ReviewERJ open research2020
Observational studies assessing the pharmacological treatment of obstructive lung disease: strengths, challenges and considerations for study design.
Review in ERJ open research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- The utility of quantitative computed tomography in cohort studies of chronic obstructive pulmonary disease: a narrative review.Journal of thoracic disease · 2023Review
- Roles of real-world evidence in severe asthma treatment: challenges and opportunities.ERJ open research · 2023Article
- Prevalence and Impact of Comorbidities in Individuals with Chronic Obstructive Pulmonary Disease: A Systematic Review.Tuberculosis and respiratory diseases · 2022Review
- Heterogeneity within and between physician-diagnosed asthma and/or COPD: NOVELTY cohort.The European respiratory journal · 2021Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Randomised controlled trials (RCTs) are the gold standard for evaluating treatment efficacy in patients with obstructive lung disease. However, due to strict inclusion criteria and the conditions required for ascertaining statistical significance, the patients included typically represent as little as 5% of the general obstructive lung disease population. Thus, studies in broader patient populations are becoming increasingly important. These can be randomised effectiveness trials or observational studies providing data on real-world treatment effectiveness and safety data that complement efficacy RCTs. In this review we describe the features associated with the diagnosis of asthma and chronic obstructive pulmonary disease (COPD) in the real-world clinical practice setting. We also discuss how RCTs and observational studies have reported opposing outcomes with several treatments and inhaler devices due to differences in study design and the variations in patients recruited by different study types. Whilst observational studies are not without weaknesses, we outline recently developed tools for defining markers of quality of observational studies. We also examine how observational studies are capable of providing valuable insights into disease mechanisms and management and how they are a vital component of research into obstructive lung disease. As we move into an era of personalised medicine, recent observational studies, such as the NOVEL observational longiTudinal studY (NOVELTY), have the capacity to provide a greater understanding of the value of a personalised healthcare approach in patients in clinical practice by focussing on standardised outcome measures of patient-reported outcomes, physician assessments, airway physiology, and blood and airway biomarkers across both primary and specialist care.
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.