Prediction of individual outcomes for asthma sufferers

Storlie, Curtis B.; Branda, Megan E.; Gionfriddo, Michael R.; Shah, Nilay D.; Rank, Matthew A.

doi:10.1093/biostatistics/kxx055

Cited by 3 publications

(3 citation statements)

References 16 publications

(17 reference statements)

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“…The O-U process was evaluated on a month grid, using observed weight when available, thus making it equivalent to an autoregressive (AR(1)) model. 48 Estimation and imputation for all variables were carried out simultaneously via a Bayesian computational framework (rjags package in R). To assess the performance of the weight imputation model, a cross-validation was run by predicting weight at diagnosis for 20% of women with a known weight.…”

Section: Methodsmentioning

confidence: 99%

Obesity and survival among a cohort of breast cancer patients is partially mediated by tumor characteristics

et al. 2019

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Obesity exerts adverse effects on breast cancer survival, but the means have not been fully elucidated. We evaluated obesity as a contributor to breast cancer survival according to tumor molecular subtypes in a population-based case–cohort study using data from the Surveillance Epidemiology and End Results (SEER) program. We determined whether obese women were more likely to be diagnosed with poor prognosis tumor characteristics and quantified the contribution of obesity to survival. Hazard ratios (HRs) and 95% confidence intervals (CI) were calculated via Cox multivariate models. The effect of obesity on survival was evaluated among 859 incident breast cancers (subcohort; 15% random sample; median survival 7.8 years) and 697 deaths from breast cancer (cases; 100% sample). Obese women had a 1.7- and 1.8-fold increased risk of stage III/IV disease and grade 3/4 tumors, respectively. Obese women with Luminal A- and Luminal B-like breast cancer were 1.8 (95% CI 1.3–2.5) and 2.2 (95% CI 0.9–5.0) times more likely to die from their cancer compared to normal weight women. In mediation analyses, the proportion of excess mortality attributable to tumor characteristics was 36.1% overall and 41% and 38% for Luminal A- and Luminal B-like disease, respectively. Obesity was not associated with breast cancer-specific mortality among women who had Her2-overexpressing or triple-negative tumors. Obesity may influence hormone-positive breast cancer-specific mortality in part through fostering poor prognosis tumors. When tumor biology is considered as part of the causal pathway, the public health impact of obesity on breast cancer survival may be greater than previously estimated.

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Section: Methodsmentioning

confidence: 99%

Obesity and survival among a cohort of breast cancer patients is partially mediated by tumor characteristics

et al. 2019

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“…This is further complicated with five responses due to the multivariate nature of the random effects. However, the Markov chain Monte Carlo method is effective at fitting such models [103] and will be used here. We will assess whether there are any large imbalances in patient characteristics among clusters between the baseline period and intervention period and include any variables wherein large imbalances are detected in the model.…”

Section: Statistical Methods For Primary and Secondary Outcomes {20a}mentioning

confidence: 99%

Pragmatic cluster randomized trial to evaluate effectiveness and implementation of enhanced EHR-facilitated cancer symptom control (E2C2)

Rutten

Ruddy

Chlan

et al. 2020

Trials

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Background: The prevalence of inadequate symptom control among cancer patients is quite high despite the availability of definitive care guidelines and accurate and efficient assessment tools. Methods: We will conduct a hybrid type 2 stepped wedge pragmatic cluster randomized clinical trial to evaluate a guideline-informed enhanced, electronic health record (EHR)-facilitated cancer symptom control (E2C2) care model. Teams of clinicians at five hospitals that care for patients with various cancers will be randomly assigned in steps to the E2C2 intervention. The E2C2 intervention will have two levels of care: level 1 will offer low-touch, automated self-management support for patients reporting moderate sleep disturbance, pain, anxiety, depression, and energy deficit symptoms or limitations in physical function (or both). Level 2 will offer nurse-managed collaborative care for patients reporting more intense (severe) symptoms or functional limitations (or both). By surveying and interviewing clinical staff, we will also evaluate whether the use of a multifaceted, evidence-based implementation strategy to support adoption and use of the E2C2 technologies improves patient and clinical outcomes. Finally, we will conduct a mixed methods evaluation to identify disparities in the adoption and implementation of the E2C2 intervention among elderly and rural-dwelling patients with cancer. Discussion: The E2C2 intervention offers a pragmatic, scalable approach to delivering guideline-based symptom and function management for cancer patients. Since discrete EHR-imbedded algorithms drive defining aspects of the intervention, the approach can be efficiently disseminated and updated by specifying and modifying these centralized EHR algorithms.

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“…This is manifested in exacerbation rate (or risk) being the primary end-point in the majority of clinical trials in asthma. Common analytic approaches include marginal models [7][8][9], modeling time to the first exacerbation in a survival analysis framework [10], using models for count outcomes [11,12], or employing models for recurrent events [13,14]. Recent developments in the analysis of exacerbation trials include the use of random effect models to account for between-individual variability in exacerbation rates [13,14], and the use of a mixture of a random effect or a gap-time model with a logistic regression model to allow for the excessive presence of individuals without any exacerbations ("zero inflation") during follow-up [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases

Safari

Petkau

FitzGerald

et al. 2023

BMC Med Inform Decis Mak

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Background The natural history of many chronic diseases is characterized by periods of increased disease activity, commonly referred to as flare-ups or exacerbations. Accurate characterization of the burden of these exacerbations is an important research objective. Methods The purpose of this work was to develop a statistical framework for nuanced characterization of the three main features of exacerbations: their rate, duration, and severity, with interrelationships among these features being a particular focus. We jointly specified a zero-inflated accelerated failure time regression model for the rate, an accelerated failure time regression model for the duration, and a logistic regression model for the severity of exacerbations. Random effects were incorporated into each component to capture heterogeneity beyond the variability attributable to observed characteristics, and to describe the interrelationships among these components. Results We used pooled data from two clinical trials in asthma as an exemplary application to illustrate the utility of the joint modeling approach. The model fit clearly indicated the presence of heterogeneity in all three components. A novel finding was that the new therapy reduced not just the rate but also the duration of exacerbations, but did not have a significant impact on their severity. After controlling for covariates, exacerbations among more frequent exacerbators tended to be shorter and less likely to be severe. Conclusions We conclude that a joint modeling framework, programmable in available software, can provide novel insights about how the rate, duration, and severity of episodic events interrelate, and enables consistent inference on the effect of treatments on different disease outcomes. Trial registration Ethics approval was obtained from the University of British Columbia Human Ethics Board (H17-00938).

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Prediction of individual outcomes for asthma sufferers

Cited by 3 publications

References 16 publications

Obesity and survival among a cohort of breast cancer patients is partially mediated by tumor characteristics

Obesity and survival among a cohort of breast cancer patients is partially mediated by tumor characteristics

Pragmatic cluster randomized trial to evaluate effectiveness and implementation of enhanced EHR-facilitated cancer symptom control (E2C2)

A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases

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