Possible poriferan body fossils in early Neoproterozoic microbial reefs.

Molecular phylogeny indicates that metazoans (animals) emerged early in the Neoproterozoic era1, but physical evidence is lacking. The search for animal fossils from the Proterozoic eon is hampered by uncertainty about what physical characteristics to expect. Sponges are the most basic known animal type2,3; it is possible that body fossils of hitherto-undiscovered Proterozoic metazoans might resemble aspect(s) of Phanerozoic fossil sponges. Vermiform microstructure4,5, a complex petrographic feature in Phanerozoic reefal and microbial carbonates, is now known to be the body fossil of nonspicular keratosan demosponges6-10. This Article presents petrographically identical vermiform microstructure from approximately 890-million-year-old reefs. The millimetric-to-centimetric vermiform-microstructured organism lived only on, in and immediately beside reefs built by calcifying cyanobacteria (photosynthesizers), and occupied microniches in which these calcimicrobes could not live. If vermiform microstructure is in fact the fossilized tissue of keratose sponges, the material described here would represent the oldest body-fossil evidence of animals known to date, and would provide the first physical evidence that animals emerged before the Neoproterozoic oxygenation event and survived through the glacial episodes of the Cryogenian period.

Author Correction: Early fungi from the Proterozoic era in Arctic Canada.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Early fungi from the Proterozoic era in Arctic Canada.

Fungi are crucial components of modern ecosystems. They may have had an important role in the colonization of land by eukaryotes, and in the appearance and success of land plants and metazoans1-3. Nevertheless, fossils that can unambiguously be identified as fungi are absent from the fossil record until the middle of the Palaeozoic era4,5. Here we show, using morphological, ultrastructural and spectroscopic analyses, that multicellular organic-walled microfossils preserved in shale of the Grassy Bay Formation (Shaler Supergroup, Arctic Canada), which dates to approximately 1,010-890 million years ago, have a fungal affinity. These microfossils are more than half a billion years older than previously reported unambiguous occurrences of fungi, a date which is consistent with data from molecular clocks for the emergence of this clade6,7. In extending the fossil record of the fungi, this finding also pushes back the minimum date for the appearance of eukaryotic crown group Opisthokonta, which comprises metazoans, fungi and their protist relatives8,9.

Designing individually randomized group treatment trials with repeated outcome measurements using generalized estimating equations.

Individually randomized group treatment (IRGT) trials, in which the clustering of outcome is induced by group-based treatment delivery, are increasingly popular in public health research. IRGT trials frequently incorporate longitudinal measurements, of which the proper sample size calculations should account for correlation structures reflecting both the treatment-induced clustering and repeated outcome measurements. Given the relatively sparse literature on designing longitudinal IRGT trials, we propose sample size procedures for continuous and binary outcomes based on the generalized estimating equations approach, employing the block exchangeable correlation structures with different correlation parameters for the treatment arm and for the control arm, and surveying five marginal mean models with different assumptions of time effect: no-time constant treatment effect, linear-time constant treatment effect, categorical-time constant treatment effect, linear time by treatment interaction, and categorical time by treatment interaction. Closed-form sample size formulas are derived for continuous outcomes, which depends on the eigenvalues of the correlation matrices; detailed numerical sample size procedures are proposed for binary outcomes. Through simulations, we demonstrate that the empirical power agrees well with the predicted power, for as few as eight groups formed in the treatment arm, when data are analyzed using the matrix-adjusted estimating equations for the correlation parameters with a bias-corrected sandwich variance estimator.

Dietary intervention in gestational diabetes: a qualitative study of the acceptability and feasibility of a novel whole-diet intervention in healthcare professionals.

Gestational diabetes is treated with medical nutrition therapy, delivered by healthcare professionals; however, the optimal diet for affected women is unknown. Randomised controlled trials, such as the DiGest (Dietary Intervention in Gestational Diabetes) trial, will address this knowledge gap, but the acceptability of whole-diet interventions in pregnancy is unclear. Whole-diet approaches reduce bias but require high levels of participant commitment and long intervention periods to generate meaningful clinical outcomes. We aimed to assess healthcare professionals' views on the acceptability of the DiGest dietbox intervention for women with gestational diabetes and to identify any barriers to adherence which could be addressed to support good recruitment and retention to the DiGest trial. Female healthcare professionals (n 16) were randomly allocated to receive a DiGest dietbox containing 1200 or 2000 kcal/d including at least one weeks' food. A semi-structured interview was conducted to explore participants' experience of the intervention. Interviews were audio-recorded, transcribed verbatim and analysed thematically using NVivo software. Based on the findings of qualitative interviews, modifications were made to the dietboxes. Participants found the dietboxes convenient and enjoyed the variety and taste of the meals. Factors which facilitated adherence included participants having a good understanding of study aims and sufficient organisational skills to facilitate weekly meal planning in advance. Barriers to adherence included peer pressure during social occasions and feelings of deprivation or hunger (affecting both standard and reduced calorie groups). Healthcare professionals considered random allocation to a whole-diet replacement intervention to be acceptable and feasible in a clinical environment and offered benefits to participants including convenience.

The Use and Perceptions of the Defense and Veterans Pain Rating Scale by Nursing Personnel.

BACKGROUND: In 2010, the Office of the US Army Surgeon General recommended the Veterans Administration (VA) assess pain using the Defense and Veterans Pain Rating Scale (DVPRS). One item in the DVPRS is for measuring pain intensity. This item contains a combination of five response metrics: categories, faces, colors, numbers, and functional descriptors. A few studies have supported patients' and health care providers' preferences for the DVPRS and its psychometric properties. However, they also left uncertainties about its usability and validity. AIMS: To advance our understanding of the DVPRS, this study examined the use and perceptions of the DVPRS' pain intensity item by nursing personnel during multi-modal care. DESIGN: A cross-sectional survey design was used. SETTING: VA Community Living Center. PARTICIPANTS: Nursing personnel. METHODS: Nursing personnel answered closed- and open-ended survey questions during a single session. RESULTS: Nursing personnel reported sufficient training before implementing the measure and that patients primarily used the numeric metric. When patients used a non-numeric metric, the nursing personnel responded in variable ways. In addition, the nursing personnel interpreted the functional descriptors differently. The nursing personnel also noted the need to supplement the pain intensity item with patients' pain duration and pain location. CONCLUSIONS: Results from this study inform the nursing community about the DVPRS' pain intensity item, which combines multiple response metrics. The results support the need for nursing units to generate and standardize procedures for using the item to measure multi-site pain and for interpreting and documenting patients' non-numeric responses. The effects of such procedures on the measure's usability and psychometric properties warrants additional investigation.

A Community-Based Intervention for Managing Hypertension in Rural South Asia.

BACKGROUND: The burden of hypertension is escalating, and control rates are poor in low- and middle-income countries. Cardiovascular mortality is high in rural areas. METHODS: We conducted a cluster-randomized, controlled trial in rural districts in Bangladesh, Pakistan, and Sri Lanka. A total of 30 communities were randomly assigned to either a multicomponent intervention (intervention group) or usual care (control group). The intervention involved home visits by trained government community health workers for blood-pressure monitoring and counseling, training of physicians, and care coordination in the public sector. A total of 2645 adults with hypertension were enrolled. The primary outcome was reduction in systolic blood pressure at 24 months. Follow-up at 24 months was completed for more than 90% of the participants. RESULTS: At baseline, the mean systolic blood pressure was 146.7 mm Hg in the intervention group and 144.7 mm Hg in the control group. At 24 months, the mean systolic blood pressure fell by 9.0 mm Hg in the intervention group and by 3.9 mm Hg in the control group; the mean reduction was 5.2 mm Hg greater with the intervention (95% confidence interval [CI], 3.2 to 7.1; P<0.001). The mean reduction in diastolic blood pressure was 2.8 mm Hg greater in the intervention group than in the control group (95% CI, 1.7 to 3.9). Blood-pressure control (<140/90 mm Hg) was achieved in 53.2% of the participants in the intervention group, as compared with 43.7% of those in the control group (relative risk, 1.22; 95% CI, 1.10 to 1.35). All-cause mortality was 2.9% in the intervention group and 4.3% in the control group. CONCLUSIONS: In rural communities in Bangladesh, Pakistan, and Sri Lanka, a multicomponent intervention that was centered on proactive home visits by trained government community health workers who were linked with existing public health care infrastructure led to a greater reduction in blood pressure than usual care among adults with hypertension. (Funded by the Joint Global Health Trials scheme; COBRA-BPS ClinicalTrials.gov number, NCT02657746.).

Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes.

Stepped wedge cluster randomized trials (SW-CRTs) with binary outcomes are increasingly used in prevention and implementation studies. Marginal models represent a flexible tool for analyzing SW-CRTs with population-averaged interpretations, but the joint estimation of the mean and intraclass correlation coefficients (ICCs) can be computationally intensive due to large cluster-period sizes. Motivated by the need for marginal inference in SW-CRTs, we propose a simple and efficient estimating equations approach to analyze cluster-period means. We show that the quasi-score for the marginal mean defined from individual-level observations can be reformulated as the quasi-score for the same marginal mean defined from the cluster-period means. An additional mapping of the individual-level ICCs into correlations for the cluster-period means further provides a rigorous justification for the cluster-period approach. The proposed approach addresses a long-recognized computational burden associated with estimating equations defined based on individual-level observations, and enables fast point and interval estimation of the intervention effect and correlations. We further propose matrix-adjusted estimating equations to improve the finite-sample inference for ICCs. By providing a valid approach to estimate ICCs within the class of generalized linear models for correlated binary outcomes, this article operationalizes key recommendations from the CONSORT extension to SW-CRTs, including the reporting of ICCs.

Telemedicine Referral to Improve Access to Specialty Care for Preschool Children in Rural Alaska: A Cluster-Randomized Controlled Trial.

OBJECTIVES: Preschool programs provide essential preventive services, such as hearing screening, but in rural regions, limited access to specialists and loss to follow-up compound rural health disparities. We conducted a parallel-arm cluster-randomized controlled trial to evaluate telemedicine specialty referral for preschool hearing screening. The goal of this trial was to improve timely identification and treatment of early childhood infection-related hearing loss, a preventable condition with lifelong implications. We hypothesized that telemedicine specialty referral would improve time to follow-up and the number of children receiving follow-up compared with the standard primary care referral. DESIGN: We conducted a cluster-randomized controlled trial in K-12 schools in 15 communities over two academic years. Community randomization occurred within four strata using location and school size. In the second academic year (2018-2019), an ancillary trial was performed in the 14 communities that had preschools to compare telemedicine specialty referral (intervention) to standard primary care referral (comparison) for preschool hearing screening. Randomization of communities from the main trial was used for this ancillary trial. All children enrolled in preschool were eligible. Masking was not possible because of timing in the second year of the main trial, but referral assignment was not openly disclosed. Study team members and school staff were masked throughout data collection, and statisticians were blinded to allocation during analysis. Preschool screening occurred once, and children who were referred for possible hearing loss or ear disease were monitored for follow-up for 9 months from the screening date. The primary outcome was time to ear/hearing-related follow-up from the date of screening. The secondary outcome was any ear/hearing follow-up from screening to 9 months. Analyses were conducted using an intention-to-treat approach. RESULTS: A total of 153 children were screened between September 2018 and March 2019. Of the 14 communities, 8 were assigned to the telemedicine specialty referral pathway (90 children), and 6 to the standard primary care referral pathway (63 children). Seventy-one children (46.4%) were referred for follow-up: 39 (43.3%) in the telemedicine specialty referral communities and 32 (50.8%) in the standard primary care referral communities. Of children referred, 30 (76.9%) children in telemedicine specialty referral communities and 16 (50.0%) children in standard primary care referral communities received follow-up within 9 months (Risk Ratio = 1.57; 95% confidence interval [CI], 1.22 to 2.01). Among children who received follow-up, median time to follow-up was 28 days (interquartile range [IQR]: 15 to 71) in telemedicine specialty referral communities compared with 85 days (IQR: 26 to 129) in standard primary care referral communities. Mean time to follow-up for all referred children was 4.5 (event time ratio = 4.5; 95% CI, 1.8 to 11.4; p = 0.045) times faster in telemedicine specialty referral communities compared with standard primary care referral communities in the 9-month follow-up time frame. CONCLUSIONS: Telemedicine specialty referral significantly improved follow-up and reduced time to follow-up after preschool hearing screening in rural Alaska. Telemedicine referrals could extend to other preventive school-based services to improve access to specialty care for rural preschool children.

Machine Learning Model Identifies Preoperative Opioid Use, Male Sex, and Elevated Body Mass Index as Predictive Factors for Prolonged Opioid Consumption Following Arthroscopic Meniscal Surgery.

PURPOSE: To develop a predictive machine learning model to identify prognostic factors for continued opioid prescriptions after arthroscopic meniscus surgery. METHODS: Patients undergoing arthroscopic meniscal surgery, such as meniscus debridement, repair, or revision at a single institution from 2013 to 2017 were retrospectively followed up to 1 year postoperatively. Procedural details were recorded, including concomitant procedures, primary versus revision, and whether a partial debridement or a repair was performed. Intraoperative arthritis severity was measured using the Outerbridge Classification. The number of opioid prescriptions in each month was recorded. Primary analysis used was the multivariate Cox-Regression model. We then created a naïve Bayesian model, a machine learning classifier that uses Bayes' theorem with an assumption of independence between variables. RESULTS: A total of 581 patients were reviewed. Postoperative opioid refills occurred in 98 patients (16.9%). Multivariate logistic modeling was used; independent risk factors for opioid refills included male sex, larger body mass index, and chronic preoperative opioid use, while meniscus resection demonstrated decreased likelihood of refills. Concomitant procedures, revision procedures, and presence of arthritis graded by the Outerbridge classification were not significant predictors of postoperative opioid refills. The naïve Bayesian model for extended postoperative opioid use demonstrated good fit with our cohort with an area under the curve of 0.79, sensitivity of 94.5%, positive predictive value (PPV) of 83%, and a detection rate of 78.2%. The two most important features in the model were preoperative opioid use and male sex. CONCLUSION: After arthroscopic meniscus surgery, preoperative opioid consumption and male sex were the most significant predictors for sustained opioid use beyond 1 month postoperatively. Intraoperative arthritis was not an independent risk factor for continued refills. A machine learning algorithm performed with high accuracy, although with a high false positive rate, to function as a screening tool to identify patients filling additional narcotic prescriptions after surgery. LEVEL OF EVIDENCE: III, retrospective comparative study.

Improving sandwich variance estimation for marginal Cox analysis of cluster randomized trials.

Cluster randomized trials (CRTs) frequently recruit a small number of clusters, therefore necessitating the application of small-sample corrections for valid inference. A recent systematic review indicated that CRTs reporting right-censored, time-to-event outcomes are not uncommon and that the marginal Cox proportional hazards model is one of the common approaches used for primary analysis. While small-sample corrections have been studied under marginal models with continuous, binary, and count outcomes, no prior research has been devoted to the development and evaluation of bias-corrected sandwich variance estimators when clustered time-to-event outcomes are analyzed by the marginal Cox model. To improve current practice, we propose nine bias-corrected sandwich variance estimators for the analysis of CRTs using the marginal Cox model and report on a simulation study to evaluate their small-sample properties. Our results indicate that the optimal choice of bias-corrected sandwich variance estimator for CRTs with survival outcomes can depend on the variability of cluster sizes and can also slightly differ whether it is evaluated according to relative bias or type I error rate. Finally, we illustrate the new variance estimators in a real-world CRT where the conclusion about intervention effectiveness differs depending on the use of small-sample bias corrections. The proposed sandwich variance estimators are implemented in an R package CoxBcv.

The Patient Experience After Spontaneous Coronary Artery Dissection.

OBJECTIVE: There is considerable burden of anxiety, depression, and post-traumatic stress disorder in patients with spontaneous coronary artery dissection (SCAD), yet research is limited on the experience and impact of SCAD from the patient perspective. This literature review sought to describe the current state of the literature on the patient experience of SCAD and consequences for patients following a SCAD event from the patient perspective. To better understand how people's experiences of SCAD affect their wellbeing, quality of life, lifestyles, and identity, and what would be useful from the patient perspective, an integrative review was performed. METHODS: An integrative literature review was conducted to understand the experience of SCAD and the post-event implications. Five databases were searched. Search terms included 'spontaneous coronary artery dissection', 'SCAD', 'patient', 'experience', 'perspective', and 'opinion'. English-language, peer-reviewed primary research in people with a diagnosis of SCAD that reflected the patient experience was included. Data indicating the SCAD experience including distress prevalence were extracted into an Excel spreadsheet, and narrative synthesis of included studies followed. FINDINGS: From 325 identified studies, five were included for review, yielding a combined sample of 447 participants. Patients with SCAD reported a lack of information about SCAD and the recovery process, and use of the internet for obtaining information. Patients with SCAD reported challenges in recovery including anxiety associated with fear of recurrence and uncertainty, and a need for greater support. A wide range of negative emotions was reported during and after the SCAD event. Participants reported participating in support groups, with mixed reviews of their appropriateness and effectiveness.

Perceptions of drug users and community gate keepers on the causes and consequences of synthetic drug use in Kunming: A three-level social-ecological approach.

Synthetic drug use (SDU) is on the rise in China. Utilizing a grounded three-level social-ecological theoretical model, we aim to better understand how users, medical professionals, and other community gatekeepers perceive the causes and consequences of synthetic drug use in Kunming, China. Past work typically relies on drug users confined to rehabilitation facilities. Utilizing qualitative methods, our work integrates how various community actors perceive problems around synthetic drug use. Thirty face-to-face interviews were conducted in Kunming that were audio-recorded and transcribed. We identify emergent personal, interpersonal and societal level themes shaping SDU which provided our grounded theoretical model. Regardless of their social position, informants identified curiosity, peer networks that facilitated exposure, and the communality of sharing the drug experience as reasons to try synthetic drugs. Drug users reported negative consequences of SDU including the inability to sleep, a fear that others might discover one was using, and the difficulty of quitting. Medical professionals and others in the community were more likely to identify potential harms of SDU. Still, these community members felt synthetic drugs were less problematic than traditional drugs and reported less prejudice and stigma about these new drugs. Overall, medical professionals felt ill-prepared to deal with this new epidemic.

When Shepherds Shed: Trajectories of Weight-Related Behaviors in a Holistic Health Intervention Tailored for US Christian Clergy.

Maintaining healthy behaviors is challenging. Based upon previous reports that in North Carolina (NC), USA, overweight/obese clergy lost weight during a two-year religiously tailored health intervention, we described trajectories of diet, physical activity, and sleep. We investigated whether behavior changes were associated with weight and use of health-promoting theological messages. Improvements were observed in sleep, calorie-dense food intake, and physical activity, with the latter two associated with weight loss. While theological messages were well-retained, their relationship with behaviors depended on the specific message, behavior, and timing. Findings offer insights into weight loss mechanisms, including the role of theological messages in religiously tailored health interventions.

Constrained randomization and statistical inference for multi-arm parallel cluster randomized controlled trials.

A practical limitation of cluster randomized controlled trials (cRCTs) is that the number of available clusters may be small, resulting in an increased risk of baseline imbalance under simple randomization. Constrained randomization overcomes this issue by restricting the allocation to a subset of randomization schemes where sufficient overall covariate balance across comparison arms is achieved. However, for multi-arm cRCTs, several design and analysis issues pertaining to constrained randomization have not been fully investigated. Motivated by an ongoing multi-arm cRCT, we elaborate the method of constrained randomization and provide a comprehensive evaluation of the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs in multi-arm cRCTs, with varying combinations of design and analysis-based covariate adjustment strategies. In particular, as randomization-based tests have not been extensively studied in multi-arm cRCTs, we additionally develop most-powerful randomization tests under the linear mixed model framework for our comparisons. Our results indicate that under constrained randomization, both model-based and randomization-based analyses could gain power while preserving nominal type I error rate, given proper analysis-based adjustment for the baseline covariates. Randomization-based analyses, however, are more robust against violations of distributional assumptions. The choice of balance metrics and candidate set sizes and their implications on the testing of the pairwise and global hypotheses are also discussed. Finally, we caution against the design and analysis of multi-arm cRCTs with an extremely small number of clusters, due to insufficient degrees of freedom and the tendency to obtain an overly restricted randomization space.

Healthy eating and all-cause mortality among Chinese aged 80 years or older.

BACKGROUND: There is little evidence of the influence of dietary patterns on mortality risk among adults 80 years or older ("oldest-old"). We evaluated the association between the Simplified Healthy Eating index (SHE-index) and mortality among Chinese oldest-old. METHODS: Population-based cohort study from the Chinese Longitudinal Healthy Longevity Survey (CLHLS 1998-2014, n = 35 927), conducted in 22 Chinese provinces, were pooled for analysis. The first seven waves of the CLHLS (1998, 2000, 2002, 2005, 2008-09, 2011-12, and 2013-2014) were utilized, with follow-up to the last wave (2018) (range 0-21 years). The SHE-index was collected in each wave, and was constructed from intake frequency of nine dietary variables, with a higher score indicating better diet quality. Cox proportional hazards model with dietary patterns as a time-varying exposure was employed to analyze the relationship between SHE-index and mortality. RESULTS: At baseline, the median age of all participants was 92 years (25th percentile, 85 years; 75th percentile, 100 years). In multivariable models, the hazard ratios (95% confidence intervals) for SHE-index quartile 2, quartile 3 and quartile 4 versus quartile1 were 0.91 (0.88, 0.93), 0.89 (0.86, 0.92) and 0.82 (0.78, 0.85), respectively. Results were generally consistent for men and women and in a large number of sensitivity analyses. CONCLUSIONS: Healthier eating patterns were associated with a significant reduction in the risk of all-cause mortality among Chinese oldest-old, lending support to the importance of life-long adherence to healthy diet into advanced old age.

Oral health self-care behaviours in serious mental illness: A systematic review and meta-analysis.

AIM: To understand the relationship between serious mental illness and oral health self-care behaviours using meta-analytic methods and a narrative synthesis of available literature. METHOD: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines [PROSPERO reference: CRD42020176779]. Search terms pertaining to serious mental illness and oral health were entered into EMBASE, PsycINFO, Medline and CINAHL. Eligible studies included a sample of people with a serious mental illness and a quantitative measure of an oral health self-care behaviour (eg dental visits, toothbrushing). The Effective Public Health Practice Project tool was utilised to appraise the quality of the literature. Studies in the meta-analysis contained a non-clinical or general population comparator sample. RESULTS: People with a serious mental illness were significantly less likely to visit the dentist (OR 0.46, 95% CI 0.32-0.065, p > 0.001) or brush their teeth (OR 0.19, 95% CI 0.08-0.42, p < 0.001) when compared to non-clinical comparator samples. Few studies explored other oral health self-care behaviours (eg flossing and mouth washing), but uptake was generally low in people with a serious mental illness. The study quality of included studies was variable. CONCLUSIONS: The research showed a reduced uptake of oral health self-care behaviours in people with a serious mental illness. Suboptimal oral health can negatively impact on physical, social and psychological functioning. Further research is needed to understand the reasons for low rates of oral health self-care behaviours in this population.

Sauti ya Vijana (SYV; The Voice of Youth): Longitudinal Outcomes of an Individually Randomized Group Treatment Pilot Trial for Young People Living with HIV in Tanzania.

Sauti ya Vijana is a mental health and life skills intervention delivered by young adult group leaders for the improvement of HIV outcomes in young people living with HIV in Tanzania. This pilot randomized controlled trial estimated exploratory intervention effectiveness compared to standard of care. YPLWH (N = 105) were randomized to receive intervention or SOC. The mean age of participants was 18.1 years and 53% were female. Mean scores on mental health measures (Patient Health Questionnaire [PHQ-9], Strengths and Difficulties Questionnaire [SDQ], UCLA Trauma) were asymptomatic to mild in both study arms through 30-month follow-up with a non-significant fluctuation of 1-2 points. The mean self-reported adherence was higher in the intervention arm across all time points (but the confidence interval contained the null at all time points except 6 months). Risk ratio of virologic suppression (HIV RNA < 400 copies/mL) in the intervention arm compared to SOC was 1.15 [95% CI = 0.95, 1.39]) at 6-months, 1.17 [95% CI: 0.92, 1.48] at 12-months, and 0.99 [95% CI 0.76, 1.31] at 18-months. Though these findings were not powered for statistical significance, the trends in HIV outcomes suggest that SYV holds promise for improving antiretroviral therapy (ART) adherence and virologic suppression in YPLWH.

power swgee: GEE-based power calculations in stepped wedge cluster randomized trials.

Stepped wedge cluster randomized trials are increasingly being used to evaluate interventions in medical, public health, educational, and social science contexts. With the longitudinal and crossover nature of a SW-CRT, complex analysis techniques are often needed which makes appropriately powering SW-CRTs challenging. In this paper, we introduce a newly-developed SW-CRT power calculator, embedded within the power command in Stata. The power calculator assumes a marginal model (i.e., generalized estimating equations [GEE]) for the primary analysis of SW-CRTs, for which other currently available SW-CRT power calculators may not be suitable. The program accommodates complete cross-sectional and closed-cohort designs, and includes multilevel correlation structures appropriate for such designs. We discuss the methods and formulae underlying our SW-CRT calculator, and provide illustrative examples of the use of power swgee. We provide suggestions about the choice of parameters in power swgee, and conclude by discussing areas of future research which may improve the program.

Power considerations for generalized estimating equations analyses of four-level cluster randomized trials.

In this article, we develop methods for sample size and power calculations in four-level intervention studies when intervention assignment is carried out at any level, with a particular focus on cluster randomized trials (CRTs). CRTs involving four levels are becoming popular in healthcare research, where the effects are measured, for example, from evaluations (level 1) within participants (level 2) in divisions (level 3) that are nested in clusters (level 4). In such multilevel CRTs, we consider three types of intraclass correlations between different evaluations to account for such clustering: that of the same participant, that of different participants from the same division, and that of different participants from different divisions in the same cluster. Assuming arbitrary link and variance functions, with the proposed correlation structure as the true correlation structure, closed-form sample size formulas for randomization carried out at any level (including individually randomized trials within a four-level clustered structure) are derived based on the generalized estimating equations approach using the model-based variance and using the sandwich variance with an independence working correlation matrix. We demonstrate that empirical power corresponds well with that predicted by the proposed method for as few as eight clusters, when data are analyzed using the matrix-adjusted estimating equations for the correlation parameters with a bias-corrected sandwich variance estimator, under both balanced and unbalanced designs.