ReviewHandbook of clinical neurology2017
Statistical modeling of Huntington disease onset.
Review in Handbook of clinical neurology, 2017. 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.
- A novel prognostic 6-gene signature for osteoporosis.Frontiers in endocrinology · 2022Article
- Machine learning analysis of gene expression profile reveals a novel diagnostic signature for osteoporosis.Journal of orthopaedic surgery and research · 2021Article
- A Potential Three-Gene-Based Diagnostic Signature for Hypertension in Pregnancy.International journal of general medicine · 2021Article
- EARLY DIAGNOSIS OF NEUROLOGICAL DISEASE USING PEAK DEGENERATION AGES OF MULTIPLE BIOMARKERS.The annals of applied statistics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Huntington disease (HD) is caused by a CAG trinucleotide expansion in the huntingtin gene. We now have the power to predict age-at-onset from subject-specific features like motor and neuroimaging measures. In clinical trials, properly modeling onset age is important, because it improves power calculations and directs clinicians to recruit subjects with certain features. The history of modeling onset, from simple linear and logistic regression to advanced survival models, is discussed. We highlight their advantages and disadvantages, emphasizing the methodological challenges when genetic mutation status is unavailable. We also discuss the potential bias and higher variability incurred from the uncertainty associated with subjective definitions for onset. Methods to adjust for the uncertainty in survival models are still in their infancy, but would be beneficial for HD and neurodegenerative diseases with long prodromal periods like Alzheimer's and Parkinson's disease.
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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.