Parametric Time‐to‐Event Model for Acute Exacerbations in Idiopathic Pulmonary Fibrosis

Tang, Fei; Weber, Benjamin; Stowasser, Susanne; Korell, Julia

doi:10.1002/psp4.12485

Cited by 4 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A TTE analysis has been published for time-to-first exacerbation in idiopathic pulmonary fibrosis. 32 The model we present can be used to predict asthma exacerbations based on observed time-varying covariates from other patient studies (even for other drugs) where the relevant time-varying covariates have been collected, but the patient material and study duration is not necessarily sufficient for directly assessing outcomes in exacerbations. This information would be relevant during early stage asthma drug development and can likely streamline decision making regarding design for future studies within asthma drug development (e.g., dose selection and sample size determinations).…”

Section: Discussionmentioning

confidence: 99%

“…Models for predicting exacerbations in chronic obstructive pulmonary disease exist 30,31 (these are not parametric TTE models). A TTE analysis has been published for time‐to‐first exacerbation in idiopathic pulmonary fibrosis 32 …”

Section: Discussionmentioning

confidence: 99%

“…A TTE analysis has been published for time‐to‐first exacerbation in idiopathic pulmonary fibrosis. 32 …”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Population repeated time‐to‐event analysis of exacerbations in asthma patients: A novel approach for predicting asthma exacerbations based on biomarkers, spirometry, and diaries/questionnaires

Svensson¹,

Ribbing²,

Kotani³

et al. 2021

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Population repeated time‐to‐event analysis of exacerbations in asthma patients: A novel approach for predicting asthma exacerbations based on biomarkers, spirometry, and diaries/questionnaires

Svensson¹,

Ribbing²,

Kotani³

et al. 2021

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

show abstract

“…All models 15 , 16 , 28–45 were at high ROB in development or validation. Eight models 16 , 35–37 , 39 , 40 , 42 , 45 are rated as high ROB in applicability. Figures 2 and 3 show the proportion assessment for each PROBAST item and proportion of studies with potential bias using PROBAST, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…This is most commonly due to concerns about applicability in domain 1 (population) for included randomized clinical trials or specific groups. In addition, two 37 , 40 out of 20 studies’ outcomes included exacerbation, but the definition of acute exacerbation may have varied between studies and may have been expanded to include events for which a trigger can be identified, such as infection. 46 …”

Section: Resultsmentioning

confidence: 99%

Bias and Reporting Quality of Clinical Prognostic Models for Idiopathic Pulmonary Fibrosis: A Cross-Sectional Study

Di¹,

Li²,

Yang³

et al. 2022

RMHP

View full text Add to dashboard Cite

Objective This study aims to evaluate the risk of bias (ROB) and reporting quality of idiopathic pulmonary fibrosis (IPF) prediction models by assessing characteristics of these models. Methods The development and/or validation of IPF prognostic models were identified via an electronic search of PubMed, Embase, and Web of Science (from inception to 12 August, 2021). Two researchers independently assessed the risk of bias (ROB) and reporting quality of IPF prediction models based on the Prediction model Risk Of Bias Assessment Tool (PROBAST) and Transparent Reporting of a multivariable prognostic model for Individual Prognosis or Diagnosis (TRIPOD) checklist. Results Twenty prognostic model studies for IPF were included, including 7 (35%) model development and external validation studies, 8 (40%) development studies, and 5 (25%) external validation studies. According to PROBAST, all studies were appraised with high ROB, because of deficient reporting in the domains of participants (45.0%) and analysis (67.3%), and at least 55% studies were susceptible to 4 of 20 sources of bias. For the reporting quality, none of them completely adhered to the TRIPOD checklist, with the lowest mean reporting score for the methods and results domains (46.6% and 44.7%). For specific items, eight sub-items had a reporting rate ≥80% and adhered to the TRIPOD checklist, and nine sub-items had a very poor reporting rate, less than 30%. Conclusion Studies adhering to PROBAST and TRIPOD checklists are recommended in the future. The reproducibility and transparency can be improved when studies completely adhere to PROBAST and TRIPOD checklists.

show abstract

Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

Wijk

Simonsson

2022

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

Parametric time‐to‐event analysis is an important pharmacometric method to predict the probability of an event up until a certain time as a function of covariates and/or drug exposure. Modeling is performed at the level of the hazard function describing the instantaneous rate of an event occurring at that timepoint. We give an overview of the parametric time‐to‐event analysis starting with graphical exploration by Kaplan–Meier plotting for the event data including censoring and nonparametric hazard estimators such as the kernel‐based visual hazard comparison for the underlying hazard. The most common hazard functions including the exponential, Gompertz, Weibull, log‐normal, log‐logistic, and circadian functions are described in detail. A Shiny application was developed to graphically guide the modeler which of the most common hazard functions presents a similar shape compared to the data in order to guide which hazard functions to test in the parametric time‐to‐event analysis. For the chosen hazard function(s), the Shiny application can additionally be used to explore corresponding parameter values to inform on suitable initial estimates for parametric modeling as well as on possible covariate or treatment relationships to certain parameters. Moreover, it can be used for the dissemination of results as well as communication, training, and workshops on time‐to‐event analysis. By guiding the modeler on which functions and what parameter values to test and compare as well as to assist in dissemination, the Shiny application developed here greatly supports the modeler in complicated parametric time‐to‐event modeling.

show abstract

Parametric Time‐to‐Event Model for Acute Exacerbations in Idiopathic Pulmonary Fibrosis

Cited by 4 publications

References 26 publications

Population repeated time‐to‐event analysis of exacerbations in asthma patients: A novel approach for predicting asthma exacerbations based on biomarkers, spirometry, and diaries/questionnaires

Population repeated time‐to‐event analysis of exacerbations in asthma patients: A novel approach for predicting asthma exacerbations based on biomarkers, spirometry, and diaries/questionnaires

Bias and Reporting Quality of Clinical Prognostic Models for Idiopathic Pulmonary Fibrosis: A Cross-Sectional Study

Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

Contact Info

Product

Resources

About