Evidence map›Paper›PMID 36461748›Full record

ReviewJournal of clinical pharmacology2022

Natural History and Real-World Data in Rare Diseases: Applications, Limitations, and Future Perspectives.

Jing Liu, Jeffrey S Barrett, Efthimia T Leonardi, Lucy Lee, Satrajit Roychoudhury, Yong Chen, Panayiota Trifillis

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of clinical pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

84 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  20. Rigorous genetic diagnosis review in natural history studies.Orphanet journal of rare diseases · 2026
    Article

24 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jing LiuPfizer, Inc., Groton, Connecticut, USA.
Jeffrey S BarrettCritical Path Institute, Rare Disease Cures Accelerator Data Analytics Platform, Tucson, Arizona, USA.
Efthimia T LeonardiPTC Therapeutics, Inc., South Plainfield, New Jersey, USA.
Lucy LeePTC Therapeutics, Inc., South Plainfield, New Jersey, USA.
Satrajit RoychoudhuryPfizer, Inc., Groton, Connecticut, USA.
Yong ChenPfizer, Inc., Groton, Connecticut, USA.
Panayiota TrifillisPTC Therapeutics, Inc., South Plainfield, New Jersey, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and drug development for these conditions, including patient identification and recruitment, trial design, and costs. Natural history data and real-world data (RWD) play significant roles in defining and characterizing disease progression, final patient populations, novel biomarkers, genetic relationships, and treatment effects. This review provides an introduction to rare diseases, natural history data, RWD, and real-world evidence, the respective sources and applications of these data in several rare diseases. Considerations for data quality and limitations when using natural history and RWD are also elaborated. Opportunities are highlighted for cross-sector collaboration, standardized and high-quality data collection using new technologies, and more comprehensive evidence generation using quantitative approaches such as disease progression modeling, artificial intelligence, and machine learning. Advanced statistical approaches to integrate natural history data and RWD to further disease understanding and guide more efficient clinical study design and data analysis in drug development in rare diseases are also discussed.

Indexed as

Artificial IntelligenceRare DiseasesDisease ProgressionDrug DevelopmentHumansResearch Designdisease progression modelingnatural historyrare diseasesreal-world datareal-world evidence

Identifiers

PMID36461748
PMCPMC10107901

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.