The Association Between Rate and Severity of Exacerbations in Chronic Obstructive Pulmonary Disease: An Application of a Joint Frailty-Logistic Model

Sadatsafavi, Mohsen; Sin, Don D.; Zafarí, Zafar; Criner, Gerard J.; Connett, John E.; Lazarus, Stephen C.; Han, MeiLan K.; Martínez, Fernando J.; Albert, Richard K.

doi:10.1093/aje/kww085

Cited by 34 publications

(33 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A previous analysis demonstrated that the pattern of exacerbation occurrence was consistent with a constant hazard over time. 24 Fewer than 4% of individuals died and a previous analysis showed little impact from the competing risk of death on the statistical inference on exacerbation rate. 24 As the treatment is randomized, there is no structural confounding.…”

Section: Application: a Clinical Trial Of Preventive Antibiotic Theramentioning

confidence: 93%

“…24 Fewer than 4% of individuals died and a previous analysis showed little impact from the competing risk of death on the statistical inference on exacerbation rate. 24 As the treatment is randomized, there is no structural confounding. The possibility of chance confounding (noticeable imbalance of covariates due to randomization 25 ) was ruled out in an exploratory analysis (the standardized mean difference between treatment groups was less than 5% for all covariates).…”

Section: Application: a Clinical Trial Of Preventive Antibiotic Theramentioning

confidence: 93%

See 1 more Smart Citation

A threshold‐free summary index for quantifying the capacity of covariates to yield efficient treatment rules

Sadatsafavi

Mansournia

Gustafson

2020

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

When data on treatment assignment, outcomes, and covariates from a randomized trial are available, a question of interest is to what extent covariates can be used to optimize treatment decisions. Statistical hypothesis testing of covariate‐by‐treatment interaction is ill‐suited for this purpose. The application of decision theory results in treatment rules that compare the expected benefit of treatment given the patient's covariates against a treatment threshold. However, determining treatment threshold is often context‐specific, and any given threshold might seem arbitrary when the overall capacity towards predicting treatment benefit is of concern. We propose the Concentration of Benefit index (Cb), a threshold‐free metric that quantifies the combined performance of covariates towards finding individuals who will benefit the most from treatment. The construct of the proposed index is comparing expected treatment outcomes with and without knowledge of covariates when one of a two randomly selected patients is to be treated. We show that the resulting index can also be expressed in terms of the integrated efficiency of individualized treatment decision over the entire range of treatment thresholds. We propose parametric and semiparametric estimators, the latter being suitable for out‐of‐sample validation and correction for optimism. We used data from a clinical trial to demonstrate the calculations in a step‐by‐step fashion. The proposed index has intuitive and theoretically sound interpretation and can be estimated with relative ease for a wide class of regression models. Beyond the conceptual developments, various aspects of estimation and inference for such a metric need to be pursued in future research. R code that implements the method for a variety of regression models is provided at (https://github.com/msadatsafavi/txBenefit).

show abstract

Section: Application: a Clinical Trial Of Preventive Antibiotic Theramentioning

confidence: 93%

Section: Application: a Clinical Trial Of Preventive Antibiotic Theramentioning

confidence: 93%

A threshold‐free summary index for quantifying the capacity of covariates to yield efficient treatment rules

Sadatsafavi

Mansournia

Gustafson

2020

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…Empirical evidence supports the presence of such heterogeneity(33). The extent of such heterogeneity was based on our previous work using data from the MACRO study(34). This heterogeneity was modeled through individual-specific (random-effect) intercepts for log-hazard of exacerbation rate and log-odds of exacerbation severity ( Table 1 )(34).…”

Section: Methodsmentioning

confidence: 99%

Development and validation of the Evaluation Platform In COPD (EPIC): a population-based outcomes model of COPD for Canada

Sadatsafavi

Ghanbarian

Adibi

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

BackgroundWe report the development, validation, and implementation of an open-source population-based outcomes model of Chronic Obstructive Pulmonary Disease (COPD) for Canada.MethodsEvaluation Platform in COPD (EPIC) is a discrete event simulation model of Canadians 40 years of age or older. Three core features of EPIC are its open-population design (incorporating projections of future population growth, aging, and smoking trends), its incorporation of heterogeneity in lung function decline and burden of exacerbations, and its modeling of the natural history of COPD from inception. Multiple original data analyses, as well as values reported in the literature, were used to populate the model. Extensive face validity as well as internal and external validity evaluations were performed.ResultsThe model was internally validated on demographic projections, mortality rates, lung function trajectories, COPD exacerbations, and stability of COPD prevalence over time within strata of risk factors. In external validation, it moderately overestimated rate of overall exacerbations in two independent trials, but generated consistent estimates of rate of severe exacerbations and mortality.LimitationsIn its current version, EPIC does not consider uncertainty in the evidence. Several components such as additional (e.g., environmental and occupational) risk factors, treatment, symptoms, and comorbidity will have to be added in future iterations.ConclusionsEPIC is the first multi-purpose outcome- and policy-focused model of COPD for Canada. By modeling the natural history of COPD from its inception, it is capable of modeling the outcomes of decisions across the entire care pathway of COPD. Platforms of this type have the capacity to be iteratively updated to incorporate the latest evidence and to project the outcomes of many different scenarios within a consistent framework.

show abstract

“…It is increasingly recognized that COPD is a heterogeneous disease. Individuals can vary markedly in their rate of lung function decline 13 and frequency of exacerbations 14,15 over the course of their disease. For example, COPD patients in the Lung Health Study had an annual rate of change in FEV 1 that ranged from rapidly declining to modestly increasing (95% CI −83 mL/yr to +15 mL/yr).…”

Section: Introductionmentioning

confidence: 99%

“…13 Similarly, the annual rate of exacerbations observed in the MACRO clinical trial varied from 0.47 to 4.22. 15 Quantifying this variation at an individual level is critical to enabling precise risk factor and disease management. 16…”

Section: Introductionmentioning

confidence: 99%

Heterogeneity in the respiratory symptoms of patients with mild-moderate COPD

Johnson

Safari

Tan

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

1Background: The burden of symptoms varies markedly between patients with Chronic 2 Obstructive Pulmonary Disease (COPD) and is only weakly correlated with lung function 3 impairment. While heterogeneity in lung function decline and exacerbations have been 4 previously studied, the extent of heterogeneity in symptoms and the factors associated with this 5 heterogeneity are not well understood. 6 Methods: A sample of the general Canadian population ≥ 40 years with persistent airflow 7 limitation was followed for up to 3 years. Participants reported whether they experienced chronic 8 coughing, phlegm, wheezing, or dyspnea during visits at 18-month intervals. We used mixed-9 effect logistic regression models (separately for each symptom) to assess overall heterogeneity in 1 0 the occurrence of symptoms between individuals, and the proportion of variation in symptom 1 1 burden explained by lung function versus all other clinical characteristics of participants. 1 2 Results: 548 participants (54% male, mean age 67 years) contributed 1,086 visits in total, and 1 3 82% of patients reported at least one symptom during follow-up. There was substantial 1 4 heterogeneity in the individual-specific probabilities for the occurrence of symptoms. This 1 5 heterogeneity was highest for dyspnea and lowest for phlegm (interquartile range of 1 6 probabilities: 0.15-0.77 and <0.01-0.53, respectively). FEV 1 explained 82% of the variation 1 7between individuals in the occurrence of phlegm, 26% for dyspnea, 3% for cough, and <0.1% for 1 8 wheeze. All clinical characteristics of participants (including FEV 1 ) explained between 86% of 1 9 heterogeneity in the occurrence of phlegm to <1% for wheeze. 2 0 Conclusion: There is marked heterogeneity in the burden of respiratory symptoms between 2 1 COPD patients. The ability of lung function and other commonly measured clinical 2 2characteristics to explain this heterogeneity differs between symptoms. 2 3 2 4 3 Word count: 262 2 5 2 6

show abstract

The Association Between Rate and Severity of Exacerbations in Chronic Obstructive Pulmonary Disease: An Application of a Joint Frailty-Logistic Model

Cited by 34 publications

References 29 publications

A threshold‐free summary index for quantifying the capacity of covariates to yield efficient treatment rules

A threshold‐free summary index for quantifying the capacity of covariates to yield efficient treatment rules

Development and validation of the Evaluation Platform In COPD (EPIC): a population-based outcomes model of COPD for Canada

Heterogeneity in the respiratory symptoms of patients with mild-moderate COPD

Contact Info

Product

Resources

About