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The growing number of available treatment options have led to urgent needs for reliable answers when choosing the best course of treatment for a patient. As it is often infeasible to compare a large number of treatments in a single randomized controlled trial, multivariate network meta-analyses (NMAs) are used to synthesize evidence from existing trials of a subset of the available treatments, where outcomes related to both efficacy and safety are considered simultaneously. However, these large-scale multiple-outcome NMAs have created challenges to existing methods due to the increasingly complexity of the unknown correlation structures between different outcomes and treatment comparisons. In this paper, we proposed a new framework for PAtient-centered treatment ranking via Large-scale Multivariate network meta-analysis, termed as PALM, which includes a parsimonious modeling approach, a fast algorithm for parameter estimation and inference, a novel visualization tool for comparing treatments with multivariate outcomes termed as the star plot, as well as personalized treatment ranking procedures taking into account the individual’s considerations on multiple outcomes. In application to an NMA that compares 14 treatment options for labor induction over five modalities, we provided a comprehensive illustration of the proposed framework and demonstrated its computational efficiency and practicality. Our analysis leads to new insights on comparing these 14 treatment options based on joint inference of multiple outcomes that cannot be obtained from univariate NMAs, and novel visualizations of evidence to support patient-centered clinical decision making.

Diarrheal disease accounts for more than one million deaths annually in patients over 5 years of age. Although most patients can be managed with oral rehydration solution, patients with severe dehydration require resuscitation with intravenous fluids. Scoring systems to assess dehydration have been empirically derived and validated in children under 5 years, but none have been validated for patients over 5 years. In this study, a prospective cohort of 2,172 patients over 5 years presenting with acute diarrhea to International Centre for Diarrhoeal Disease Research, Dhaka Hospital, Bangladesh, were assessed for clinical signs of dehydration. The percent difference between presentation and posthydration stable weight determined severe (≥ 9%), some (3–9%), or no (

The significance and influence of US Supreme Court majority opinions derive in large part from opinions' roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology -- the citation exponential random graph model, for which we provide user-friendly software -- enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of exogenous covariates, indicating that models of Supreme Court citation should incorporate both the effects of case characteristics and the structure of past citations., Comment: 34 pages, 10 figures

Background Episodes of acute diarrhea lead to dehydration, and existing care algorithms base treatment around categorical estimates for fluid resuscitation. This study aims to develop models for the percentage dehydration (fluid deficit) in individuals with acute diarrhea, to better target treatment and avoid the potential sequelae of over or under resuscitation. Methods This study utilizes data from two prospective cohort studies of patients with acute diarrhea in Dhaka, Bangladesh. Data were collected on patient arrival, including weight, clinical signs and symptoms, and demographic information. Consecutive weights were obtained to determine the true volume deficit of each patient. Data were entered into two distinct forward stepwise regression logistic models (DHAKA for under 5 years and NIRUDAK for 5 years and over). Results A total of 782 patients were included in the final analysis of the DHAKA data set, and 2139 were included in the final analysis of the NIRUDAK data set. The best model for the DHAKA data achieved an R2 of 0.27 and a root mean square error (RMSE) of 3.7 (compared to R2 of 0.06 and RMSE of 5.5 with the World Health Organization child care algorithm) and selected 6 predictors. The best performance model for the NIRUDAK data achieved an R2 of 0.28 and a RMSE of 2.6 (compared to R2 of 0.08 and RMSE of 4.3 with the World Health Organization adolescent/adult care algorithm) and selected 7 predictors with 2 interactions. Conclusions These are the first mathematical models for patients with acute diarrhea that allow for the calculation of a patient’s percentage dehydration (fluid deficit) and subsequent targeted treatment with fluid resuscitation. These findings are an improvement on existing World Health Organization care algorithms.

Objectives Few studies have evaluated determinants of multidrug-resistant (MDR) Vibrio cholerae O1 in older children and adults. This study aimed to characterize the prevalence of MDR V. cholerae O1 and associated risk factors among patients over five years of age in Bangladesh. Methods Stool culture and antimicrobial susceptibility testing were performed as a part of a larger study at Dhaka Hospital in Bangladesh from March 2019–March 2020. Univariate statistics and multiple logistic regression were used to assess the association between a range of variables and MDR V. cholerae O1. Results MDR was found in 175 of 623 (28.1%) V. cholerae O1 isolates. High levels of resistance were found to erythromycin (99.2%), trimethoprim-sulfamethoxazole (99.7%), and ampicillin (88.9%), while susceptibility was high to tetracyclines (99.7%), azithromycin (99.2%), ciprofloxacin (99.8%), and cephalosporins (98.6%). MDR was associated with prior antibiotic use, longer transport time to hospital, higher income, non-flush toilet use, greater stool frequency, lower blood pressure, lower mid-upper arm circumference, and lower percent dehydration. Conclusions MDR V. cholerae O1 was common among patients over five in an urban hospital in Bangladesh. Significant factors associated with MDR may be actionable in identifying patients with a high likelihood of MDR.

The pooled estimate of the average effect is of primary interest when fitting the random-effects model for meta-analysis. However estimates of study specific effects, for example those displayed on forest plots, are also often of interest. In this tutorial, we present the case, with the accompanying statistical theory, for estimating the study specific true effects using so called 'empirical Bayes estimates' or 'Best Unbiased Linear Predictions' under the random-effects model. These estimates can be accompanied by prediction intervals that indicate a plausible range of study specific true effects. We coalesce and elucidate the available literature and illustrate the methodology using two published meta-analyses as examples. We also perform a simulation study that reveals that coverage probability of study specific prediction intervals are substantially too low if the between-study variance is small but not negligible. Researchers need to be aware of this defect when interpreting prediction intervals. We also show how empirical Bayes estimates, accompanied with study specific prediction intervals, can embellish forest plots. We hope that this tutorial will serve to provide a clear theoretical underpinning for this methodology and encourage its widespread adoption.

Here we describe methods for assessing heterogeneity of treatment effects over prespecified subgroups in observational studies, using outcome-model–based (g-formula), inverse probability weighting, doubly robust, and matching estimators of subgroup-specific potential outcome means, conditional average treatment effects, and measures of heterogeneity of treatment effects. We compare the finite-sample performance of different estimators in simulation studies where we vary the total sample size, the relative frequency of each subgroup, the magnitude of treatment effect in each subgroup, and the distribution of baseline covariates, for both continuous and binary outcomes. We find that the estimators’ bias and variance vary substantially in finite samples, even when there is no unobserved confounding and no model misspecification. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study (August 1975–December 1996) to compare the effect of surgery plus medical therapy with that of medical therapy alone for chronic coronary artery disease in subgroups defined by previous myocardial infarction or left ventricular ejection fraction.

Evidence about specific carbohydrate diet (SCD) for inflammatory bowel disease (IBD) is limited. We conducted 54 single-subject, double-crossover N-of-1 trials comparing SCD with a modified SCD (MSCD) and comparing each with the participant's baseline, usual diet (UD).Across 19 sites, we recruited patients aged 7-18 years with IBD and active inflammation. Following a 2-week baseline (UD), patients were randomized to 1 of 2 sequences of 4 alternating 8-week SCD and MSCD periods. Outcomes included fecal calprotectin and patient-reported symptoms. We report posterior probabilities from Bayesian models comparing diets.Twenty-one (39%) participants completed the trial, 9 (17%) completed a single crossover, and 24 (44%) withdrew. Withdrawal or early completion occurred commonly (lack of response [n = 11], adverse events [n = 11], and not desiring to continue [n = 6]). SCD and MSCD performed similarly for most individuals. On average, there was1% probability of a clinically meaningful difference in IBD symptoms between SCD and MSCD. The average treatment difference was -0.3 (95% credible interval -1.2, 0.75). There was no significant difference in the ratio of fecal calprotectin geometric means comparing SCD and MSCD (0.77, 95% credible interval 0.51, 1.10). Some individuals had improvement in symptoms and fecal calprotectin compared with their UD, whereas others did not.SCD and MSCD did not consistently improve symptoms or inflammation, although some individuals may have benefited. However, there are inherent difficulties in examining dietary changes that complicate study design and ultimately conclusions regarding effectiveness.

In 2016, diarrheal disease was the eighth leading cause of mortality globally accounting for over 1.6 million deaths with the majority of deaths in adults and children over 5 years. This study aims to investigate the clinical, sociodemographic, and environmental risk factors associated with common bacterial acute diarrhea among adults and children over 5. Data were collected from March 2019 to March 2020 in patients over 5 years presenting with acute gastroenteritis at icddr,b. Stool samples were collected from each patient for culture and polymerase chain reaction (PCR) testing. Bivariate associations between independent variables and stool-testing indicating bacterial etiology were calculated. This analysis included 2,133 diarrheal patients of whom a bacterial enteropathogen was identified in 1,537 (72%). Detection of bacteria was associated with: younger age (OR 0.92; 95% CI: 0.88–0.96), lower mean arterial pressure (OR 0.84; 95% CI: 0.79–0.89), heart rate (OR 1.06; 95% CI: 1.01–1.10), percentage dehydration (OR 1.33; 95% CI: 1.13–1.55), respiration rate (OR 1.23; 95% CI: 1.04–1.46), lower mid-upper arm circumference (OR 0.97; 95% CI: 0.94–0.99), confused/lethargic mental status (OR 1.85; 95% CI: 1.11–3.25), rice watery stool (OR 1.92; 95% CI: 1.54–2.41), and vomiting more than three times in the past 24 hours (OR 1.30; 95% CI: 1.06–1.58). Higher monthly income (OR 0.92; 95% CI: 0.86–0.98), > 8 years of education (OR 0.79; 95% CI: 0.63–1.00), and having more than five people living at home (OR 0.80; 95% CI: 0.66–0.98) were associated with lower odds of bacterial diarrhea. These findings may help guide the development of predictive tools to aid in identifying patients with bacterial diarrhea for timely and appropriate use of antibiotics.

Objectives Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful. Study design and setting We use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes. Results Four characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R2 of 50%). Applied examples are used to illustrate the use of the BoS prediction model. Conclusions Cochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required.

BACKGROUND The availability of mobile clinical decision support (CDS) tools has grown substantially with the increased prevalence of smartphone devices and apps. Although health care providers express interest in integrating mobile health (mHealth) technologies into their clinical settings, concerns have been raised, including perceived disagreements between information provided by mobile CDS tools and standard guidelines. Despite their potential to transform health care delivery, there remains limited literature on the provider’s perspective on the clinical utility of mobile CDS tools for improving patient outcomes, especially in low- and middle-income countries. OBJECTIVE This study aims to describe providers’ perceptions about the utility of a mobile CDS tool accessed via a smartphone app for diarrhea management in Bangladesh. In addition, feedback was collected on the preliminary components of the mobile CDS tool to address clinicians’ concerns and incorporate their preferences. METHODS From November to December 2020, qualitative data were gathered through 8 web-based focus group discussions with physicians and nurses from 3 Bangladeshi hospitals. Each discussion was conducted in the local language—Bangla—and audio recorded for transcription and translation by the local research team. Transcripts and codes were entered into NVivo (version 12; QSR International), and applied thematic analysis was used to identify themes that explore the clinical utility of an mHealth app for assessing dehydration severity in patients with acute diarrhea. Summaries of concepts and themes were generated from reviews of the aggregated coded data; thematic memos were written and used for the final analysis. RESULTS Of the 27 focus group participants, 14 (52%) were nurses and 13 (48%) were physicians; 15 (56%) worked at a diarrhea specialty hospital and 12 (44%) worked in government district or subdistrict hospitals. Participants’ experience in their current position ranged from 2 to 14 years, with an average of 10.3 (SD 9.0) years. Key themes from the qualitative data analysis included current experience with CDS, overall perception of the app’s utility and its potential role in clinical care, barriers to and facilitators of app use, considerations of overtreatment and undertreatment, and guidelines for the app’s clinical recommendations. Participants felt that the tool would initially take time to use, but once learned, it could be useful during epidemic cholera. Some felt that clinical experience remains an important part of treatment that can be supplemented, but not replaced, by a CDS tool. In addition, diagnostic information, including mid-upper arm circumference and blood pressure, might not be available to directly inform programming decisions. CONCLUSIONS Participants were positive about the mHealth app and its potential to inform diarrhea management. They provided detailed feedback, which developers used to revise the mobile CDS tool. These formative qualitative data provided timely and relevant feedback to improve the utility of a CDS tool for diarrhea treatment in Bangladesh. CLINICALTRIAL

Background The availability of mobile clinical decision support (CDS) tools has grown substantially with the increased prevalence of smartphone devices and apps. Although health care providers express interest in integrating mobile health (mHealth) technologies into their clinical settings, concerns have been raised, including perceived disagreements between information provided by mobile CDS tools and standard guidelines. Despite their potential to transform health care delivery, there remains limited literature on the provider’s perspective on the clinical utility of mobile CDS tools for improving patient outcomes, especially in low- and middle-income countries. Objective This study aims to describe providers’ perceptions about the utility of a mobile CDS tool accessed via a smartphone app for diarrhea management in Bangladesh. In addition, feedback was collected on the preliminary components of the mobile CDS tool to address clinicians’ concerns and incorporate their preferences. Methods From November to December 2020, qualitative data were gathered through 8 web-based focus group discussions with physicians and nurses from 3 Bangladeshi hospitals. Each discussion was conducted in the local language—Bangla—and audio recorded for transcription and translation by the local research team. Transcripts and codes were entered into NVivo (version 12; QSR International), and applied thematic analysis was used to identify themes that explore the clinical utility of an mHealth app for assessing dehydration severity in patients with acute diarrhea. Summaries of concepts and themes were generated from reviews of the aggregated coded data; thematic memos were written and used for the final analysis. Results Of the 27 focus group participants, 14 (52%) were nurses and 13 (48%) were physicians; 15 (56%) worked at a diarrhea specialty hospital and 12 (44%) worked in government district or subdistrict hospitals. Participants’ experience in their current position ranged from 2 to 14 years, with an average of 10.3 (SD 9.0) years. Key themes from the qualitative data analysis included current experience with CDS, overall perception of the app’s utility and its potential role in clinical care, barriers to and facilitators of app use, considerations of overtreatment and undertreatment, and guidelines for the app’s clinical recommendations. Participants felt that the tool would initially take time to use, but once learned, it could be useful during epidemic cholera. Some felt that clinical experience remains an important part of treatment that can be supplemented, but not replaced, by a CDS tool. In addition, diagnostic information, including mid-upper arm circumference and blood pressure, might not be available to directly inform programming decisions. Conclusions Participants were positive about the mHealth app and its potential to inform diarrhea management. They provided detailed feedback, which developers used to revise the mobile CDS tool. These formative qualitative data provided timely and relevant feedback to improve the utility of a CDS tool for diarrhea treatment in Bangladesh.

Black and Hispanic individuals in the US experience more socioeconomic adversities that are associated with disparities in tobacco use and cessation than White individuals. This study examined if racial/ethnic differences in smoking abstinence were mediated by socioeconomic (SES) adversities.Data from 7,101 established smokers were identified in Wave 1 (2013-2014) of the Population Assessment of Tobacco and Health (PATH) and followed to Wave 4 (2016-2018). The study outcome was cigarette abstinence at Wave 4. The main independent variable was race/ethnicity (Non-Hispanic White [White], Non-Hispanic Black [Black] and Hispanic). The mediators were five measures of SES adversities (unemployment, poverty, difficulty with money, lower education level, lack of health insurance). A weighted Generalized Structural Equation Model (GSEM) was used to estimate the total, direct, and indirect effect of race/ethnicity on the odds of quitting mediated by the five SES adversities. This model was adjusted by study covariates, including health and smoking characteristics.The indirect effect of race/ethnicity on cessation showed that differences in quitting between Black and White individuals as well as Hispanic and White individuals were mediated by SES adversities. However, the differences in quitting between Hispanic and Black individuals were not mediated by SES adversities. Black and Hispanic individuals were less likely to quit than White individuals, but Hispanic individuals were more likely to quit than Black individuals. There were no direct effects between Black or Hispanic individuals compared to White individuals. Those with higher SES were more likely to quit compared to those with lower SES.Smoking abstinence is higher in White individuals compared to Black and Hispanic individuals and is mediated by SES adversities. However, smoking abstinence is higher among Hispanic individuals compared to Black individuals and it is not mediated by SES adversities. Future studies should consider the role of other factors, such as psychosocial support, racism, discrimination, and stress over the life course in explaining differences in smoking abstinence between Black and Hispanic individuals.

INTRODUCTION: E-cigarette (e-cig) use is widespread and may play an important role in facilitating smoking reduction. Racial/ethnic minorities are less likely than Whites to use e-cigs and suffer disproportionate tobacco-related disease, making them a priority for harm reduction. This paper explores factors associated with smoking reduction among African American (AA) and Latinx smokers enrolled in a trial assessing toxicant exposure in those assigned to e-cigs or smoking as usual. METHODS: Participants were randomized to receive 6 weeks of JUUL e-cigs or continue smoking cigarettes as usual (N=187). This analysis focuses on 109 participants randomized to e-cigs. We modeled cigarettes smoked in the past week at baseline and week 6 as a function of a priori selected predictors (number of JUUL pods used throughout the study, baseline cigarette dependence, and baseline cotinine) using a Poisson model fit with generalized estimating equations. RESULTS: Over the six-week study, cigarette smoking decreased from an average of 82.4 to 15.5 cigarettes per week. Greater numbers of JUUL pods used predicted a greater smoking reduction by week 6 (IRR = 0.94 [0.91, 0.96], p < 0.001). Higher baseline cigarette dependence (IRR = 1.03 [1.01, 1.05], p = 0.004), and baseline cotinine (IRR = 1.18 [1.03, 1.37], p = 0.020) predicted a lesser smoking reduction. CONCLUSIONS: AA and Latinx smokers reduced their cigarette consumption while using JUUL e-cigs. Higher e-cig use during an intervention to switch to e-cigs to reduce harm may facilitate a transition to smoking fewer cigarettes, offering an opportunity to narrow smoking-related health disparities.

Objectives We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites. Materials and Methods ODAL effectively utilizes the information from the local site (where the patient-level data are accessible) and incorporates the first-order (ODAL1) and second-order (ODAL2) gradients of the likelihood function from other sites to construct an estimator without requiring iterative communication across sites or transferring patient-level data. We evaluated ODAL via extensive simulation studies and an application to a dataset from the University of Pennsylvania Health System. The estimation accuracy was evaluated by comparing it with the estimator based on the combined individual participant data or pooled data (ie, gold standard). Results Our simulation studies revealed that the relative estimation bias of ODAL1 compared with the pooled estimates was Conclusions This study demonstrates that ODAL is privacy-preserving and communication-efficient with small bias and high statistical efficiency.

OBJECTIVES: Opioids and non-steroidal anti-inflammatory drugs (NSAIDs) are frequently prescribed for chronic musculoskeletal pain, despite limited evidence of effectiveness and well-documented adverse effects. We assessed the effects of participating in a structured, personalized self-experiment (“N-of-1 trial”) on analgesic prescribing in patients with chronic musculoskeletal pain. METHODS: We randomized 215 patients with chronic pain to participate in an N-of-1 trial facilitated by a mobile health app or to receive usual care. Medical records of participating patients were reviewed at enrollment and 6 months later to assess analgesic prescribing. We established thresholds of ≥ 50, ≥ 20, and > 0 morphine milligram equivalents (MMEs) per day to capture patients taking relatively high doses only, patients taking low-moderate as well as relatively high doses, and patients taking any dose of opioids, respectively. RESULTS: There was no significant difference between the N-of-1 and control groups in the percentage of patients prescribed any opioids (relative odds ratio (ROR) = 1.05; 95% confidence interval [CI] = 0.61 to 1.80, p = 0.87). There was a clinically substantial but statistically not significant reduction of the percentage of patients receiving ≥ 20 MME (ROR = 0.58; 95% CI = 0.33 to 1.04, p = 0.07) and also in the percentage receiving ≥ 50 MME (ROR = 0.50; 95% CI = 0.19 to 1.34, p = 0.17). There was a significant reduction in the proportion of patients in the N-of-1 group prescribed NSAIDs compared with control (relative odds ratio = 0.53; 95% CI = 0.29 to 0.96, p = 0.04), with no concomitant increase in average pain intensity. There was no significant change in use of adjunctive medications (acetaminophen, gabapentenoids, or topicals). DISCUSSION: These exploratory results suggest that participation in N-of-1 trials may reduce long-term use of NSAIDs; there is also a weak signal for an effect on use of opioids. Additional research is needed to confirm these results and elucidate possible mechanisms. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02116621. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-019-05303-0) contains supplementary material, which is available to authorized users.

Personalized nutrition (PN) approaches have been shown to help drive behavior change and positively influence health outcomes. This has led to an increase in the development of commercially available PN programs, which utilize various forms of individual-level information to provide services and products for consumers. The lack of a well-accepted definition of PN or an established set of guiding principles for the implementation of PN creates barriers for establishing credibility and efficacy. To address these points, the North American Branch of the International Life Sciences Institute convened a multidisciplinary panel. In this article, a definition for PN is proposed: "Personalized nutrition uses individual-specific information, founded in evidence-based science, to promote dietary behavior change that may result in measurable health benefits." In addition, 10 guiding principles for PN approaches are proposed: 1) define potential users and beneficiaries; 2) use validated diagnostic methods and measures; 3) maintain data quality and relevance; 4) derive data-driven recommendations from validated models and algorithms; 5) design PN studies around validated individual health or function needs and outcomes; 6) provide rigorous scientific evidence for an effect on health or function; 7) deliver user-friendly tools; 8) for healthy individuals, align with population-based recommendations; 9) communicate transparently about potential effects; and 10) protect individual data privacy and act responsibly. These principles are intended to establish a basis for responsible approaches to the evidence-based research and practice of PN and serve as an invitation for further public dialog. Several challenges were identified for PN to continue gaining acceptance, including defining the health–disease continuum, identification of biomarkers, changing regulatory landscapes, accessibility, and measuring success. Although PN approaches hold promise for public health in the future, further research is needed on the accuracy of dietary intake measurement, utilization and standardization of systems approaches, and application and communication of evidence.

Reporting guidelines, such as the consolidated standards of reporting trials (CONSORT)statement, improve the reporting of research in the medical literature (Turner et al., 2012). Many such guidelines exist and the CONSORT extension to non-pharmacological trials (Boutron et al., 2008)provides suitable guidance for reporting betweengroups intervention studies in the behavioral sciences. The CONSORT extension for N-of-1 Trials (CENT 2015)was developed for multiple crossover trials with single individuals in the medical sciences (Shamseer et al., 2015; Vohra et al., 2015), but there is no reporting guideline in the CONSORT tradition for single-case research used in the behavioral sciences. We developed the single-case reporting guideline in behavioural interventions (SCRIBE)2016 to meet this need. This statement article describes the methodology of the development of the SCRIBE 2016, along with the outcome of 2 Delphi surveys and a consensus meeting of experts. We present the resulting 26-item SCRIBE 2016 checklist. The article complements the more detailed SCRIBE 2016 explanation and elaboration article (Tate et al., 2016)that provides a rationale for each of the items and examples of adequate reporting from the literature. Both these resources will assist authors to prepare reports of single-case research with clarity, completeness, accuracy, and transparency. They will also provide journal reviewers and editors with a practical checklist against which such reports may be critically evaluated.We developed a reporting guideline to provide authors with guidance about what should be reported when writing a paper for publication in a scientific journal using a particular type of research design: the single-case experimental design. This report describes the methods used to develop the single-case reporting guideline in behavioural interventions (SCRIBE)2016. As a result of 2 online surveys and a 2-day meeting of experts, the SCRIBE 2016 checklist was developed, which is a set of 26 items that authors need to address when writing about single-case research. This article complements the more detailed SCRIBE 2016 explanation and elaboration article (Tate et al., 2016)that provides a rationale for each of the items and examples of adequate reporting from the literature. Both these resources will assist authors to prepare reports of single-case research with clarity, completeness, accuracy, and transparency. They will also provide journal reviewers and editors with a practical checklist against which such reports may be critically evaluated. We recommend that the SCRIBE 2016 is used by authors preparing manuscripts describing single-case research for publication, as well as journal reviewers and editors who are evaluating such manuscripts.Nous avons elabore des normes de presentation (sorte de lignes directrices)servant de guide pratique pour la redaction d'un article a publier dans une revue scientifique, utilisantun design de recherche particulier: le protocole experimental a cas unique. Le present article decrit la procedure utilisee afin d’elaborer ces normes: « single-case reporting guideline in behavioural interventions » (SCRIBE-2016). A l'issue de deux enquetes en ligne et d'une conference de consensus reunissant des experts durant deux jours, une liste de conformite (check-list)SCRIBE-2016 comprenant 26 items que les auteurs devraient considerer lorsqu'ils redigent des travaux utilisant les protocoles a cas unique a ete etablie. Cet article complete ainsi l'article intitule « SCRIBE-2016 explication and elaboration » (Tate et al., 2016)qui detaille et explique chaque item de la liste tout en fournissant, en guise de modele, des illustrations pratiques tires de travaux exemplaires de la litterature. Ces deux ressources aideront les auteurs a rediger avec clarte, exhaustivite, exactitude et transparence des articles de recherche portant sur des cas uniques. Elles fourniront egalement aux experts des revues ainsi qu’a leurs redacteurs en chef une liste de verification pratique qui leur servira de grille de lecture critique de ces articles. Nous recommandons de fait que le SCRIBE-2016 soit utilise aussi bien par les auteurs qui envisagent de publier des manuscrits rendant compte d'une recherche a cas unique, que par les experts et editeurs de revues qui les evaluent.Les normes en matiere de redaction d'articles, a l'instar de celles dediees a l’ecriture de travaux d'essais randomises controles (CONSORT), ameliorent la qualite des comptes rendus de la recherche dans la litterature medicale (Turner et al., 2012). Il existe de nombreuses normes de ce genre parmi lesquelles l'extension du CONSORT pour les essais non-pharmacologiques (Boutron et al., 2008)qui fournit deja un cadre approprie pour rendre compte d’etudes experimentales intergroupes en science du comportement. Une extension du CONSORT pour les essais « N = 1 » (CENT 2015)a egalement bien ete developpee en sciences medicales pour les essais transversaux multiples realises aupres de cas uniques (Shamseer et al., 2015; Vohra et al., 2015). Toutefois il n'existait pas, jusqu’a lors, de normes de presentation comparables au CONSORTet applicables aux protocoles a cas uniques en sciences du comportement. Pour repondre a ce besoin, nous avons mis au point ces « normes de presentation d'une recherche interventionnelle utilisant un protocole a cas unique » (SCRIBE-2016). Cet article decrit la procedure d’elaboration du SCRIBE-2016, ainsi que les resultats des deux enquetes Delphi et de la conference de consensus d'experts realises dans ce contexte. Nous y presentonsles normes SCRIBE-2016 en 26 items qui en resultent. La presente declaration est un preambule a l'article plus detaille intitule « SCRIBE-2016 explanation and elaboration » (Tate et al., 2016), qui detaille et apporte un eclaircissement pour chacun des items des normes de presentation et les illustre par des exemples de travaux adequats tires de la litterature. Ces deux ressources aideront les auteurs a rediger avec clarte, exhaustivite, exactitude et transparence des travaux de recherche portant sur des cas uniques. Elles fourniront egalement aux experts des revues ainsi qu'aux redacteurs en chef une liste de verification pratique qui leur servira de grille de lecture critique de ces travaux.

Objective To evaluate diagnostic tests, analysts use meta-analyses to provide inputs to parameters in decision models. Choosing parameter estimands from meta-analyses requires understanding the meta-analytic and decision-making contexts. Study Design and Setting We expand on an analysis comparing positron emission tomography (PET), PET with computed tomography (PET/CT), and conventional workup (CW) in women with suspected recurrent breast cancer. We discuss Bayesian meta-analytic summaries (posterior mean over a set of existing studies, posterior estimate in an existing study, posterior predictive mean in a new study) used to estimate diagnostic test parameters (prevalence, sensitivity, specificity) needed to calculate quality-adjusted life years in a decision model contextualizing PET, PET/CT, and CW. Results The mean and predictive mean give similar estimates, but the latter displays greater uncertainty. Namely, PET/CT outperforms CW on average but may not do better than CW when implemented in future settings. Conclusion Selecting estimands for decision model parameters from meta-analyses requires understanding the relationship between decision settings and meta-analysis studies' settings, specifically whether the former resemble one or all study settings or represents new settings. We provide an algorithm recommending appropriate estimands as input parameters in decision models for diagnostic tests to obtain output parameters consistent with the decision context.

Clinicians and patients often try a treatment for an initial period to inform longer-term therapeutic decisions. A more rigorous approach involves N-of-1 trials. In these single-patient crossover trials, typically conducted in patients with chronic conditions, individual patients are given candidate treatments in a double-blinded, random sequence of alternating periods to determine the most effective treatment for that patient. However, to date, these trials are rarely done outside of research settings and have not been integrated into general care where they could offer substantial benefit. Designating this classical, N-of-1 trial design as Type 1, there also are new and evolving uses of N-of-1 trials that we designate as Type 2. In these, rather than focusing on optimizing treatment for chronic diseases when multiple approved choices are available, as is typical of Type 1, a Type 2 N-of-1 trial tests treatments designed specifically for a patient with a rare diseases, to facilitate personalized medicine. While the aims differ, both types face the challenge of collecting individual-patient evidence using standard, trusted, widely accepted methods. To fulfill their potential for producing both clinical and research benefits, and to be available for wide use, N-of-1 trials will have to fit into the current healthcare ecosystem. This will require generalizable and accepted processes, platforms, methods, and standards. This also will require sustainable value-based arrangements among key stakeholders. In this article, we review opportunities, stakeholders, issues, and possible approaches that could support general use of N-of-1 trials and deliver benefit to patients and the healthcare enterprise. To assess and expand the benefits of N-of-1 trials, we propose multi-stakeholder meetings, workshops, and the generation of methods, standards, and platforms that would support wider availability and the value of N-of-1 trials.

Background Antimicrobial resistance (AMR) is a global public health threat and is increasingly prevalent among enteric pathogens in low- and middle-income countries (LMICs). However, the burden of multidrug-resistant organisms (MDROs) in older children, adults, and elderly patients with acute diarrhea in LMICs is poorly understood. This study’s aim was to characterize the prevalence of MDR enteric pathogens isolated from patients with acute diarrhea in Dhaka, Bangladesh, and assess a wide range of risk factors associated with MDR. Methods This study was a secondary analysis of data collected from children over 5 years, adults, and elderly patients with acute diarrhea at the International Centre for Diarrhoeal Disease Research, Bangladesh Dhaka Hospital between March 2019 and March 2020. Clinical, historical, socio-environmental information, and a stool sample for culture and antimicrobial susceptibility testing were collected from each patient. Univariate statistics and multiple logistic regression were used to assess the prevalence of MDR among enteric pathogens and the association between independent variables and presence of MRDOs among culture-positive patients. Results A total of 1198 patients had pathogens isolated by stool culture with antimicrobial susceptibility results. Among culture-positive patients, the prevalence of MDR was 54.3%. The prevalence of MDR was highest in Aeromonas spp. (81.5%), followed by Campylobacter spp. (72.1%), Vibrio cholerae (28.1%), Shigella spp. (26.2%), and Salmonella spp. (5.2%). Factors associated with having MDRO in multiple logistic regression included longer transport time to hospital (>90 min), greater stool frequency, prior antibiotic use prior to hospital presentation, and non-flush toilet use. However, pseudo-R2 was low 0.086, indicating that other unmeasured variables need to be considered to build a more robust predictive model of MDR. Conclusions MDR enteric pathogens were common in this study population with clinical, historical, and socio-environmental risk factors associated with MDROs. These findings may help guide clinical decision-making regarding antibiotic use and selection in patients at greatest risk of complications due to MDROs. Further prospective research is urgently needed to determine what additional factors place patients at greatest risk of MDRO, and the best strategies to mitigate the spread of MDR in enteric pathogens.

The pooled estimate of the average effect is of primary interest when fitting the random‐effects model for meta‐analysis. However estimates of study specific effects, for example those displayed on forest plots, are also often of interest. In this tutorial, we present the case, with the accompanying statistical theory, for estimating the study specific true effects using so called 'empirical Bayes estimates' or 'Best Unbiased Linear Predictions' under the random‐effects model. These estimates can be accompanied by prediction intervals that indicate a plausible range of study specific true effects. We coalesce and elucidate the available literature and illustrate the methodology using two published meta‐analyses as examples. We also perform a simulation study that reveals that coverage probability of study specific prediction intervals are substantially too low if the between‐study variance is small but not negligible. Researchers need to be aware of this defect when interpreting prediction intervals. We also show how empirical Bayes estimates, accompanied with study specific prediction intervals, can embellish forest plots. We hope that this tutorial will serve to provide a clear theoretical underpinning for this methodology and encourage its widespread adoption.

Diarrheal diseases lead to an estimated 1.3 million deaths each year, with the majority of those deaths occurring in patients over five years of age. As the severity of diarrheal disease can vary widely, accurately assessing dehydration status remains the most critical step in acute diarrhea management. The objective of this study is to empirically derive clinical diagnostic models for assessing dehydration severity in patients over five years with acute diarrhea in low resource settings. We enrolled a random sample of patients over five years with acute diarrhea presenting to the icddr,b Dhaka Hospital. Two blinded nurses independently assessed patients for symptoms/signs of dehydration on arrival. Afterward, consecutive weights were obtained to determine the percent weight change with rehydration, our criterion standard for dehydration severity. Full and simplified ordinal logistic regression models were derived to predict the outcome of none (9%) dehydration. The reliability and accuracy of each model were assessed. Bootstrapping was used to correct for over-optimism and compare each model’s performance to the current World Health Organization (WHO) algorithm. 2,172 patients were enrolled, of which 2,139 (98.5%) had complete data for analysis. The Inter-Class Correlation Coefficient (reliability) was 0.90 (95% CI = 0.87, 0.91) for the full model and 0.82 (95% CI = 0.77, 0.86) for the simplified model. The area under the Receiver-Operator Characteristic curve (accuracy) for severe dehydration was 0.79 (95% CI: 0.76–0.82) for the full model and 0.73 (95% CI: 0.70, 0.76) for the simplified model. The accuracy for both the full and simplified models were significantly better than the WHO algorithm (p, Author summary More than a million adults and older children die each year from diarrhea, with the vast majority living in low- and middle-income countries. Accurately assessing hydration status remains the most critical step in caring for these patients. While most patients with diarrhea can be managed safely at home, a small proportion will have severe dehydration and require intravenous fluid in a hospital setting to prevent organ damage and death. Despite dramatic improvements in diarrhea care around the world over the past several decades, there are still no evidence-based tools that doctors and nurses in low resource settings can use to determine an adult patient’s hydration status. Our study collected data on more than two thousand patients with acute diarrhea over the age of five years at Dhaka Hospital in Bangladesh. Using artificial intelligence techniques, we developed two new clinical diagnostic tools that can accurately and reliably predict the severity of dehydration in patients with diarrhea, which we will incorporate into a simple mobile phone application for use by doctors and nurses in low resource settings. Once validated, these new tools could improve care for patients with acute diarrhea worldwide.

The normal-normal hierarchical model (NNHM) constitutes a simple and widely used framework for meta-analysis. In the common case of only few studies contributing to the meta-analysis, standard approaches to inference tend to perform poorly, and Bayesian meta-analysis has been suggested as a potential solution. The Bayesian approach, however, requires the sensible specification of prior distributions. While non-informative priors are commonly used for the overall mean effect, the use of weakly informative priors has been suggested for the heterogeneity parameter, in particular in the setting of (very) few studies. To date, however, a consensus on how to generally specify a weakly informative heterogeneity prior is lacking. Here we investigate the problem more closely and provide some guidance on prior specification., Comment: 42 pages, 10 figures, 20 tables

Objective To examine pain treatment preferences before and after participation in an N-of-1 trial. Study Design and Setting In this observational study nested within a randomized trial, we examined chronic pain patients’ preferences before and after treatment in relation to N-of-1 trial results; assessed the influence of different schemes for defining comparative “superiority” on potential conclusions; and generated classification trees illustrating the relationship between pre-treatment preferences, N-of-1 trial results, and post-treatment preferences. Results Treatment preferences differed pre- and post-trial for 40% of participants. The proportion of patients whose N-of-1 trials demonstrated “superiority” of one treatment regimen over the other varied depending on how superiority was defined and ranged from 24% (using criteria that required statistically significant differences between regimens) to 62% (when relying only on differences in point estimates). Regardless of criteria for declaring treatment superiority, nearly three-fourths of patients with equivocal N-of-1 trial results nevertheless expressed definite preferences post-trial. Conclusion A large segment of patients undergoing N-of-1 trials for chronic pain altered their treatment preferences. However, the direction of preference change did not necessarily correspond to the N-of-1 results. More research is needed to understand how patients use N-of-1 trial results, why preferences are “sticky” even in the face of personalized data, and how patients and clinicians might be educated to use N-of-1 trial results more informatively.

Systematic reviews and meta-analyses synthesize results from well-conducted studies to optimize healthcare decision-making. Network meta-analysis (NMA) is particularly useful for improving precision, drawing new comparisons, and ranking multiple interventions. However, recommendations can be misled if published results are a selective sample of what has been collected by trialists, particularly when publication status is related to the significance of the findings. Unfortunately, the missing-not-at-random nature of this problem and the numerous parameters involved in modeling NMAs pose unique computational challenges to quantifying and correcting for publication bias, such that sensitivity analysis is used in practice. Motivated by this important methodological gap, we developed a novel and stable expectation-maximization (EM) algorithm to correct for publication bias in the network setting. We validate the method through simulation studies and show that it achieves substantial bias reduction in small to moderately sized NMAs. We also calibrate the method against a Bayesian analysis of a published NMA on antiplatlet therapies for maintaining vascular patency.

Key Points Question What is the effect of using nicotine salt pod system (NSPS) electronic cigarettes (e-cigarettes) for replacing cigarettes (ie, switching) on biomarkers of tobacco exposure and potential harm among cigarette smokers? Findings In this randomized clinical trial including 186 African American and Latinx adult smokers, those randomized to the e-cigarette condition maintained their cotinine levels and significantly reduced urinary 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and carbon monoxide levels and respiratory symptoms compared with controls smoking as usual at week 6. Lung function and blood pressure remained unchanged. Meaning This randomized clinical trial found that switching to NSPS e-cigarettes among adult smokers did not increase nicotine exposure and led to short-term reduction in the major pulmonary carcinogen, NNAL, compared with continued smoking., This randomized clinical trial examines biomarkers of carcinogen exposure in African American and Latinx smokers who switch to electronic cigarettes vs smoking as usual., Importance Fourth-generation nicotine salt pod system (NSPS) electronic cigarettes (e-cigarettes) are the leading class of e-cigarettes. They contain high nicotine concentrations, which may facilitate switching among smokers, but could also lead to increased exposure to nicotine and biomarkers of potential harm. African American and Latinx smokers experience significant tobacco-related health disparities. The potential of NSPS e-cigarettes to reduce smoking-related harm among these groups is unknown. Objective To compare the harm reduction potential of NSPS e-cigarette vs combustible cigarettes. Design, Setting, and Participants This unblinded randomized clinical trial compared 6 weeks of e-cigarette use vs cigarettes as usual from to 2018 to 2019 among smokers in the San Diego, California, and Kansas City, Missouri, areas. Participants included African American and Latinx adult combustible cigarette smokers who smoked at least 5 cigarettes/d on at least 25 of the past 30 days for at least 6 months and were interested in switching to e-cigarettes. Data were analyzed from September 18, 2019, to September 4, 2020. Interventions 6 weeks of e-cigarette use in a choice of pod flavors (5% nicotine) along with brief education, training, and action planning to completely switch to e-cigarettes from combustible cigarettes. The control group smoked combustible cigarettes as usual. Main Outcomes and Measures The primary outcome was reduction in urinary 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) concentration at week 6. Secondary outcomes were change in urinary cotinine, expired carbon monoxide (CO), respiratory symptoms, lung function, blood pressure, past 7-day consumption of combustible cigarettes, and switching rates (e-cigarette group only) at weeks 2 and 6. Results This study included 186 participants, including 92 African American participants and 94 Latinx participants. The mean (SD) age was 43.3 (12.5) years, and 75 (40.3%) were women. Participants smoked a mean (SD) of 12.1 (7.2) cigarettes/d on 6.8 (0.6) d/wk at baseline. A total of 125 participants were randomized to the e-cigarette group and 61 were randomized to the control group. At baseline, median (interquartile range) NNAL was 124 (45-197) pg/mL in the e-cigarette group and 88 (58-197) pg/mL in the control group. At week 6, the e-cigarette group had significantly greater reductions in NNAL (relative risk [RR], 0.36 [95% CI, 0.23-0.54]; P