A Pilot Study to Evaluate the Use of Automated Nicotine Metabolite Ratio Reporting within Primary Care as an Implementation Strategy to Increase the Use of Tobacco Treatments.

INTRODUCTION: Concerns about safety and effectiveness of tobacco treatments reduce their use. We explored integrating the nicotine metabolite ratio (NMR), and messaging about its potential for improving safety and effectiveness, as a strategy to increase use of tobacco treatments within primary care. METHODS: Through a prospective cohort design, we explored the effects of integrating NMR testing within primary care on the provision of tobacco treatment; 65 patients completed assessments including NMR before a clinic visit. At the clinic visit, patients' clinicians received an electronic health record alert about the patient's NMR and personalized treatment recommendations to improve effectiveness and safety. Being asked about smoking and advised to quit, and a referral for tobacco treatment or medication prescription, were assessed within 30 days of the appointment and were compared to a usual care cohort (N=85). RESULTS: The NMR and usual care cohorts reported similar rates of being asked about smoking (92.3% vs. 92.9%, p=1.0), being advised to quit (72.3% vs. 74.1%, p=0.85), being referred for tobacco treatment (23.1% vs. 36.5%, p=0.11), and receiving tobacco use medications (20% vs. 27.1%, p=0.34). In the NMR cohort, fast vs. slow metabolizers were more likely to receive medication (26% vs. 0%, p=0.003) and all patients who received varenicline (n=8) were fast metabolizers. CONCLUSIONS: NMR results and treatment recommendations did not increase tobacco treatment rates in primary care, although it may increase treatment rates and use of varenicline for fast metabolizers. Future studies could test ways to use the NMR to increase tobacco treatment rates in clinical settings. IMPLICATIONS: This study generated a novel implementation strategy, namely an electronic health record alert about patients' NMR and personalized treatment recommendations, in an effort to increase tobacco treatment rates in primary care. While the strategy did not increase tobacco treatment rates, it may have boosted the rate of varenicline prescription for patients who metabolize nicotine faster, aligning with evidence-based practice.

Estimating the Effects of Non-Pharmaceutical Interventions and Population Mobility on Daily COVID-19 Cases: Evidence from Ontario.

This study uses coronavirus disease 2019 (COVID-19) case counts and Google mobility data for 12 of Ontario's largest Public Health Units from Spring 2020 until the end of January 2021 to evaluate the effects of non-pharmaceutical interventions (NPIs; policy restrictions on business operations and social gatherings) and population mobility on daily cases. Instrumental variables (IV) estimation is used to account for potential simultaneity bias, because both daily COVID-19 cases and NPIs are dependent on lagged case numbers. IV estimates based on differences in lag lengths to infer causal estimates imply that the implementation of stricter NPIs and indoor mask mandates are associated with reductions in COVID-19 cases. Moreover, estimates based on Google mobility data suggest that increases in workplace attendance are correlated with higher case counts. Finally, from October 2020 to January 2021, daily Ontario forecasts from Box-Jenkins time-series models are more accurate than official forecasts and forecasts from a susceptible-infected-removed epidemiology model.

Cette étude cherche à évaluer les effets des interventions non pharmaceutiques (INPs; restrictions sur les activités commerciales et rassemblements sociaux) et de la mobilité de la population sur le nombre de cas d'infection par jour, en utilisant les nombres de cas d'infection par la maladie à coronavirus 2019 (COVID-19) et les données de mobilité de Google pour 12 des plus grands Bureaux de Santé publique de l'Ontario entre le printemps 2020 et la fin janvier 2021. La méthode des variables instrumentales (VI) permet de rendre compte d'un biais potentiel de simultanéité puisque les taux quotidiens de COVID-19 et les INPs dépendent, tous les deux, du nombre de cas décalés. Les estimations par les VI basées sur les différences de durée des décalages d'ajustement pour inférer des estimations causales impliquent que de plus strictes INPs et le port obligatoire du masque dans les endroits fermés sont associés à une réduction de cas d'infection. Par ailleurs, Les estimations basées sur les données de mobilité de Google montrent que la présence accrue sur le lieu du travail est corrélée avec un plus grand nombre de cas d'infection. Finalement, d'octobre 2020 à Janvier 2021, les prévisions faites à partir de modèles de Box-Jenkins en série chronologique s'avèrent plus précises que les prévisions officielles et que celles utilisant le modèle épidémiologique susceptible ­ infecté ­ retiré.

Comparing Kaplan-Meier curves with the probability of agreement.

The probability of agreement has been used as an effective strategy for quantifying the similarity between the reliability of two populations. By contrast to hypothesis testing approaches based on P-values, the probability of agreement provides a more realistic assessment of similarity by emphasizing practically important differences. In this article, we propose the use of the probability of agreement to evaluate the similarity of two Kaplan-Meier curves, which estimate the survival functions in two populations. This article extends the probability of agreement paradigm to right censored data and explores three different methods of quantifying uncertainty in the probability of agreement estimate. The first approach provides a convenient assessment based on large-sample normal-theory (LSNT), while the other two approaches are nonparametric alternatives based on ordinary and fractional random-weight bootstrap (FRWB) techniques. All methods are illustrated with examples for which comparing the survival curves of related populations is of interest and the efficacy of the methods are also evaluated through simulation studies. Based on these simulations we recommend point estimation using the proposed LSNT calculation and confidence interval estimation via the FRWB approach. We also provide a Shiny app that facilitates an automated implementation of the methodology.

Using multiple agreement methods for continuous repeated measures data: a tutorial for practitioners.

BACKGROUND: Studies of agreement examine the distance between readings made by different devices or observers measuring the same quantity. If the values generated by each device are close together most of the time then we conclude that the devices agree. Several different agreement methods have been described in the literature, in the linear mixed modelling framework, for use when there are time-matched repeated measurements within subjects. METHODS: We provide a tutorial to help guide practitioners when choosing among different methods of assessing agreement based on a linear mixed model assumption. We illustrate the use of five methods in a head-to-head comparison using real data from a study involving Chronic Obstructive Pulmonary Disease (COPD) patients and matched repeated respiratory rate observations. The methods used were the concordance correlation coefficient, limits of agreement, total deviation index, coverage probability, and coefficient of individual agreement. RESULTS: The five methods generated similar conclusions about the agreement between devices in the COPD example; however, some methods emphasized different aspects of the between-device comparison, and the interpretation was clearer for some methods compared to others. CONCLUSIONS: Five different methods used to assess agreement have been compared in the same setting to facilitate understanding and encourage the use of multiple agreement methods in practice. Although there are similarities between the methods, each method has its own strengths and weaknesses which are important for researchers to be aware of. We suggest that researchers consider using the coverage probability method alongside a graphical display of the raw data in method comparison studies. In the case of disagreement between devices, it is important to look beyond the overall summary agreement indices and consider the underlying causes. Summarising the data graphically and examining model parameters can both help with this.

History and Correlates of Smoking Cessation Behaviors Among Smokers With Serious Mental Illness.

INTRODUCTION: Individuals with serious mental illness (SMI) smoke at rates two to three times greater than the general population but are less likely to receive treatment. Increasing our understanding of correlates of smoking cessation behaviors in this group can guide intervention development. AIMS AND METHODS: Baseline data from an ongoing trial involving smokers with SMI (N = 482) were used to describe smoking cessation behaviors (ie, quit attempts, quit motivation, and smoking cessation treatment) and correlates of these behaviors (ie, demographics, attitudinal and systems-related variables). RESULTS: Forty-three percent of the sample did not report making a quit attempt in the last year, but 44% reported making one to six quit attempts; 43% and 20%, respectively, reported wanting to quit within the next 6 months or the next 30 days. Sixty-one percent used a smoking cessation medication during their quit attempt, while 13% utilized counseling. More quit attempts were associated with lower nicotine dependence and carbon monoxide and greater beliefs about the harms of smoking. Greater quit motivation was associated with lower carbon monoxide, minority race, benefits of cessation counseling, and importance of counseling within the clinic. A greater likelihood of using smoking cessation medications was associated with being female, smoking more cigarettes, and receiving smoking cessation advice. A greater likelihood of using smoking cessation counseling was associated with being male, greater academic achievement, and receiving smoking cessation advice. CONCLUSIONS: Many smokers with SMI are engaged in efforts to quit smoking. Measures of smoking cessation behavior are associated with tobacco use indicators, beliefs about smoking, race and gender, and receiving cessation advice. IMPLICATIONS: Consideration of factors related to cessation behaviors among smokers with SMI continues to be warranted, due to their high smoking rates compared to the general population. Increasing our understanding of these predictive characteristics can help promote higher engagement in evidence-based smoking cessation treatments among this subpopulation.

A Cluster-Randomized Clinical Trial Testing the Effectiveness of the Addressing Tobacco Through Organizational Change Model for Improving the Treatment of Tobacco Use in Community Mental Health Care: Preliminary Study Feasibility and Baseline Findings.

INTRODUCTION: People with mental illness are more likely to smoke and less likely to receive tobacco treatment than the general population. The Addressing Tobacco Through Organizational Change (ATTOC) approach supports organizational change to increase tobacco treatment in this population. We describe preliminary study feasibility and baseline behaviors and attitudes among clients and staff regarding tobacco treatment, and assesse correlates of treatment of smoking. METHODS: Preliminary accrual, engagement, and baseline data are reported from a cluster-randomized trial comparing ATTOC to usual care. Feasibility, thus far, was the rate of site and participant accrual and engagement (eg, participants remaining in the trial). Correlates of assessing smoking, advising cessation, and providing treatment were assessed. RESULTS: Site and participant accrual is 80% (8/10) and 86% (456/533), and engagement is 100% and 82%. "Staff asking about smoking" was reported by 63% of clients and 38% of staff; "staff advising cessation" was reported by 57% of clients and 46% of staff; staff report "assisting clients with any medication" at most 22% of the time, whereas at most 18% of clients report receiving a cessation medication; 59% of clients want tobacco treatment, but 36% of staff think that it is part of their job. "Staff assisting with medications" is related to more training, believing treating smoking is part of their job, and believing patients are concerned about smoking (ps < .05). CONCLUSIONS: This trial of training in tobacco treatment within mental health care is feasible thus far; self-reported rates of tobacco treatment are low and associated with clinician attitudes and barriers. IMPLICATIONS: Evaluation of ways to help address tobacco use treatment in community mental health care is feasible and needed, including the use of technical assistance and training guided by an organizational change approach.

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities.

Introduction: There is a vast literature on the performance of different short-term forecasting models for country specific COVID-19 cases, but much less research with respect to city level cases. This paper employs daily case counts for 25 Metropolitan Statistical Areas (MSAs) in the U.S. to evaluate the efficacy of a variety of statistical forecasting models with respect to 7 and 28-day ahead predictions. Methods: This study employed Gradient Boosted Regression Trees (GBRT), Linear Mixed Effects (LME), Susceptible, Infectious, or Recovered (SIR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to generate daily forecasts of COVID-19 cases from November 2020 to March 2021. Results: Consistent with other research that have employed Machine Learning (ML) based methods, we find that Median Absolute Percentage Error (MAPE) values for both 7-day ahead and 28-day ahead predictions from GBRTs are lower than corresponding values from SIR, Linear Mixed Effects (LME), and Seasonal Autoregressive Integrated Moving Average (SARIMA) specifications for the majority of MSAs during November-December 2020 and January 2021. GBRT and SARIMA models do not offer high-quality predictions for February 2021. However, SARIMA generated MAPE values for 28-day ahead predictions are slightly lower than corresponding GBRT estimates for March 2021. Discussion: The results of this research demonstrate that basic ML models can lead to relatively accurate forecasts at the local level, which is important for resource allocation decisions and epidemiological surveillance by policymakers.

Predictors of improved clinician screening, assessment, and treatment for tobacco use for clients in community mental healthcare following training.

Introduction: People with mental illness (MI) are more likely to smoke cigarettes and less likely to receive treatment for tobacco use than the general population. Understanding factors associated with improved staff treatment of tobacco use in community mental health settings has received limited study. Methods: We used data from a completed cluster-randomized clinical trial that tested two interventions designed to increase treatment for tobacco use in mental health clinics. Among 222 clinic staff, we examined demographic and employment characteristics, changes in perceived skills, knowledge, and beliefs using the S-KAP (i.e., perceptions of staff responsibility to treat tobacco use; client quit motivation; client outcomes; and barriers) as predictors of change in clinician reported delivery of tobacco use treatment following training. Results: Clinician reported treatment of client tobacco use significantly increased from baseline to week 52 across both study arms (p<0.001). This increase in reported treatment for tobacco use was associated with increases from baseline to week 52 in clinician reported skills to treat tobacco use, perceptions of responsibility to treat client tobacco use, and perceptions about client motivation to quit smoking (p's<0.05). Conclusions: Training clinicians in community mental healthcare to address client tobacco use may improve outcomes by helping them to develop the needed skills, convincing them that treating tobacco use is part of their role as clinicians, and by helping clinicians to recognize that clients are motivated to quit smoking. These may be targets to improve how clinicians in community health settings address client tobacco use.

A randomized clinical trial testing two implementation strategies to promote the treatment of tobacco dependence in community mental healthcare.

INTRODUCTION: People with serious mental illness (SMI) are more likely to smoke and less likely to receive tobacco treatment. Implementation strategies may address clinician and organizational barriers to treating tobacco in mental healthcare. METHODS: A cluster-randomized trial (Clinic N=13, Client N=610, Staff N=222) tested two models to promote tobacco treatment in community mental healthcare: standard didactic training vs. Addressing Tobacco Through Organizational Change (ATTOC), an organizational model that provides clinician and leadership training and addresses system barriers to tobacco treatment. Primary outcomes were changes in tobacco treatment from clients, staff, and medical records. Secondary outcomes were changes in smoking, mental health, and quality of life (QOL), and staff skills and barriers to treat tobacco. RESULTS: Clients at ATTOC sites reported a significant increase in receiving tobacco treatment from clinician at weeks 12 and 24 (ps<0.05) and tobacco treatments and policies from clinics at weeks 12, 24, 36, and 52 (ps<0.05), vs. standard sites. ATTOC staff reported a significant increase in skills to treat tobacco at week 36 (p=0.05), vs. standard sites. For both models, tobacco use medications, from clients (week 52) and medical records (week 36), increased (ps<0.05), while perceived barriers decreased at weeks 24 and 52 (ps<0.05); 4.3% of clients quit smoking which was not associated with model. QOL and mental health improved over 24 weeks for both models (ps<0.05). CONCLUSIONS: Standard training and ATTOC improve use of evidence-based tobacco treatments in community mental healthcare without worsening mental health, but ATTOC may more effectively address this practice gap.

Monitoring radiation use in cardiac fluoroscopy imaging procedures.

PURPOSE: Timely identification of systematic changes in radiation delivery of an imaging system can lead to a reduction in risk for the patients involved. However, existing quality assurance programs involving the routine testing of equipment performance using phantoms are limited in their ability to effectively carry out this task. To address this issue, the authors propose the implementation of an ongoing monitoring process that utilizes procedural data to identify unexpected large or small radiation exposures for individual patients, as well as to detect persistent changes in the radiation output of imaging platforms. METHODS: Data used in this study were obtained from records routinely collected during procedures performed in the cardiac catheterization imaging facility at St. Andrew's War Memorial Hospital, Brisbane, Australia, over the period January 2008-March 2010. A two stage monitoring process employing individual and exponentially weighted moving average (EWMA) control charts was developed and used to identify unexpectedly high or low radiation exposure levels for individual patients, as well as detect persistent changes in the radiation output delivered by the imaging systems. To increase sensitivity of the charts, we account for variation in dose area product (DAP) values due to other measured factors (patient weight, fluoroscopy time, and digital acquisition frame count) using multiple linear regression. Control charts are then constructed using the residual values from this linear regression. The proposed monitoring process was evaluated using simulation to model the performance of the process under known conditions. RESULTS: Retrospective application of this technique to actual clinical data identified a number of cases in which the DAP result could be considered unexpected. Most of these, upon review, were attributed to data entry errors. The charts monitoring the overall system radiation output trends demonstrated changes in equipment performance associated with relocation of the equipment to a new department. When tested under simulated conditions, the EWMA chart was capable of detecting a sustained 15% increase in average radiation output within 60 cases (<1 month of operation), while a 33% increase would be signaled within 20 cases. CONCLUSIONS: This technique offers a valuable enhancement to existing quality assurance programs in radiology that rely upon the testing of equipment radiation output at discrete time frames to ensure performance security.

Rate and correlates of tobacco treatment during a primary care visit for a largely urban and African American sample of smokers.

Introduction: Evidence-based treatments for tobacco use are under-utilized and primary care visits may be an opportune time to address this gap. This study examined the rate at which primary care visits included tobacco use treatment and examined patient demographics, smoking characteristics, attitudes about tobacco use treatments, and comorbidities as correlates of treatment provision. Methods: This prospective study assessed demographics, smoking characteristics, attitudes about tobacco use treatments, and comorbidities via interview prior to a primary care visit among 105 patients. One week following the appointment, 85 patients were reassessed for the tobacco use treatments they received during their appointment (i.e., asked about their tobacco use, advised to quit, and provided with a referral to a tobacco use treatment program or an FDA-approved tobacco use medication). Results: 93% of patients were asked about their tobacco use, 74% were advised to quit, 37% were provided with a referral for tobacco use treatment, and 27% received an FDA-approved medication (16% NRT, 11% varenicline or bupropion). Patients with higher quit motivation and who endorsed that medications can reduce cravings were more likely to report receiving tobacco use medication. Patients with a self-reported substance abuse history were less likely to report receiving tobacco use medications. Conclusions: The provision of tobacco use medications within primary care remains low. Strategies to increase patient quit motivation and help patients understand that tobacco use medications can mitigate cravings may increase use. Strategies may also be needed to ensure that patients with comorbid substance abuse still receive tobacco use treatments.

Assessing barriers to providing tobacco use disorder treatment in community mental health settings with a revised version of the Smoking Knowledge, Attitudes, and Practices (S-KAP) instrument.

BACKGROUND: Tobacco use disorder (TUD) rates are 2-3 times higher among people with serious mental illness (SMI) than the general population. Clinicians working in outpatient community mental health clinics are well positioned to provide TUD treatment to this group, but rates of treatment provision are very low. Understanding factors associated with the provision of TUD treatment by mental health clinicians is a priority. METHODS: This study used baseline data from an ongoing cluster-randomized clinical trial evaluating two approaches to training clinicians to increase TUD treatment. Following a psychometric assessment of our assessment tool, the Smoking Knowledge, Attitudes, and Practices (S-KAP) instrument, a new factor structure was evaluated utilizing confirmatory factor analysis. Structural equation modeling was then used to examine the associations between TUD treatment practices and clinician, setting, and patient characteristics in a sample of 182 mental health clinicians across 10 mental health clinics. RESULTS: Clinician but not setting or patient characteristics emerged as significant correlates of providing TUD treatment. Specifically, clinicians' general ethical commitment to providing TUD services and perceptions of their skills in providing this type of care were associated with providing TUD treatment. In contrast, clinician perceptions of patient motivation, anticipated quit rates, or available setting resources were not significantly associated with providing TUD treatment. CONCLUSIONS: Enhancing community mental health clinician TUD treatment skills and commitment to providing such services may reduce TUD rates among people with SMI. Future studies should evaluate interventions that target these factors.

Rationale and design of a randomized factorial clinical trial of pharmacogenetic and adherence optimization strategies to promote tobacco cessation among persons with HIV.

BACKGROUND: Tobacco use is approximately three times more common in people living with HIV (PLWH) than the general population. Moreover, current behavioral and pharmacological smoking cessation interventions are less effective for PLWH, highlighting a need for novel ways to optimize tobacco cessation treatments in this group. Prior research indicates that personalized treatment based on the nicotine metabolite ratio (NMR), a biomarker of nicotine metabolism, and augmenting smoking cessation medication adherence may improve cessation treatment for PLWH. METHODS: In this 2 × 2 factorial design trial, 488 smokers with HIV receive 12 weeks of smoking cessation medication along with randomization to 1) tailor the smoking cessation drug to their metabolism or not, and 2) provide additional counseling on smoking cessation medication adherence or not. Those randomized to the pharmacogenetic optimization arm receive varenicline or the nicotine patch based on their NMR (varenicline for fast metabolizers and the nicotine patch for slow metabolizers) and those in the control arm receive varenicline. Those randomized to the experimental adherence counseling arm receive Managed Problem Solving (MAPS) targeting their smoking cessation medication and those in the control arm receive standard counseling. CONCLUSION: PLWH on suppressive antiretroviral therapy who smoke lose more life-years due to tobacco use than to their HIV infection, and have lower response rates to current evidence-based treatments for smoking cessation. Both the NMR tailoring and MAPS interventions have the potential to optimize treatments for tobacco use among this population. If effective, this trial may demonstrate ways to further improve long-term health outcomes for PLWH.

NSC 622124 inhibits human Eg5 and other kinesins via interaction with the conserved microtubule-binding site.

Kinesin-5 proteins are essential for formation of a bipolar mitotic spindle in most and, perhaps all, eukaryotic cells. Several Kinesin-5 proteins, notably the human version, HsEg5, are targets of a constantly expanding group of small-molecule inhibitors, which hold promise both as tools for probing mechanochemical transduction and as anticancer agents. Although most such compounds are selective for HsEg5 and closely related Kinesin-5 proteins, some, such as NSC 622124, exhibit activity against at least one kinesin from outside the Kinesin-5 family. Here we show NSC 622124, despite identification in a screen that yielded inhibitors now known to target the HsEg5 monastrol-binding site, does not compete with [(14)C]monastrol for binding to HsEg5 and is able to inhibit the basal and microtubule-stimulated ATPase activity of the monastrol-insensitive Kinesin-5, KLP61F. NSC 622124 competes with microtubules, but not ATP, for interaction with HsEg5 and disrupts the microtubule binding of HsEg5, KLP61F, and Kinesin-1. Proteolytic degradation of an HsEg5.NSC622124 complex revealed that segments of the alpha3 and alpha5 helices map to the inhibitor-binding site. Overall, our results demonstrate that NSC 622124 targets the conserved microtubule-binding site of kinesin proteins. Further, unlike compounds previously reported to target the kinesin microtubule-binding site, NSC 622124 does not produce any enhancement of basal ATPase activity and thus acts solely as a negative regulator through interaction with a site traditionally viewed as a binding region for positive regulators (i.e., microtubules). Our work emphasizes the concept that microtubule-dependent motor proteins may be controlled at multiple sites by both positive and negative effectors.

Comparing heteroscedastic measurement systems with the probability of agreement.

Deciding whether two measurement systems agree well enough to be used interchangeably is important in medical and clinical contexts. Recently, the probability of agreement was proposed as an alternative to comparison techniques such as correlation, regression, and the limits of agreement approach, when the systems' measurement errors are homoscedastic. However, in medical and clinical contexts, it is common for measurement variability to increase proportionally with the magnitude of measurement. In this article, we extend the probability of agreement analysis to accommodate heteroscedastic measurement errors, demonstrating the versatility of this simple metric. We illustrate its use with two examples: one involving the comparison of blood pressure measurement devices, and the other involving the comparison of serum cholesterol assays.

The effect of temporal aggregation level in social network monitoring.

Social networks have become ubiquitous in modern society, which makes social network monitoring a research area of significant practical importance. Social network data consist of social interactions between pairs of individuals that are temporally aggregated over a certain interval of time, and the level of such temporal aggregation can have substantial impact on social network monitoring. There have been several studies on the effect of temporal aggregation in the process monitoring literature, but no studies on the effect of temporal aggregation in social network monitoring. We use the degree corrected stochastic block model (DCSBM) to simulate social networks and network anomalies and analyze these networks in the context of both count and binary network data. In conjunction with this model, we use the Priebe scan method as the monitoring method. We demonstrate that temporal aggregation at high levels leads to a considerable decrease in the ability to detect an anomaly within a specified time period. Moreover, converting social network communication data from counts to binary indicators can result in a significant loss of information, hindering detection performance. Aggregation at an appropriate level with count data, however, can amplify the anomalous signal generated by network anomalies and improve detection performance. Our results provide both insights on the practical effects of temporal aggregation and a framework for the study of other combinations of network models, surveillance methods, and types of anomalies.

Assessing agreement between two measurement systems: An alternative to the limits of agreement approach.

The comparison of two measurement systems is important in medical and other contexts. A common goal is to decide if a new measurement system agrees suitably with an existing one, and hence whether the two can be used interchangeably. Various methods for assessing interchangeability are available, the most popular being the limits of agreement approach due to Bland and Altman. In this article, we review the challenges of this technique and propose a model-based framework for comparing measurement systems that overcomes those challenges. The proposal is based on a simple metric, the probability of agreement, and a corresponding plot which can be used to summarize the agreement between two measurement systems. We also make recommendations for a study design that facilitates accurate and precise estimation of the probability of agreement.