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IntroductionHow to provide practice-integrated decision support to patients remains a challenge. We are testing the effectiveness of a practice-integrated programme targeting patients with a physician recommendation for colorectal cancer (CRC) screening.Methods and analysisIn partnership with healthcare teams, we developed 'e-assist: Colon Health', a patient-targeted, postvisit CRC screening decision support programme. The programme is housed within an electronic health record (EHR)-embedded patient portal. It leverages a physician screening recommendation as the cue to action and uses the portal to enrol and intervene with patients. Programme content complements patient-physician discussions by encouraging screening, addressing common questions and assisting with barrier removal. For evaluation, we are using a randomised trial in which patients are randomised to receive e-assist: Colon Health or one of two controls (usual care plus or usual care). Trial participants are average-risk, aged 50-75 years, due for CRC screening and received a physician order for stool testing or colonoscopy. Effectiveness will be evaluated by comparing screening use, as documented in the EHR, between trial enrollees in the e-assist: Colon Health and usual care plus (CRC screening information receipt) groups. Secondary outcomes include patient-perceived benefits of, barriers to and support for CRC screening and patient-reported CRC screening intent. The usual care group will be used to estimate screening use without intervention and programme impact at the population level. Differences in outcomes by study arm will be estimated with hierarchical logit models where patients are nested within physicians.Ethics and disseminationAll trial aspects have been approved by the Institutional Review Board of the health system in which the trial is being conducted. We will disseminate findings in diverse scientific venues and will target clinical and quality improvement audiences via other venues. The intervention could serve as a model for filling the gap between physician recommendations and patient action.Trial registration numberNCT02798224; Pre-results.

Within each of 170 physicians, patients were randomized to access e‐assist, an online program that aimed to increase colorectal cancer screening (CRCS), or control. Compliance was partial: 78.34%$$ 78.34\% $$ of the experimental patients accessed e‐assist while no controls were provided the access. Of interest are the average causal effect of assignment to treatment and the complier average causal effect as well as the variation of these causal effects across physicians. Each physician generates probabilities of screening for experimental compliers (experimental patients who accessed e‐assist), control compliers (controls who would have accessed e‐assist had they been assigned to e‐assist), and never takers (patients who would have avoided e‐assist no matter what). Estimating physician‐specific probabilities jointly over physicians poses novel challenges. We address these challenges by maximum likelihood, factoring a "complete‐data likelihood" uniquely into the conditional distribution of screening and partially observed compliance given random effects and the distribution of random effects. We marginalize this likelihood using adaptive Gauss–Hermite quadrature. The approach is doubly iterative in that the conditional distribution defies analytic evaluation. Because the small sample size per physician constrains estimability of multiple random effects, we reduce their dimensionality using a shared random effects model having a factor analytic structure. We assess estimators and recommend sample sizes to produce reasonably accurate and precise estimates by simulation, and analyze data from a trial of a CRCS intervention. [ABSTRACT FROM AUTHOR]

Research shows genetic counselors generally have pro‐White implicit biases—both prejudice and stereotyping. Cultural competency training aims to foster equitable beliefs, behaviors, and attitudes in cross‐cultural genetic counseling sessions, including those that are racially discordant (genetic counselors and patients are from different racial backgrounds). Therefore, cultural competency training has the potential to mitigate bias and reduce disparities. Here, we report the prevalence of cultural competency training among genetic counselors and associations between recency of training and counselors' racial biases. We conducted an online survey of genetic counselors and trainees in fall 2021. The survey assessed four types of bias (implicit/explicit prejudice and implicit/explicit stereotyping), time since last cultural competency training, time since last communication skills training, and frequency of clinic sessions with Black patients. Multiple linear regressions modeled associations between cultural competency training and different types of bias, adjusting for communication skills training, frequency of encounters with Black patients, and counselor race (White vs. non‐White). Two hundred fifteen participants (107 genetic counselors and 108 trainees) responded, and 205 reported whether they had prior cultural competency training. Of these, 187 (91%) reported ever having cultural competency training, most (53%) of participants who had training had it within 6 months prior to survey completion. We found no clear pattern of associations between cultural competency training and racial biases (implicit or explicit) in adjusted analyses. Participants who had cultural competency training four or more years prior demonstrated less negative implicit stereotyping toward Black individuals compared with those having more recent training; but no statistically significant effect was found for participants who reported never having cultural competency training, compared with those having training more recently than 4 years prior. Overall, our findings do not support that cultural competency training is negatively associated with, or mitigates, Black/White racial prejudices and stereotypes against Black patients. These findings suggest more effective interventions are needed to reduce racial biases. [ABSTRACT FROM AUTHOR]

We consider two-level models where a continuous response R and continuous covariates C are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data R obs and C obs . However, if the model for R given C includes random coefficients, interactions, or polynomial terms, their joint distribution will be nonstandard. We propose a family of unique factorizations involving selected "provisionally known random effects" u such that h (R obs , C obs | u) is normally distributed and u is a low-dimensional normal random vector; we approximate h (R obs , C obs) = ∫ h (R obs , C obs | u) g (u) du via adaptive Gauss-Hermite quadrature. For polynomial models, the approximation is exact but, in any case, can be made as accurate as required given sufficient computation time. The model incorporates random effects as explanatory variables, reducing bias due to measurement error. By construction, our factorizations solve problems of compatibility among fully conditional distributions that have arisen in Bayesian imputation based on the Gibbs Sampler. We spell out general rules for selecting u, and show that our factorizations can support fully compatible Bayesian methods of imputation using the Gibbs Sampler. for this article are available online. [ABSTRACT FROM AUTHOR]

Does reduced class size cause higher academic achievement for both Black and other students in reading, mathematics, listening, and word recognition skills? Do Black students benefit more than other students from reduced class size? Does the magnitude of the minority advantages vary significantly across schools? This article addresses the causal questions via analysis of experimental data from Tennessee's Student/Teacher Achievement Ratio study where students and teachers are randomly assigned to small or regular class type. Causal inference is based on a three-level multivariate simultaneous equation model (SM) where the class type as an instrumental variable (IV) and class size as an endogenous regressor interact with a Black student indicator. The randomized IV causes class size to vary which, by hypothesis, influences academic achievement overall and moderates a disparity in academic achievement between Black and other students. Within each subpopulation characterized by the ethnicity, the effect of reduced class size on academic achievement is the average causal effect. The difference in the average causal effects between the race ethnic groups yields the causal disparity in academic achievement. The SM efficiently handles ignorable missing data with a general missing pattern and is estimated by maximum likelihood. This approach extends Rubin's causal model to a three-level SM with cross-level causal interaction effects, requiring intact schools and no interference between classrooms as a modified Stable Unit Treatment Value Assumption. The results show that, for Black students, reduced class size causes higher academic achievement in the four domains each year from kindergarten to third grade, while for other students, it improves the four outcomes except for first-grade listening in kindergarten and first grade only. Evidence shows that Black students benefit more than others from reduced class size in first-, second-, and third-grade academic achievement. This article does not find evidence that the causal minority disparities are heterogeneous across schools in any given year. (Contains 3 figures, 4 tables, and 1 note.)

Social scientists are frequently interested in assessing the qualities of social settings such as classrooms, schools, neighborhoods, or day care centers. The most common procedure requires observers to rate social interactions within these settings on multiple items and then to combine the item responses to obtain a summary measure of setting quality. A key aspect of the quality of such a summary measure is its reliability. In this paper we derive a confidence interval for reliability, a test for the hypothesis that the reliability meets a minimum standard, and the power of this test against alternative hypotheses. Next, we consider the problem of using data from a preliminary field study of the measurement procedure to inform the design of a later study that will test substantive hypotheses about the correlates of setting quality. The preliminary study is typically called the "generalizability study" or "G study" while the later, substantive study is called the "decision study" or "D study." We show how to use data from the G study to estimate reliability, a confidence interval for the reliability, and the power of tests for the reliability of measurement produced under alternative designs for the D study. We conclude with a discussion of sample size requirements for G studies.

This article addresses three questions: Does reduced class size cause higher academic achievement in reading, mathematics, listening, and word recognition skills? If it does, how large are these effects? Does the magnitude of such effects vary significantly across schools? The authors analyze data from Tennessee's Student/Teacher Achievement Ratio study (STAR) of 1985, where students and teachers are randomly assigned to a small or regular class. The authors propose a three-level multivariate simultaneous equation model with an instrumental variable (IV) and estimation via maximum likelihood (ML) to analyze the data under an assumption of data missing at random (MAR). The IV, random assignment of students to a small or regular class, reduces class size which, by hypothesis, improves academic achievement in these domains. The authors extend Rubin's Causal Model (RCM) by involving a modified Stable Unit Treatment Value Assumption (SUTVA), requiring no interference between classrooms and intact schools. The method accommodates data with a general missing pattern and extracts full information for analysis from the STAR data. The authors investigate both homogenous and heterogenous causal effects of class size on academic achievement scores across schools. The results show that reducing class size improves reading, mathematics, listening, and word recognition test scores from kindergarten to third grade, although the effects appear relatively small in second grade. The authors find no evidence that the causal effects vary across schools. (Contains 2 figures and 6 tables.)

In organizational studies involving multiple levels, the association between a covariate and an outcome often differs at different levels of aggregation, giving rise to widespread interest in "contextual effects models." Such models partition the regression into within- and between-cluster components. The conventional approach uses each cluster's sample average of the covariate as a regressor to identify the between-cluster component of the regression. This procedure, however, yields biased estimates of contextual effects unless the cluster sizes are large. Moreover, bias in estimation of such contextual coefficients in turn introduces bias in estimated coefficients of other correlated cluster-level covariates. Missing data further complicate valid inferences. This article proposes an alternative approach that conditions on the latent "true" cluster means of covariates having contextual effects while taking into account ignorable missing data with a general missing pattern at each level. The proposed model may include random coefficients. We compare inferences under different approaches in estimation of a contextual effects model using data from two national surveys of high school achievement. (Contains 6 tables and 1 figure.)

Abstract Background Clot strength by Thrombelastography (TEG) is associated with mortality during trauma and has been linked to severity of tissue hypoperfusion. However, the optimal method for monitoring this important relationship remains undefined. We hypothesize that oxygen transport measurements will be associated with clot strength during traumatic shock, and test this hypothesis using a swine model of controlled traumatic shock. Methods N = 33 swine were subjected to femur fracture and hemorrhagic shock by controlled arterial bleeding to a predetermined level of oxygen debt measured by continuous indirect calorimetry. Hemodynamics, oxygen consumption, systemic central venous oxygenation (ScvO2), base excess, lactate, and clot maximal amplitude by TEG (TEG-MA) as clot strength were measured at baseline and again when oxygen debt = 80 ml/kg during shock. Oxygen transport and metabolic markers of tissue perfusion were then evaluated for significant associations with TEG-MA. Forward stepwise selection was then used to create regression models identifying the strongest associations between oxygen transport and TEG-MA independent of other known determinants of clot strength. Results Multiple markers of tissue perfusion, oxygen transport, and TEG-MA were all significantly altered during shock compared to baseline measurements (p < 0.05). However, only ScvO2 demonstrated a strong bivariate association with TEG-MA measured during shock (R = 0.7, p < 0.001). ScvO2 measured during shock was also selected by forward stepwise selection as an important covariate in linear regression models of TEG-MA after adjusting for the covariates fibrinogen, pH, platelet count, and hematocrit (Whole model R2 = 0.99, p ≤ 0.032). Conclusions Among multiple measurements of oxygen transport, only ScvO2 was found to retain a significant association with TEG-MA during shock after adjusting for multiple covariates. ScvO2 should be further studied for its utility as a clinical marker of both tissue hypoxia and clot formation during traumatic shock.