SynthesisCPT: pharmacometrics & systems pharmacology2017
Model-based Meta-Analysis on the Efficacy of Pharmacological Treatments for Idiopathic Pulmonary Fibrosis.
Synthesis in CPT: pharmacometrics & systems pharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 3 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Combining Aggregate Data and Individual Patient Data in Model-Based Meta-Analysis: An Illustrative Case Study of Tofacitinib in Rheumatoid Arthritis Patients.CPT: pharmacometrics & systems pharmacology · 2026Pooled it
- Applications of Model-Based Meta-Analysis in Drug Development.Pharmaceutical research · 2022Pooled it
- Model-Based Meta-Analysis Compares DAS28 Rheumatoid Arthritis Treatment Effects and Suggests an Expedited Trial Design for Early Clinical Development.Clinical pharmacology and therapeutics · 2021Pooled it
- Examining the Reliability of Model-Based Meta-Analysis (MBMA) Outcomes: A Simulation Study.CPT: pharmacometrics & systems pharmacology · 2026Article
- Repurposing disulfiram: targeting Zeb1 to attenuate paraquat-induced pulmonary fibrosis in rats.Journal of molecular histology · 2025Article
- Pharmacometrics meets statistics-A synergy for modern drug development.CPT: pharmacometrics & systems pharmacology · 2021Article
- Danhong injection in the treatment of idiopathic pulmonary fibrosis: A protocol for systematic review and meta-analysis.Medicine · 2020Article
- Bacterial burden in the lower airways predicts disease progression in idiopathic pulmonary fibrosis and is independent of radiological disease extent.The European respiratory journal · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the US Food and Drug Administration (FDA) approved the first two drugs (pirfenidone and nintedanib) indicated for the treatment of idiopathic pulmonary fibrosis (IPF). The purpose of this analysis was to leverage publicly available data to quantify comparative efficacy of compounds that are approved or in development. An analysis-ready database was developed, and the analysis dataset is composed of summary-level data from 43 arms in 20 trials, with treatment durations ranging from 8-104 weeks. A hierarchical multivariable regression model with nonparametric placebo estimation was used to fit the longitudinal profile of change from baseline of percent predicted forced vital capacity (%predicted FVC) data. Pirfenidone and nintedanib were the only drugs identified to have significant estimated positive treatment effects. Model simulations were performed to further evaluate the covariate and time course of treatment effects on longitudinal change from baseline %predicted FVC to inform future trial designs and support decision making.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.