rendered unreliable.

MULTIPLE IMPUTATION FOR MISSINGNESS DUE TO NONLINKAGE AND PROGRAM CHARACTERISTICS: A CASE STUDY OF THE NATIONAL HEALTH INTERVIEW SURVEY LINKED TO MEDICARE CLAIMS

Record linkage is a valuable and efficient tool for connecting information from different data sources. The National Center for Health Statistics (NCHS) has linked its population-based health surveys with administrative data, including Medicare enrollment and claims records. However, the linked NCHS-Medicare files are subject to missing data; first, not all survey participants agree to record linkage, and second, Medicare claims data are only consistently available for beneficiaries enrolled in the Fee-for-Service (FFS) program, not in Medicare Advantage (MA) plans. In this research, we examine the usefulness of multiple imputation for handling missing data in linked National Health Interview Survey (NHIS)-Medicare files. The motivating example is a study of mammography status from 1999 to 2004 among women aged 65 years and older enrolled in the FFS program. In our example, mammography screening status and FFS/MA plan type are missing for NHIS survey participants who were not linkage eligible. Mammography status is also missing for linked participants in an MA plan. We explore three imputation approaches: (i) imputing screening status first, (ii) imputing FFS/MA plan type first, (iii) and imputing the two longitudinal processes simultaneously. We conduct simulation studies to evaluate these methods and compare them using the linked NHIS-Medicare files. The imputation procedures described in our paper would also be applicable to other public health-related research using linked data files with missing data issues arising from program characteristics (e.g., intermittent enrollment or data collection) reflected in administrative data and linkage eligibility by survey participants.

Multiple imputation of completely missing repeated measures data within person from a complex sample: application to accelerometer data in the National Health and Nutrition Examination Survey

The Physical Activity Monitor component was introduced into the 2003-2004 National Health and Nutrition Examination Survey (NHANES) to collect objective information on physical activity including both movement intensity counts and ambulatory steps. Because of an error in the accelerometer device initialization process, the steps data were missing for all participants in several primary sampling units, typically a single county or group of contiguous counties, who had intensity count data from their accelerometers. To avoid potential bias and loss in efficiency in estimation and inference involving the steps data, we considered methods to accurately impute the missing values for steps collected in the 2003-2004 NHANES. The objective was to come up with an efficient imputation method that minimized model-based assumptions. We adopted a multiple imputation approach based on additive regression, bootstrapping and predictive mean matching methods. This method fits alternative conditional expectation (ace) models, which use an automated procedure to estimate optimal transformations for both the predictor and response variables. This paper describes the approaches used in this imputation and evaluates the methods by comparing the distributions of the original and the imputed data. A simulation study using the observed data is also conducted as part of the model diagnostics. Finally, some real data analyses are performed to compare the before and after imputation results. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

The use of multiple imputation method for the validation of 24-h food recalls by part-time observation of dietary intake in school

External validation of food recall over 24 h in schoolchildren is often restricted to eating events in schools and is based on direct observation as the reference method. The aim of this study was to estimate the dietary intake out of school, and consequently the bias in such research design based on only part-time validated food recall, using multiple imputation (MI) conditioned on the information on child age, sex, BMI, family income, parental education and the school attended. The previous-day, web-based questionnaire WebCAAFE, structured as six meals/snacks and thirty-two foods/beverage, was answered by a sample of 7-11-year-old Brazilian schoolchildren (n 602) from five public schools. Food/beverage intake recalled by children was compared with the records provided by trained observers during school meals. Sensitivity analysis was performed with artificial data emulating those recalled by children on WebCAAFE in order to evaluate the impact of both differential and non-differential bias. Estimated bias was within ±30 % interval for 84·4 % of the thirty-two foods/beverages evaluated in WebCAAFE, and half of the latter reached statistical significance (P<0·05). Rarely (<3 %) consumed dietary items were often under-reported (fish/seafood, vegetable soup, cheese bread, French fries), whereas some of those most frequently reported (meat, bread/biscuits, fruits) showed large overestimation. Compared with the analysis restricted to fully validated data, MI reduced differential bias in sensitivity analysis but the bias still remained large in most cases. MI provided a suitable statistical framework for part-time validation design of dietary intake over six daily eating events.

A randomized study of internet parent training accessed from community technology centers

Behavioral parent training (BPT) has been shown to be efficacious to improve parenting skills for problematic interactions with adolescents displaying oppositional and antisocial behaviors. Some research suggests that support group curricula might be transferred to the Internet, and some studies suggest that other curriculum designs might also be effective. In this research, a BPT program for parents of at-risk adolescents was tested on the Internet in a randomized trial (N = 307) from computer labs at six community technology centers in or near large metropolitan areas. The instructional design was based on asynchronous scenario-based e-learning, rather than a traditional parent training model where presentation of course material builds content sequentially over multiple class sessions. Pretest to 30-day follow-up analyses indicated significant treatment effects on parent-reported discipline style (Parenting Scale, Adolescent version), child behavior (Eyberg Child Behavior Inventory), and on social cognitive theory constructs of intentions and self-efficacy. The effect sizes were small to medium. These findings suggest the potential to provide effective parent training programs on the Internet.

Multiple imputation for harmonizing longitudinal non-commensurate measures in individual participant data meta-analysis

There are many advantages to individual participant data meta-analysis for combining data from multiple studies. These advantages include greater power to detect effects, increased sample heterogeneity, and the ability to perform more sophisticated analyses than meta-analyses that rely on published results. However, a fundamental challenge is that it is unlikely that variables of interest are measured the same way in all of the studies to be combined. We propose that this situation can be viewed as a missing data problem in which some outcomes are entirely missing within some trials and use multiple imputation to fill in missing measurements. We apply our method to five longitudinal adolescent depression trials where four studies used one depression measure and the fifth study used a different depression measure. None of the five studies contained both depression measures. We describe a multiple imputation approach for filling in missing depression measures that makes use of external calibration studies in which both depression measures were used. We discuss some practical issues in developing the imputation model including taking into account treatment group and study. We present diagnostics for checking the fit of the imputation model and investigate whether external information is appropriately incorporated into the imputed values.

Multiple imputation and analysis for high-dimensional incomplete proteomics data

Multivariable analysis of proteomics data using standard statistical models is hindered by the presence of incomplete data. We faced this issue in a nested case–control study of 135 incident cases of myocardial infarction and 135 pair‐matched controls from the Framingham Heart Study Offspring cohort. Plasma protein markers (K = 861) were measured on the case–control pairs (N = 135), and the majority of proteins had missing expression values for a subset of samples. In the setting of many more variables than observations (K ≫ N), we explored and documented the feasibility of multiple imputation approaches along with subsequent analysis of the imputed data sets. Initially, we selected proteins with complete expression data (K = 261) and randomly masked some values as the basis of simulation to tune the imputation and analysis process. We randomly shuffled proteins into several bins, performed multiple imputation within each bin, and followed up with stepwise selection using conditional logistic regression within each bin. This process was repeated hundreds of times. We determined the optimal method of multiple imputation, number of proteins per bin, and number of random shuffles using several performance statistics. We then applied this method to 544 proteins with incomplete expression data (≤40% missing values), from which we identified a panel of seven proteins that were jointly associated with myocardial infarction. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Posterior predictive checking of multiple imputation models

Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the model and the data. The aim of this study was to evaluate the performance of the posterior predictive p-value as an imputation diagnostic. Using simulation methods, we deliberately misspecified imputation models to determine whether posterior predictive p-values were effective in identifying these problems. When estimating the regression parameter of interest, we found that more extreme p-values were associated with poorer imputation model performance, although the results highlighted that traditional thresholds for classical p-values do not apply in this context. A shortcoming of the PPC method was its reduced ability to detect misspecified models with increasing amounts of missing data. Despite the limitations of posterior predictive p-values, they appear to have a valuable place in the imputer's toolkit. In addition to automated checking using p-values, we recommend imputers perform graphical checks and examine other summaries of the test quantity distribution.

The rise of multiple imputation: a review of the reporting and implementation of the method in medical research

Background

Missing data are common in medical research, which can lead to a loss in statistical power and potentially biased results if not handled appropriately. Multiple imputation (MI) is a statistical method, widely adopted in practice, for dealing with missing data. Many academic journals now emphasise the importance of reporting information regarding missing data and proposed guidelines for documenting the application of MI have been published. This review evaluated the reporting of missing data, the application of MI including the details provided regarding the imputation model, and the frequency of sensitivity analyses within the MI framework in medical research articles.

Methods

A systematic review of articles published in the Lancet and New England Journal of Medicine between January 2008 and December 2013 in which MI was implemented was carried out.

Results

We identified 103 papers that used MI, with the number of papers increasing from 11 in 2008 to 26 in 2013. Nearly half of the papers specified the proportion of complete cases or the proportion with missing data by each variable. In the majority of the articles (86%) the imputed variables were specified. Of the 38 papers (37%) that stated the method of imputation, 20 used chained equations, 8 used multivariate normal imputation, and 10 used alternative methods. Very few articles (9%) detailed how they handled non-normally distributed variables during imputation. Thirty-nine papers (38%) stated the variables included in the imputation model. Less than half of the papers (46%) reported the number of imputations, and only two papers compared the distribution of imputed and observed data. Sixty-six papers presented the results from MI as a secondary analysis. Only three articles carried out a sensitivity analysis following MI to assess departures from the missing at random assumption, with details of the sensitivity analyses only provided by one article.

Conclusions

This review outlined deficiencies in the documenting of missing data and the details provided about imputation. Furthermore, only a few articles performed sensitivity analyses following MI even though this is strongly recommended in guidelines. Authors are encouraged to follow the available guidelines and provide information on missing data and the imputation process.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-015-0022-1) contains supplementary material, which is available to authorized users.

Mobile-Web app to self-manage low back pain: randomized controlled trial

BACKGROUND

Nonspecific low back pain (NLBP) is the diagnosis for individuals with back pain that has no underlying medical cause (eg, tumor, infection, fracture, herniated disc, spinal stenosis). The American College of Physicians (ACP) and American Pain Society (APS) recommend multidisciplinary treatments for NLBP that lasts more than 4 weeks. This approach, however, is impractical for many physicians to implement, and relatively few providers offer NLBP treatment that meets the joint ACP-APS guidelines.

OBJECTIVE

This study evaluated the efficacy of a mobile-Web intervention called "FitBack" to help users implement self-tailored strategies to manage and prevent NLBP occurrences.

METHODS

A total of 597 adults were recruited, screened, consented, and assessed online at baseline, at 2 months (T2), and at 4 months (T3). After baseline assessments, participants were randomized into three groups: FitBack intervention, alternative care group that received 8 emails urging participants to link to six Internet resources for NLBP, and control group. The FitBack group also received weekly email reminder prompts for 8 weeks plus emails to do assessments. The control group was only contacted to do assessments.

RESULTS

Users of the FitBack program showed greater improvement compared to the control group in every comparison of the critical physical, behavioral, and worksite outcome measures at 4-month follow-up. In addition, users of the FitBack program performed better than the alternative care group on current back pain, behavioral, and worksite outcomes at 4-month follow-up. For example, subjects in the control group were 1.7 times more likely to report current back pain than subjects in the FitBack group; subjects in the alternative care group were 1.6 times more likely to report current back pain at 4-month follow-up. Further, the users of the FitBack program showed greater improvement compared to both the control and alternative care groups at 4-month follow-up on patient activation, constructs of the Theory of Planned Behavior, and attitudes toward pain.

CONCLUSIONS

This research demonstrated that a theoretically based stand-alone mobile-Web intervention that tailors content to users' preferences and interests can be an effective tool in self-management of low back pain. When viewed from the RE-AIM perspective (ie, reach, efficacy/effectiveness, adoption, implementation fidelity, and maintenance), this study supports the notion that there is considerable value in this type of intervention as a potentially cost-effective tool that can reach large numbers of people. The results are promising considering that the FitBack intervention was neither supported by professional caregivers nor integrated within a health promotion campaign, which might have provided additional support for participants. Still, more research is needed on how self-guided mobile-Web interventions will be used over time and to understand factors associated with continuing user engagement.

TRIAL REGISTRATION

Clinicaltrials.gov NCT01950091; http://clinicaltrials.gov/ct2/show/NCT01950091 (Archived by WebCite at http://www.webcitation.org/6TwZucX77).

Model development including interactions with multiple imputed data

Background

Multiple imputation is a reliable tool to deal with missing data and is becoming increasingly popular in biostatistics. However, building a model with interactions that are not specified a priori, in the presence of missing data, presents a challenge. On the one hand, the interactions are needed to impute the data, while on the other hand, the data is needed to identify the interactions. The objective of this study was to present a way in which this challenge can be addressed.

Methods

This paper investigates two strategies in which model development with interactions is achieved using a single data set generated from the Expectation Maximization (EM) algorithm. Imputation using both the fully conditional specification approach and the multivariate normal approach is carried out and results are compared. The strategies are illustrated with data from a study of ambient pollution and childhood asthma in Durban, South Africa.

Results

The different approaches to model building and imputation yielded similar results despite the data being mainly categorical. Both strategies investigated for building the model using the multivariate normal imputed data resulted in the identical set of variables and interactions being identified; while models built using data imputed by fully conditional specification were marginally different for the two strategies. It was found that, for both imputation approaches, model building with backward elimination applied to the initial EM data set was easier to implement, and produced good results, compared to those from a complete case analysis.

Conclusions

Developing a predictive model including interactions with data that suffers from missingness is easily done by identifying significant interactions and then applying backward elimination to a single data set imputed from the EM algorithm. It is hoped that this idea can be further developed and, by addressing this practical dilemma, there will be increased adoption of multiple imputation in medical research when data suffers from missingness.

A guide to handling missing data in cost-effectiveness analysis conducted within randomised controlled trials

Missing data are a frequent problem in cost-effectiveness analysis (CEA) within a randomised controlled trial. Inappropriate methods to handle missing data can lead to misleading results and ultimately can affect the decision of whether an intervention is good value for money. This article provides practical guidance on how to handle missing data in within-trial CEAs following a principled approach: (i) the analysis should be based on a plausible assumption for the missing data mechanism, i.e. whether the probability that data are missing is independent of or dependent on the observed and/or unobserved values; (ii) the method chosen for the base-case should fit with the assumed mechanism; and (iii) sensitivity analysis should be conducted to explore to what extent the results change with the assumption made. This approach is implemented in three stages, which are described in detail: (1) descriptive analysis to inform the assumption on the missing data mechanism; (2) how to choose between alternative methods given their underlying assumptions; and (3) methods for sensitivity analysis. The case study illustrates how to apply this approach in practice, including software code. The article concludes with recommendations for practice and suggestions for future research.

A multiple imputation strategy for sequential multiple assignment randomized trials

Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient's health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient-specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, one of the most well-known SMARTs to date.

A comparison of strategies for imputing saturated pressure array data with application to the wheelchair-seating interface

PURPOSE

The common responses to pressure sensor saturation are extreme: either discarding of data, or wholesale alteration of experimental protocol. Here, we test four simplistic strategies for restoring missing data due to sensor saturation, avoiding such drastic measures.

METHODS

We tested these algorithms on 62 pressure maps collected from 42 individuals (20 M/22 F, 54.1 ± 26.2 years, 1.7 ± 0.1 m, 71.9 ± 17.8 kg) under a variety of seating conditions. These strategies were tested via a cross-validation design, censoring the maximum pressure value in the datasets and measuring prediction error.

RESULTS

The four strategies showed various prediction error rates: ? = 0.43 ± 0.14 (simple substitution), ? = 0.16 ± 0.21 (scaled substitution), ? = 0.19 ± 0.21 (feature extraction), and ? = 0.24 ± 0.32 (extrapolation by non-linear modeling).

CONCLUSION

For single-sensor saturation, it may be possible to restore missing data using simple techniques. Implications for Rehabilitation We present a method for imputing missing data from pressure sensor arrays. The implications for rehabilitation are as follows. Improved flexibility in design of protocols concerning interfacial pressure measurement. Restoration of missing data from existing datasets. Reduction in recruitment burden for future studies. Reduction in exposure risk to study participants.

Elevated objectively measured but not self-reported energy intake predicts future weight gain in adolescents

BACKGROUND

Although obesity putatively occurs when individuals consume more calories than needed for metabolic needs, numerous risk factor studies have not observed significant positive relations between reported caloric intake and future weight gain, potentially because reported caloric intake is inaccurate.

OBJECTIVE

The present study tested the hypothesis that objectively measured habitual energy intake, estimated with doubly labeled water, would show a stronger positive relation to future weight gain than self-reported caloric intake based on a widely used food frequency measure.

DESIGN

Two hundred and fifty-three adolescents completed a doubly labeled water (DLW) assessment of energy intake (EI), a food frequency measure, and a resting metabolic rate (RMR) assessment at baseline, and had their body mass index (BMI) measured at baseline and at 1- and 2-year follow-ups.

RESULTS

Controlling for baseline RMR, elevated objectively measured EI, but not self-reported habitual caloric intake, predicted increases in BMI over a 2-year follow-up. On average, participants under-reported caloric intake by 35%.

CONCLUSIONS

RESULTS provide support for the thesis that self-reported caloric intake has not predicted future weight gain because it is less accurate than objectively measured habitual caloric intake, suggesting that food frequency measures can lead to misleading findings. However, even objectively measured caloric intake showed only a moderate relation to future weight gain, implying that habitual caloric intake fluctuates over time and that it may be necessary to conduct serial assessments of habitual intake to better reflect the time-varying effects of caloric intake on weight gain.

Effect of an Essential Oil Mixture on Skin Reactions in Women Undergoing Radiotherapy for Breast Cancer

Purpose:This pilot study compared the effects of an essential oil mixture versus standard care on skin reactions in breast cancer patients receiving radiation. Method: Using an experimental design, 24 patients were randomized to standard care (i.e., RadiaPlexRx™ ointment) or an essential oil mixture. Products were applied topically three times a day until 1 month postradiation. Weekly skin assessments were recorded and women completed patient satisfaction and quality of life (QOL) instruments at 3-, 6-, and 10-week intervals. Results: No significant differences were found for skin, QOL, or patient satisfaction at interim or follow-up time points. Effect sizes were as follows: skin = .01 to .07 (small-medium effect); QOL = .01 to .04 (small effect); patient satisfaction = .02 (small effect). Conclusion: The essential oil mixture did not provide a better skin protectant effect than standard care. These findings suggest the essential oil mixture is equivalent to RadiaPlexRx, a common product used as standard care since it has been shown to be effective in protecting skin from radiation. Thus, this pilot provides evidence to support botanical or nonpharmaceutical options for women during radiotherapy for breast cancer.