Precision medicine analysis of heterogeneity in individual-level treatment response to amyloid beta removal in early Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) is a neurological disorder with variability in pathology and clinical progression. AD patients may differ in individual-level benefit from amyloid beta removal therapy. METHODS: Random forest models were applied to the EMERGE trial to create an individual-level treatment response (ITR) score which represents individual-level benefit of high-dose aducanumab relative to the placebo. This ITR score was used to test the existence of heterogeneity in treatment effect (HTE). RESULTS: We found statistical evidence of HTE in the Clinical Dementia Rating-Sum of Boxes (CDR-SB;P =  0.034). The observed CDR-SB benefit was 0.79 points greater in the group with the top 25% of ITR score compared to the remaining 75% (P = 0.020). Of note, the highest treatment responders had lower hippocampal volume, higher plasma phosphorylated tau 181 and a shorter duration of clinical AD at baseline. DISCUSSION: This ITR analysis provides a proof of concept for precision medicine in future AD research and drug development. HIGHLIGHTS: Emerging trials have shown a population-level benefit from amyloid beta (Aß) removal in slowing cognitive decline in early Alzheimer's disease (AD). This work demonstrates significant heterogeneity of individual-level treatment effect of aducanumab in early AD. The greatest clinical responders to Aß removal therapy have a pattern of more severe neurodegenerative process.

APOEε4 potentiates the relationship between amyloid-ß and tau pathologies.

APOEε4 is the most well-established genetic risk factor for sporadic Alzheimer's disease and is associated with cerebral amyloid-ß. However, the association between APOEε4 and tau pathology, the other major proteinopathy of Alzheimer's disease, has been controversial. Here, we sought to determine whether the relationship between APOEε4 and tau pathology is determined by local interactions with amyloid-ß. We examined three independent samples of cognitively unimpaired, mild cognitive impairment and Alzheimer's disease subjects: (1) 211 participants who underwent tau-PET with [18F]MK6240 and amyloid-PET with [18F]AZD4694, (2) 264 individuals who underwent tau-PET with [18F]Flortaucipir and amyloid-PET with [18F]Florbetapir and (3) 487 individuals who underwent lumbar puncture and amyloid-PET with [18F]Florbetapir. Using a novel analytical framework, we applied voxel-wise regression models to assess the interactive effect of APOEε4 and amyloid-ß on tau load, independently of age and clinical diagnosis. We found that the interaction effect between APOEε4 and amyloid-ß, rather than the sum of their independent effects, was related to increased tau load in Alzheimer's disease-vulnerable regions. The interaction between one APOEε4 allele and amyloid-ß was related to increased tau load, while the interaction between amyloid-ß and two APOEε4 alleles was related to a more widespread pattern of tau aggregation. Our results contribute to an emerging framework in which the elevated risk of developing dementia conferred by APOEε4 genotype involves mechanisms associated with both amyloid-ß and tau aggregation. These results may have implications for future disease-modifying therapeutic trials targeting amyloid or tau pathologies.

Diagnostic performance and prediction of clinical progression of plasma phospho-tau181 in the Alzheimer's Disease Neuroimaging Initiative.

Whilst cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers for amyloid-ß (Aß) and tau pathologies are accurate for the diagnosis of Alzheimer's disease (AD), their broad implementation in clinical and trial settings are restricted by high cost and limited accessibility. Plasma phosphorylated-tau181 (p-tau181) is a promising blood-based biomarker that is specific for AD, correlates with cerebral Aß and tau pathology, and predicts future cognitive decline. In this study, we report the performance of p-tau181 in >1000 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including cognitively unimpaired (CU), mild cognitive impairment (MCI) and AD dementia patients characterized by Aß PET. We confirmed that plasma p-tau181 is increased at the preclinical stage of Alzheimer and further increases in MCI and AD dementia. Individuals clinically classified as AD dementia but having negative Aß PET scans show little increase but plasma p-tau181 is increased if CSF Aß has already changed prior to Aß PET changes. Despite being a multicenter study, plasma p-tau181 demonstrated high diagnostic accuracy to identify AD dementia (AUC = 85.3%; 95% CI, 81.4-89.2%), as well as to distinguish between Aß- and Aß+ individuals along the Alzheimer's continuum (AUC = 76.9%; 95% CI, 74.0-79.8%). Higher baseline concentrations of plasma p-tau181 accurately predicted future dementia and performed comparably to the baseline prediction of CSF p-tau181. Longitudinal measurements of plasma p-tau181 revealed low intra-individual variability, which could be of potential benefit in disease-modifying trials seeking a measurable response to a therapeutic target. This study adds significant weight to the growing body of evidence in the use of plasma p-tau181 as a non-invasive diagnostic and prognostic tool for AD, regardless of clinical stage, which would be of great benefit in clinical practice and a large cost-saving in clinical trial recruitment.

Survival analysis under the Cox proportional hazards model with pooled covariates.

For a continuous time-to-event outcome and an expensive-to-measure exposure, we develop a pooling design and propose a likelihood-based approach to estimate the hazard ratios (HRs) of a Cox proportional hazards (PH) model. Our proposed approach fits a PH model based on pooled exposures with individually observed time-to-event outcomes. The design and estimation exploits the equivalence of the conditional logistic likelihood functions arising from a matched case-control study and the partial likelihood function of a riskset-matched, nested case-control (NCC) subset of a cohort study. To create the pools, we first focus on an NCC subcohort. Pools are formed at random while keeping the matching intact. Pool-level exposure and confounders are then evaluated and used in the likelihood to estimate the HR and the standard error of the estimates. The estimators are MLEs, provide consistent estimates of the individual-level HRs, and are asymptotically normal. Our simulation results indicate that the pooled estimates are comparable to the estimates obtained from the NCC subcohort. The units of analysis for the pooled design are the pools and not the individual participants. Hence the effective sample size is reduced. Therefore, the variance of the HR estimate increases with increasing poolsize. However, this variance inflation in small samples can be offset by including more matched controls per case within the NCC subcohort. An application is demonstrated with the Second Manifestations of ARTerial disease (SMART) study.

Inference about time-dependent prognostic accuracy measures in the presence of competing risks.

BACKGROUND: Evaluating a candidate marker or developing a model for predicting risk of future conditions is one of the major goals in medicine. However, model development and assessment for a time-to-event outcome may be complicated in the presence of competing risks. In this manuscript, we propose a local and a global estimators of cause-specific AUC for right-censored survival times in the presence of competing risks. METHODS: The local estimator - cause-specific weighted mean rank (cWMR) - is a local average of time-specific observed cause-specific AUCs within a neighborhood of given time t. The global estimator - cause-specific fractional polynomials (cFPL) - is based on modelling the cause-specific AUC as a function of t through fractional polynomials. RESULTS: We investigated the performance of the proposed cWMR and cFPL estimators through simulation studies and real-life data analysis. The estimators perform well in small samples, have minimal bias and appropriate coverage. CONCLUSIONS: The local estimator cWMR and the global estimator cFPL will provide computationally efficient options for assessing the prognostic accuracy of markers for time-to-event outcome in the presence of competing risks in many practical settings.

L'infirmière pivot associée à une expérience de soins oncologiques positive et à une satisfaction accrue des patients.

CONTEXTE ET OBJECTIFS: Un nombre croissant de résultats probants indique que l'infirmière pivot en oncologie (IPO) joue un rôle de premier plan dans l'optimisation des processus et des résultats de soins. Il faudra toutefois mener des études d'envergure pour comparer les perceptions de l'expérience de soins liés au cancer des patients traités par des infirmières pivots et de ceux qui n'ont pas bénéficié de ce type de suivi. MÉTHODOLOGIE: Des participants (N = 2 858) traités au cours des six derniers mois dans un centre de traitement du cancer situé à Montréal, au Québec, et affilié à une université ont répondu au sondage sur la satisfaction des patients en traitement anticancéreux ambulatoire, c'est-à-dire le Ambulatory Oncology Patient Satisfaction Survey (AOPSS). RÉSULTATS: L'expérience des soins oncologiques était significativement plus positives et la satisfaction plus élevée dans le groupe suivi par une infirmière pivot (n = 2 003) pour les six domaines de soins (différences moyennes de 3,32 à 8,95) et les quatre fonctions infirmières (différences moyennes de 5,64 à 10,39), comparativement au groupe sans IPO (n = 855). DISCUSSION: L'infirmière pivot joue un rôle important dans l'amélioration de l'expérience de soins et la satisfaction des patients. Les recherches futures devront explorer les potentielles relations de cause à effet entre les infirmières pivots, les processus de soins et les résultats des patients.

The nurse pivot-navigator associated with more positive cancer care experiences and higher patient satisfaction.

BACKGROUND AND OBJECTIVES: Growing evidence indicates that the nurse navigator-pivot (NN), is key to optimizing care processes and outcomes. However, large scale studies are needed to examine how patients exposed to NNs (as opposed to non-NN) differentially perceived their cancer care experiences. METHOD: Participants (N = 2,858) treated for cancer in the last six months at university-affiliated cancer centres in Montréal, Québec, completed the Ambulatory Oncology Patient Satisfaction Survey (AOPSS). RESULTS: Cancer care experiences and satisfaction were significantly higher in the NN group (n = 2,003) for all six care domains (Ds from 3.32 to 8.95) and all four nursing functions (Ds from 5.64 to 10.39) when compared to the non-NN group (n = 855). DISCUSSION: The NN role is significantly related to enhanced cancer care experiences and higher patient satisfaction. Future research should explore potential causal effects between NNs and care processes, as well as patient outcomes.

Novel clinical scores for occupational asthma due to exposure to high-molecular-weight agents.

OBJECTIVE: Specific inhalation challenge (SIC) as the reference diagnostic test for occupational asthma (OA) is not widely available worldwide. We aimed to develop non-SIC-based models for OA. METHODS: Of 427 workers who were exposed to high-molecular-weight agents and referred to OA clinic at Montréal Sacré-Cœur Hospital between 1983 and 2016, we analysed 160 workers who completed non-specific bronchial hyper-responsiveness (NSBHR) tests and still worked 1 month before SIC. OA was defined as positive SIC. Logistic regression models were developed. The accuracy of the models was quantified using calibration and discrimination measures. Their internal validity was evaluated with bootstrapping procedures. The final models were translated into clinical scores and stratified into probability groups. RESULTS: The final model, which included age ≤40 years, rhinoconjunctivitis, inhaled corticosteroid use, agent type, NSBHR, and work-specific sensitisation had a reasonable internal validity. The area under the receiver operating characteristics curve (AUC) was 0.91 (95% CI 0.86 to 0.95), statistically significantly higher than the combination of positive NSBHR and work-specific sensitisation (AUC=0.84). The top 70% of the clinical scores (ie, the high probability group) showed a significantly higher sensitivity (96.4%vs86.9%) and negative predictive value (93.6%vs84.1%) than the combination of positive NSBHR and work-specific sensitisation (p value <0.001). CONCLUSIONS: We developed novel scores for OA induced by high-molecular-weight agents with excellent discrimination. It could be helpful for secondary-care physicians who have access to pulmonary function test and allergy testing in identifying subjects at a high risk of having OA and in deciding on appropriate referral to a tertiary centre.

Time-dependent classification accuracy curve under marker-dependent sampling.

Evaluating the classification accuracy of a candidate biomarker signaling the onset of disease or disease status is essential for medical decision making. A good biomarker would accurately identify the patients who are likely to progress or die at a particular time in the future or who are in urgent need for active treatments. To assess the performance of a candidate biomarker, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are commonly used. In many cases, the standard simple random sampling (SRS) design used for biomarker validation studies is costly and inefficient. In order to improve the efficiency and reduce the cost of biomarker validation, marker-dependent sampling (MDS) may be used. In a MDS design, the selection of patients to assess true survival time is dependent on the result of a biomarker assay. In this article, we introduce a nonparametric estimator for time-dependent AUC under a MDS design. The consistency and the asymptotic normality of the proposed estimator is established. Simulation shows the unbiasedness of the proposed estimator and a significant efficiency gain of the MDS design over the SRS design.

Novel likelihood ratio tests for screening gene-gene and gene-environment interactions with unbalanced repeated-measures data.

There has been extensive literature on modeling gene-gene interaction (GGI) and gene-environment interaction (GEI) in case-control studies with limited literature on statistical methods for GGI and GEI in longitudinal cohort studies. We borrow ideas from the classical two-way analysis of variance literature to address the issue of robust modeling of interactions in repeated-measures studies. While classical interaction models proposed by Tukey and Mandel have interaction structures as a function of main effects, a newer class of models, additive main effects and multiplicative interaction (AMMI) models, do not have similar restrictive assumptions on the interaction structure. AMMI entails a singular value decomposition of the cell residual matrix after fitting the additive main effects and has been shown to perform well across various interaction structures. We consider these models for testing GGI and GEI from two perspectives: likelihood ratio test based on cell means and a regression-based approach using individual observations. Simulation results indicate that both approaches for AMMI models lead to valid tests in terms of maintaining the type I error rate, with the regression approach having better power properties. The performance of these models was evaluated across different interaction structures and 12 common epistasis patterns. In summary, AMMI model is robust with respect to misspecified interaction structure and is a useful screening tool for interaction even in the absence of main effects. We use the proposed methods to examine the interplay between the hemochromatosis gene and cumulative lead exposure on pulse pressure in the Normative Aging Study.

Privacy-preserving analysis of time-to-event data under nested case-control sampling.

Analyses of distributed data networks of rare diseases are constrained by legitimate privacy and ethical concerns. Analytical centers (e.g. research institutions) are thus confronted with the challenging task of obtaining data from recruiting sites that are often unable or unwilling to share personal records of participants. For time-to-event data, recently popularized disclosure techniques with privacy guarantees (e.g., Differentially Private Generative Adversarial Networks) are generally computationally expensive or inaccessible to applied researchers. To perform the widely used Cox proportional hazards regression, we propose an easy-to-implement privacy-preserving data analysis technique by pooling (i.e. aggregating) individual records of covariates at recruiting sites under the nested case-control sampling framework before sharing the pooled nested case-control subcohort. We show that the pooled hazard ratio estimators, under the pooled nested case-control subsamples from the contributing sites, are maximum likelihood estimators and provide consistent estimates of the individual level full cohort HRs. Furthermore, a sampling technique for generating pseudo-event times for individual subjects that constitute the pooled nested case-control subsamples is proposed. Our method is demonstrated using extensive simulations and analysis of the National Lung Screening Trial data. The utility of our proposed approach is compared to the gold standard (full cohort) and synthetic data generated using classification and regression trees. The proposed pooling technique performs to near-optimal levels comparable to full cohort analysis or synthetic data; the efficiency improves in rare event settings when more controls are matched on during nested case-control subcohort sampling.

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities.

Importance: Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-ß-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software. Objective: To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA. Design, Setting, and Participants: This diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted. The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted). The study encompassed 199 retrospective cases, where each case consisted of a predosing baseline and a postdosing follow-up MRI of patients from aducanumab clinical trials PRIME, EMERGE, and ENGAGE. Statistical analysis was performed from April to July 2023. Exposures: Use of icobrain aria, an AI-based assistive software for ARIA detection and quantification. Main Outcomes and Measures: Coprimary end points were the difference in diagnostic accuracy between assisted and unassisted detection of ARIA-E (edema and/or sulcal effusion) and ARIA-H (microhemorrhage and/or superficial siderosis) independently, assessed with the area under the receiver operating characteristic curve (AUC). Results: Among the 199 participants included in this study of radiological reading performance, mean (SD) age was 70.4 (7.2) years; 105 (52.8%) were female; 23 (11.6%) were Asian, 1 (0.5%) was Black, 157 (78.9%) were White, and 18 (9.0%) were other or unreported race and ethnicity. Among the 16 radiological readers included, 2 were specialized neuroradiologists (12.5%), 11 were male individuals (68.8%), 7 were individuals working in academic hospitals (43.8%), and they had a mean (SD) of 9.5 (5.1) years of experience. Radiologists assisted by the software were significantly superior in detecting ARIA than unassisted radiologists, with a mean assisted AUC of 0.87 (95% CI, 0.84-0.91) for ARIA-E detection (AUC improvement of 0.05 [95% CI, 0.02-0.08]; P = .001]) and 0.83 (95% CI, 0.78-0.87) for ARIA-H detection (AUC improvement of 0.04 [95% CI, 0.02-0.07]; P = .001). Sensitivity was significantly higher in assisted reading compared with unassisted reading (87% vs 71% for ARIA-E detection; 79% vs 69% for ARIA-H detection), while specificity remained above 80% for the detection of both ARIA types. Conclusions and Relevance: This diagnostic study found that radiological reading performance for ARIA detection and diagnosis was significantly better when using the AI-based assistive software. Hence, the software has the potential to be a clinically important tool to improve safety monitoring and management of patients with AD treated with amyloid-ß-directed monoclonal antibody therapies.

Specimen pooling for efficient use of biospecimens in studies of time to a common event.

For case-control studies that rely on expensive assays for biomarkers, specimen pooling offers a cost-effective and efficient way to estimate individual-level odds ratios. Pooling helps to conserve irreplaceable biospecimens for the future, mitigates limit-of-detection problems, and enables inclusion of individuals who have limited available volumes of biospecimen. Pooling can also allow the study of a panel of biomarkers under a fixed assay budget. Here, we extend this method for application to discrete-time survival studies. Assuming a proportional odds logistic model for risk of a common outcome, we propose a design strategy that forms pooling sets within those experiencing the outcome at the same event time. We show that the proposed design enables a cost-effective analysis to assess the association of a biomarker with the outcome. Because the standard likelihood is slightly misspecified for the proposed pooling strategy under a nonnull biomarker effect, the proposed approach produces slightly biased estimates of exposure odds ratios. We explore the extent of this bias via simulations and illustrate the method by revisiting a data set relating polychlorinated biphenyls and 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene to time to pregnancy.

Amyloid beta plaque accumulation with longitudinal [18F]AZD4694 PET.

Introduction: [18F]AZD4694 is an amyloid beta (Aß) imaging agent used in several observational studies and clinical trials. However, no studies have yet published data on longitudinal Aß accumulation measured with [18F]AZD4694. Methods: We assessed 146 individuals who were evaluated with [18F]AZD4694 at baseline and 2-year follow-up. We calculated annual rates of [18F]AZD4694 change for clinically defined and biomarker-defined groups. Results: Cognitively unimpaired (CU) older adults displayed subtle [18F]AZD4694 standardized uptake value ratio (SUVR) accumulation over the follow-up period. In contrast, Aß positive CU older adults displayed higher annual [18F]AZD4694 SUVR increases. [18F]AZD4694 SUVR accumulation in Aß positive mild cognitive impairment (MCI) and dementia was modest across the neocortex. Discussion: Larger increases in [18F]AZD4694 SUVR were observed in CU individuals who had abnormal amyloid positron emission tomography levels at baseline. [18F]AZD4694 can be used to monitor Aß levels in therapeutic trials as well as clinical settings, particularly prior to initiating anti-amyloid therapies.

Association of Phosphorylated Tau Biomarkers With Amyloid Positron Emission Tomography vs Tau Positron Emission Tomography.

Importance: The recent proliferation of phosphorylated tau (p-tau) biomarkers has raised questions about their preferential association with the hallmark pathologies of Alzheimer disease (AD): amyloid-ß plaques and tau neurofibrillary tangles. Objective: To determine whether cerebrospinal fluid (CSF) and plasma p-tau biomarkers preferentially reflect cerebral ß-amyloidosis or neurofibrillary tangle aggregation measured with positron emission tomography (PET). Design, Setting, and Participants: This was a cross-sectional study of 2 observational cohorts: the Translational Biomarkers in Aging and Dementia (TRIAD) study, with data collected between October 2017 and August 2021, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), with data collected between September 2015 and November 2019. TRIAD was a single-center study, and ADNI was a multicenter study. Two independent subsamples were derived from TRIAD. The first TRIAD subsample comprised individuals assessed with CSF p-tau (p-tau181, p-tau217, p-tau231, p-tau235), [18F]AZD4694 amyloid PET, and [18F]MK6240 tau PET. The second TRIAD subsample included individuals assessed with plasma p-tau (p-tau181, p-tau217, p-tau231), [18F]AZD4694 amyloid PET, and [18F]MK6240 tau PET. An independent cohort from ADNI comprised individuals assessed with CSF p-tau181, [18F]florbetapir PET, and [18F]flortaucipir PET. Participants were included based on the availability of p-tau and PET biomarker assessments collected within 9 months of each other. Exclusion criteria were a history of head trauma or magnetic resonance imaging/PET safety contraindications. No participants who met eligibility criteria were excluded. Exposures: Amyloid PET, tau PET, and CSF and plasma assessments of p-tau measured with single molecule array (Simoa) assay or enzyme-linked immunosorbent assay. Main Outcomes and Measures: Associations between p-tau biomarkers with amyloid PET and tau PET. Results: A total of 609 participants (mean [SD] age, 66.9 [13.6] years; 347 female [57%]; 262 male [43%]) were included in the study. For all 4 phosphorylation sites assessed in CSF, p-tau was significantly more closely associated with amyloid-PET values than tau-PET values (p-tau181 difference, 13%; 95% CI, 3%-22%; P = .006; p-tau217 difference, 11%; 95% CI, 3%-20%; P = .003; p-tau231 difference, 15%; 95% CI, 5%-22%; P < .001; p-tau235 difference, 9%; 95% CI, 1%-19%; P = .02) . These results were replicated with plasma p-tau181 (difference, 11%; 95% CI, 1%-22%; P = .02), p-tau217 (difference, 9%; 95% CI, 1%-19%; P = .02), p-tau231 (difference, 13%; 95% CI, 3%-24%; P = .009), and CSF p-tau181 (difference, 9%; 95% CI, 1%-21%; P = .02) in independent cohorts. Conclusions and Relevance: Results of this cross-sectional study of 2 observational cohorts suggest that the p-tau abnormality as an early event in AD pathogenesis was associated with amyloid-ß accumulation and highlights the need for careful interpretation of p-tau biomarkers in the context of the amyloid/tau/neurodegeneration, or A/T/(N), framework.

Model-Based ROC Curve: Examining the Effect of Case Mix and Model Calibration on the ROC Plot.

BACKGROUND: The performance of risk prediction models is often characterized in terms of discrimination and calibration. The receiver-operating characteristic (ROC) curve is widely used for evaluating model discrimination. However, when comparing ROC curves across different samples, the effect of case mix makes the interpretation of discrepancies difficult. Further, compared with model discrimination, evaluating model calibration has not received the same level of attention. Current methods for examining model calibration require specification of smoothing or grouping factors. METHODS: We introduce the "model-based" ROC curve (mROC) to assess model calibration and the effect of case mix during external validation. The mROC curve is the ROC curve that should be observed if the prediction model is calibrated in the external population. We show that calibration-in-the-large and the equivalence of mROC and ROC curves are together sufficient conditions for the model to be calibrated. Based on this, we propose a novel statistical test for calibration that, unlike current methods, does not require any subjective specification of smoothing or grouping factors. RESULTS: Through a stylized example, we demonstrate how mROC separates the effect of case mix and model miscalibration when externally validating a risk prediction model. We present the results of simulation studies that confirm the properties of the new calibration test. A case study on predicting the risk of acute exacerbations of chronic obstructive pulmonary disease puts the developments in a practical context. R code for the implementation of this method is provided. CONCLUSION: mROC can easily be constructed and used to interpret the effect of case mix and calibration on the ROC plot. Given the popularity of ROC curves among applied investigators, this framework can further promote assessment of model calibration. HIGHLIGHTS: Compared with examining model discrimination, examining model calibration has not received the same level of attention among investigators who develop or examine risk prediction models.This article introduces the model-based ROC (mROC) curve as the basis for graphical and statistical examination of model calibration on the ROC plot.This article introduces a formal statistical test based on mROC for examining model calibration that does not require arbitrary smoothing or grouping factors.Investigators who develop or validate risk prediction models can now also use the popular ROC plot for examining model calibration, as a critical but often neglected component in predictive analytics.

Feasibility, Acceptability, and Clinical Significance of a Dyadic, Web-Based, Psychosocial and Physical Activity Self-Management Program (TEMPO) Tailored to the Needs of Men with Prostate Cancer and Their Caregivers: A Multi-Center Randomized Pilot Trial.

BACKGROUND: Prostate cancer is the most common cancer diagnosis among men. Family caregivers (often female spouses) play a key role in ensuring patients' needs are met, frequently assuming their role with no formal training, which can contribute to a high burden. The purpose of this study was to pilot TEMPO-the first dyadic, Tailored, wEb-based, psychosocial and physical activity self-Management PrOgram for men with prostate cancer and their caregivers. METHODS: 49 men with prostate cancer and their caregivers were randomized to TEMPO or usual care. Baseline and follow-up questionnaires were completed to assess feasibility, acceptability, and clinical significance. A priori benchmarks for these outcomes were set. Thirteen exit interviews were conducted to further explore acceptability. RESULTS: Feasibility benchmarks were met with the exception for recruitment with on average 6.1 dyads recruited/month (benchmark: 8 dyads/month). Benchmarks of acceptability focused on attrition (<25%) and system usability, which were met. Using the strict criteria for adherence of 100% of the module viewed and participants spending at least 15 min on the module, 45% of participants were adherent. The clinical significance on anxiety and quality of life was supported for caregivers, and mostly supported for the men with prostate cancer. CONCLUSION: This pilot trial was successful, with minor modifications needed prior to a large trial.