Physical health and function trajectories in adults with cancer: psychosocial predictors of class membership.

PURPOSE: To prospectively examine different trajectories of recovery, across different aspects of physical health and function and to examine trajectory class membership. METHODS: This prospective study enrolled 569 recently diagnosed adult cancer patients (Mage = 58.7) between 2019 and 2022 identified through the Rapid Case Ascertainment resource of The Yale Cancer Center. Patients were diagnosed with breast (63.8%), prostate (25.3%), or colorectal cancer (10.9%) within six-months of baseline assessment. Participants completed comprehensive psychosocial and health survey measures (SF-12) through REDCap at five time points. Growth mixture modeling examined unconditional distinct trajectories for four aspects of physical health and function. We fit logistic regression and multinomial logistic regression models to estimate associations between psychosocial predictors of trajectory class membership for each of the four aspects. RESULTS: We identified distinct trajectories of physical health and function. Over one-third (38.4%) of the sample experienced low and declining scores in their ability to accomplish work/regular daily activities due to physical health. Over half (54.9%) demonstrate moderately stable general health with no improvement over time. A small but significant subset of the sample (3%, 5.7%, 5%) was in the moderate and declining groups with sharp decline in physical function, bodily pain, and general health, after treatment. Different predictors of trajectory class membership were also found. CONCLUSIONS: Our results showed heterogeneity in physical health and function trajectories and different patterns of predictors for each aspect of physical health and function. Findings have the potential to inform screening and intervention efforts to help those who may need additional support.

Documented Penicillin Allergies on Antibiotic Selection at Pediatric Emergency Department Visits.

BACKGROUND: Penicillin or amoxicillin are the recommended treatments for the most common pediatric bacterial illnesses. Allergies to penicillin are commonly reported among children but rarely true. We evaluated the impact of reported penicillin allergies on broad-spectrum antibiotic use overall and for the treatment of common respiratory infections among treat-and-release pediatric emergency department (ED) visits. METHODS: Retrospective cohort study of pediatric patients receiving antibiotics during a treat-and-release visit at a large, pediatric ED in the northeast from 2014 to 2016. Study exposure was a reported allergy to penicillin in the electronic medical record. Study outcomes were the selection of broad-spectrum antibiotics and alternative (second-line) antibiotic therapy for the treatment of acute otitis media (AOM) and group A streptococcus (GAS) pharyngitis. We used unadjusted and adjusted generalized estimating equation models to analyze the impact of reported penicillin allergies on the selection of broad-spectrum antibiotics. We used unadjusted and adjusted logistic regression models to determine the probability of children with a documented penicillin allergy receiving alternative antibiotic treatments for AOM and GAS. RESULTS: Among 12,987 pediatric patients, 810 (6.2%) had a documented penicillin allergy. Penicillin allergies increased the odds of children receiving a broad spectrum versus narrow spectrum antibiotic (adjusted odds ratio, 13.55; 95% confidence interval (CI), 11.34-16.18). In our adjusted logistic regression model, the probability of children with a documented penicillin allergy receiving alternative antibiotic treatment for AOM was 0.97 (95% CI, 0.94-0.99) and for GAS was 0.97 (95% CI, 0.92-0.99). CONCLUSIONS: Antibiotic stewardship efforts in pediatric EDs may consider the delabeling of penicillin allergies particularly among children receiving antibiotics for an acute respiratory infection as a target for intervention.

Incomplete clustering analysis via multiple imputation.

Clustering analysis is a prevalent statistical method which divides populations into several subgroups of similar units. However, most existing clustering methods require complete data. One general method that addresses incomplete data is multiple imputation (MI) which avoids many limitations found in other single imputation-based methods and complete case analyses. Nevertheless, adopting MI framework to clustering analysis can be challenging since each imputed data might consist of a different number of clusters and there is not a unique parameter for clustering analysis. In response to this problem, we have developed MICA: Multiply Imputed Cluster Analysis. MICA is a framework for clustering incomplete data consisting of two clustering stages. We assess the properties of MICA and its superiority over other existing incomplete clustering strategies based on a simulation study under various data structures. In addition, we demonstrate the usage of MICA by applying it to the Youth Risk Behavior Surveillance System (YRBSS) 2019 data.

Modeling geostatistical incomplete spatially correlated survival data with applications to COVID-19 mortality in Ghana.

Survival models which incorporate frailties are common in time-to-event data collected over distinct spatial regions. While incomplete data are unavoidable and a common complication in statistical analysis of spatial survival research, most researchers still ignore the missing data problem. In this paper, we propose a geostatistical modeling approach for incomplete spatially correlated survival data. We achieve this by exploring missingness in outcome, covariates, and spatial locations. In the process, we analyze incomplete spatially-referenced survival data using a Weibull model for the baseline hazard function and correlated log-Gaussian frailties to model spatial correlation. We illustrate the proposed method with simulated data and an application to geo-referenced COVID-19 data from Ghana. There are several disagreements between parameter estimates and credible intervals widths obtained using our proposed approach and complete case analysis. Based on these findings, we argue that our approach provides more reliable parameter estimates and has higher predictive accuracy.

Elucidating age and sex-dependent association between frontal EEG asymmetry and depression: An application of multiple imputation in functional regression.

Frontal power asymmetry (FA), a measure of brain function derived from electroencephalography, is a potential biomarker for major depressive disorder (MDD). Though FA is functional in nature, it is typically reduced to a scalar value prior to analysis, possibly obscuring its relationship with MDD and leading to a number of studies that have provided contradictory results. To overcome this issue, we sought to fit a functional regression model to characterize the association between FA and MDD status, adjusting for age, sex, cognitive ability, and handedness using data from a large clinical study that included both MDD and healthy control (HC) subjects. Since nearly 40% of the observations are missing data on either FA or cognitive ability, we propose an extension of multiple imputation (MI) by chained equations that allows for the imputation of both scalar and functional data. We also propose an extension of Rubin's Rules for conducting valid inference in this setting. The proposed methods are evaluated in a simulation and applied to our FA data. For our FA data, a pooled analysis from the imputed data sets yielded similar results to those of the complete case analysis. We found that, among young females, HCs tended to have higher FA over the θ, α, and ß frequency bands, but that the difference between HC and MDD subjects diminishes and ultimately reverses with age. For males, HCs tended to have higher FA in the ß frequency band, regardless of age. Young male HCs had higher FA in the θ and α bands, but this difference diminishes with increasing age in the α band and ultimately reverses with increasing age in the θ band.

Rheumatoid arthritis patients treated with Janus kinase inhibitors show reduced humoral immune responses following BNT162b2 vaccination.

OBJECTIVES: The mRNA-based COVID-19 vaccines are now employed globally and have shown high efficacy in preventing SARS-CoV-2 infection. However, less is known about the vaccine efficacy in immune-suppressed individuals. This study sought to explore whether humoral immunity to the COVID-19 vaccine BNT162b2 is altered in RA patients treated with Janus kinase inhibitors by analysing their antibodies titre, neutralization activity and B cell responses. METHODS: We collected plasma samples from 12 RA patients who were treated with Janus kinase inhibitors and received two doses of the BNT162b2 vaccine, as well as 26 healthy individuals who were vaccinated with the same vaccine. We analysed the quantity of the anti-spike IgG and IgA antibodies that were elicited following the BNT162b2 vaccination, the plasma neutralization capacity and the responsiveness of the B-lymphocytes. We used ELISA to quantify the antibody titres, and a plasma neutralization assay was used to determine the virus neutralization capacity. Alteration in expression of the genes that are associated with B cell activation and the germinal centre response were analysed by quantitative PCR. RESULTS: Reduced levels of anti-spike IgG antibodies and neutralization capacity were seen in the RA patients who were treated with JAK inhibitors in comparison with healthy individuals. Furthermore, B cell responsiveness to the SARS-CoV-2 spike protein was reduced in the RA patients. CONCLUSION: RA patients who are treated with JAK inhibitors show a suppressed humoral response following BNT162b2 vaccination, as revealed by the quantity and quality of the anti-spike antibodies.

Peripheral B cells repress B-cell regeneration in aging through a TNF-α/IGFBP-1/IGF-1 immune-endocrine axis.

Loss of B lymphocyte regeneration in the bone marrow (BM) is an immunologic hallmark of advanced age, which impairs the replenishment of peripheral B-cell subsets and results in impaired humoral responses, thereby contributing to immune system dysfunction associated with aging. A better understanding of the mechanism behind this loss may suggest ways to restore immune competence and promote healthy aging. In this study, we uncover an immune-endocrine regulatory circuit that mediates cross-talk between peripheral B cells and progenitors in the BM, to balance B-cell lymphopoiesis in both human and mouse aging. We found that tumor necrosis factor α (TNF-α), which is increasingly produced by peripheral B cells during aging, stimulates the production of insulin-like growth factor-binding protein 1 (IGFBP-1), which binds and sequesters insulin-like growth factor 1 (IGF-1) in the circulation, thereby restraining its activity in promoting B-cell lymphopoiesis in the BM. Upon B-cell depletion in aging humans and mice, circulatory TNF-α decreases, resulting in increased IGF-1 and reactivation of B-cell lymphopoiesis. Perturbation of this circuit by administration of IGF-1 to old mice or anti-TNF-α antibodies to human patients restored B-cell lymphopoiesis in the BM. Thus, we suggest that in both human and mouse aging, peripheral B cells use the TNF-α/IGFBP-1/IGF-1 axis to repress B-cell lymphopoiesis. This trial was registered at www.clinicaltrials.govas#NCT00863187.

Pharmacist eConsult service for primary care medication optimization and safety.

BACKGROUND: There is a critical need in primary care to proactively prevent, identify, and resolve poor medication-related outcomes. However, more than 80% of primary care practices do not have clinical pharmacists as members of expanded care teams. The emergence of eConsult services in primary care settings presents an opportunity for primary care providers (PCPs) to consult with clinical pharmacists as "on-demand" pharmacotherapy specialists. OBJECTIVES: The objectives were to (1) determine the use of a clinical pharmacist in an existing eConsult network, (2) characterize the use and type of clinical pharmacist eConsults sent by PCPs, and (3) measure the implementation percentage of pharmacist recommendations by PCPs. METHODS: The study was conducted in a federally qualified health center using an existing eConsult platform. A clinical pharmacist was contracted to receive eConsults and was expected to respond within 2 business days. PCPs were introduced to the pharmacist eConsult service through presentations that reviewed the clinical pharmacist's education/training, suitable pharmacist eConsult topics, and workflow for sending a pharmacist eConsult. RESULTS: A total of 57 eConsults containing 123 individual questions were answered. Advanced practice nurses (APRNs) sent 3 times the number of eConsults and individual questions per eConsult compared with physicians (P < 0.0001). Most eConsult questions from APRNs related to adverse drug events/drug interactions (44%), drug or dosage changes to reach therapeutic goals (18%), and renal/hepatic dosage adjustments (13%). However, physician eConsult questions were primarily targeted on patient-specific drug or dosage adjustments (62%) and comprehensive medication regimen reviews (17%). A total of 74% of the pharmacist eConsult responses had at least 50% of the recommendations implemented by PCPs. CONCLUSION: This study revealed the use of a pharmacist eConsult service for medication-related questions in an existing eConsult network for PCPs. As more practices enroll in value-based plans, pharmacist eConsults can improve the quality and safety of prescribing and chronic medication management.

A pilot RCT of a school nurse delivered intervention to reduce student anxiety.

The goal of this study was to evaluate the feasibility and impact of brief school-nurse-administered interventions for reducing anxiety. Thirty school nurses in Connecticut and Maryland were randomly assigned to deliver the Child Anxiety Learning Modules (CALM; n = 14) or CALM-Relaxation only (CALM-R; n = 16). Students (N = 54) were ages 5-12 (M age = 8; 84.9% White; 68.5% female) with elevated anxiety symptoms and/or anxiety disorders. Feasibility was assessed based on recruitment, retention, attendance, training and intervention satisfaction, and intervention adherence. Multiple informants, including independent evaluators (IEs), completed measures of clinical improvement at postintervention and at a 3-month follow-up. Of nurses in CALM and CALM-R, 62% and 81%, respectively, enrolled a student and completed an average of 6 sessions. Youth retention was 85% and 94% in CALM and CALM-R, respectively. Training and intervention satisfaction were high. At postintervention and follow-up, youth in both groups showed significant reductions in anxiety and related symptoms and improvements in functioning. Within-group effect sizes were medium to large, and between-group effect sizes were small. Task shifting responsibility for delivering brief mental health interventions to school nurses is feasible and shows promise for reducing anxiety and related impairment. This approach may also be integrated within a response to intervention model used in schools.Public Health Significance: Brief school-nurse-administered anxiety reduction interventions were shown to be feasible and had a positive impact on student anxiety and related impairment highlighting that school nurses can be an important school resource.

Comprehensive Benefit-Risk Assessment of Noninferior Treatments Using Multicriteria Decision Analysis.

OBJECTIVES: To develop a simple approach for evaluating the overall benefit-risk of a new noninferiority treatment compared with a standard of care. METHODS: We propose using multicriteria decision analysis that accounts for uncertainty associated with both clinical outcomes and patient preference data. Because patients' preferences are likely to be influenced by their baseline characteristics, we suggest carrying out a preference study at the beginning of a trial. To reduce the burden of an additional study questionnaire, preference elicitation could be done on a small sample of trial participants. To restore preferences for all trial participants, we propose using multiple imputation (MI). Using simulations, we examine whether 3 different MI procedures lead to the same benefit-risk assessment conclusion, as if all trial participant preferences were obtained. We also compare MI results to complete case analysis, where only preferences of the small sample of trial participants are considered. RESULTS: We show that the MI procedure successfully restores patients' preferences for the trial participants using different outcome criteria and preferences. For example, using 3 outcome criteria with only 10% of the trial participants providing their preferences, complete case analysis demonstrated a new noninferior treatment as favorable only 5.1% of the time, whereas MI procedures did so between 16.2% and 17.9% of the time. Given that 17.6% correspond to the fully observed weights, the MI methods demonstrate favorable results. CONCLUSIONS: The MI procedure can help facilitate a simple comprehensive benefit-risk assessment for new noninferior treatments.

A conversation with Thomas (Tom) R. Belin- 2020 HPSS long-term excellence award winner.

At the 2020 International Conference on Health Policy Statistics held in San Diego, Thomas (Tom) R. Belin was awarded the Long-Term Excellence Award from the Health Policy Statistics Section of the American Statistical Association. Dr. Belin was exceptionally and uniquely qualified for this award. Highlights include his innovative statistical applications for health care research and his substantial contributions to the statistics and health policy communities through mentoring and service. In this interview, we asked Tom to share stories about his upbringing, schooling, and career phases to gain insights into his numerous achievements.

Incomplete data analysis of non-inferiority clinical trials: Difference between binomial proportions case.

BACKGROUND: Incomplete data analysis continues to be a major issue for non-inferiority clinical trials. Due to the steadily increasing use of non-inferiority study design, we believe this topic deserves an immediate attention. METHODS: We evaluated the performance of various strategies, including complete case analysis and various imputations techniques for handling incomplete non-inferiority clinical trials when outcome of interest is difference between binomial proportions. Non-inferiority of a new treatment was determined using a fixed margin approach with 95-95% confidence interval method. The methods used to construct the confidence intervals were compared as well and included: Wald, Farrington-Manning and Newcombe methods. RESULTS: We found that worst-case and best-case scenario imputation methods should not be used for analysis of incomplete data in non-inferiority trial design, since such methods seriously inflate type-I error rates and produce biased estimates. In addition, we report conditions under which complete case analysis is an acceptable strategy for missing at random missingness mechanism. Importantly, we show how two-stage multiple imputation could be successfully applied for incomplete data that follow missing not at random patterns, and thus result in controlled type-I error rates and unbiased estimates. CONCLUSION: This thorough simulation study provides a road map for the analysis of incomplete data in non-inferiority clinical trials for different types of missingness. We believe that the results reported in this paper could serve practitioners who encounter missing data problems in their non-inferiority clinical trials.

Multiple Imputation for Incomplete Data in Environmental Epidemiology Research.

PURPOSE OF REVIEW: Incomplete data are a common problem in statistical analysis of environmental epidemiological research. However, many researchers still ignore this complication. We evaluate the performance of two commonly used multiple imputation (MI) methods (fully conditional specification and multivariate normal) for handling missing data and compare them to complete case analysis (CCA) method. We further discuss issues that arise when these methods are being used. RECENT FINDINGS: MI is a simulation-based approach to deal with incomplete data. In general, MI will perform better then ad hoc techniques such as CCA. MI is an approach which replaces the missing data with plausible values and allows for additional uncertainty due to the missing information caused by the incomplete data. To illustrate this, we use data of 944 women from the Collaborative Perinatal Project and compare estimates between these methods. The goal is to examine if each of two outcomes, birth-weight and spontaneous abortion, in the data set are associated with mothers' smoking status during pregnancy adjusting for baseline covariates in the model. Results indicate that MI is better suited for handling incomplete data and led to a significant improvement in parameter estimates compared to CCA. The two MI methods produced similar point estimates, but slightly different standard errors.

Imputing race and ethnic information in administrative health data.

OBJECTIVE: To improve on existing methods to infer race/ethnicity in health care data through an analysis of birth records from Connecticut. DATA SOURCE: A total of 162 467 Connecticut birth records from 2009 to 2013. STUDY DESIGN: We developed a logistic model to predict race/ethnicity using data from US Census and patient-level information. Model performance was tested and compared to previous studies. Five performance measures were used for comparison. PRINCIPAL FINDINGS: Our full model correctly classifies 81 percent of subjects and shows improvement over extant methods. We achieved substantially improved sensitivity in predicting black race. CONCLUSIONS: Predictive models using Census information and patients' demographic characteristics can be used to accurately populate race/ethnicity information in health care databases, enhancing opportunities to investigate and address disparities in access to, utilization of, and outcomes of care.

Confidence Intervals for the Area Under the Receiver Operating Characteristic Curve in the Presence of Ignorable Missing Data.

Receiver operating characteristic curves are widely used as a measure of accuracy of diagnostic tests and can be summarised using the area under the receiver operating characteristic curve (AUC). Often, it is useful to construct a confidence interval for the AUC; however, because there are a number of different proposed methods to measure variance of the AUC, there are thus many different resulting methods for constructing these intervals. In this article, we compare different methods of constructing Wald-type confidence interval in the presence of missing data where the missingness mechanism is ignorable. We find that constructing confidence intervals using multiple imputation based on logistic regression gives the most robust coverage probability and the choice of confidence interval method is less important. However, when missingness rate is less severe (e.g. less than 70%), we recommend using Newcombe's Wald method for constructing confidence intervals along with multiple imputation using predictive mean matching.

The treatment of incomplete data: Reporting, analysis, reproducibility, and replicability.

Proper analysis and reporting of incomplete data continues to be a challenging task for practitioners from various research areas. Recently Nguyen, Strazdins, Nicholson and Cooklin (NSNC; 2018) evaluated the impact of complete case analysis and multiple imputation in studies of parental employment and health. Their work joins interdisciplinary efforts to educate and motivate scientists across the research community to use principled statistical methods when analyzing incomplete data. Although we fully support and encourage work in parallel to NSNC's, we also think that further actions should be taken by the research community to improve current practices. In this commentary, we discuss some aspects and misconceptions related to analysis of incomplete data, in particular multiple imputation. In our view, the missing data problem is part of a larger problem of research reproducibility and replicability today. Thus, we believe that improving analysis and reporting of incomplete data will make reproducibility and replicability efforts easier. We also provide a brief checklist of recommendations which could be used by members of the scientific community, including practitioners, journal editors, and reviewers to set higher publication standards.

The Ethics in Synthetics: Statistics in the Service of Ethics and Law in Health-Related Research in Big Data from Multiple Sources.

An ethical advancement of scientific knowledge demands a delicate equilibrium between benefits and harms, in particular in health-related research. When applying and advancing scientific knowledge or technologies, Article 4 of UNESCO's Universal Declaration on Bioethics and Human Rights, ethically justifiable research requires maximizing direct and indirect benefits and minimizing possible harms. The National Institution of Health [NIH] Data Sharing Policy and Implementation Guidance similarly states that data necessary for drawing valid conclusions and advancing medical research should be made as widely and freely available as possible (in order to share the benefits) while safeguarding the privacy of participants from potentially harmful disclosure of sensitive information. This paper discusses the challenges in the maximization of research benefit and the minimization of potential harms in the unique context of health-related research in Big Data from multiple sources, which are differently protected by the law. Part I frames the ethical dilemma by discussing potential benefits and harms, showing the constant misalignment in health-related research in Big Data from multiple sources, between the benefits in the use of confidential information for scientific purposes and the value in keeping confidentiality. Part II addresses existing regulations, including their nature and legal coverage. It highlights the prevailing challenges when combining data from multiple sources that are differently protected by the law. Part III compares different requirements for consent or authorization to use persons' health information for research. It focuses on the difficulty of existing regulation to ensure those requirements when using multiple sources of data. Part IV investigates whether exemptions from the authorization requirement could prevail in the context of information that exceeds the protection of HIPAA and the Protection of Human Subjects Regulations. In Part V the paper proposes a solution of a statistical nature, using the method of synthetic data to balance conflicting considerations. Part VI shows how the use of synthetic data can overcome some of the ethical challenges.

Multiple Imputation for Incomplete Data in Epidemiologic Studies.

Epidemiologic studies are frequently susceptible to missing information. Omitting observations with missing variables remains a common strategy in epidemiologic studies, yet this simple approach can often severely bias parameter estimates of interest if the values are not missing completely at random. Even when missingness is completely random, complete-case analysis can reduce the efficiency of estimated parameters, because large amounts of available data are simply tossed out with the incomplete observations. Alternative methods for mitigating the influence of missing information, such as multiple imputation, are becoming an increasing popular strategy in order to retain all available information, reduce potential bias, and improve efficiency in parameter estimation. In this paper, we describe the theoretical underpinnings of multiple imputation, and we illustrate application of this method as part of a collaborative challenge to assess the performance of various techniques for dealing with missing data (Am J Epidemiol. 2018;187(3):568-575). We detail the steps necessary to perform multiple imputation on a subset of data from the Collaborative Perinatal Project (1959-1974), where the goal is to estimate the odds of spontaneous abortion associated with smoking during pregnancy.

Inverse-Probability-Weighted Estimation for Monotone and Nonmonotone Missing Data.

Missing data is a common occurrence in epidemiologic research. In this paper, 3 data sets with induced missing values from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are provided as examples of prototypical epidemiologic studies with missing data. Our goal was to estimate the association of maternal smoking behavior with spontaneous abortion while adjusting for numerous confounders. At the same time, we did not necessarily wish to evaluate the joint distribution among potentially unobserved covariates, which is seldom the subject of substantive scientific interest. The inverse probability weighting (IPW) approach preserves the semiparametric structure of the underlying model of substantive interest and clearly separates the model of substantive interest from the model used to account for the missing data. However, IPW often will not result in valid inference if the missing-data pattern is nonmonotone, even if the data are missing at random. We describe a recently proposed approach to modeling nonmonotone missing-data mechanisms under missingness at random to use in constructing the weights in IPW complete-case estimation, and we illustrate the approach using 3 data sets described in a companion article (Am J Epidemiol. 2018;187(3):568-575).