domingo, 26 de enero de 2014

Scientific reporting is suboptimal for aspe... [J Clin Epidemiol. 2014] - PubMed - NCBI

Genomics & Risk Prediction: Limitations & Pitfalls

New CDC study: Scientific reporting is suboptimal in genetic risk prediction studies, especially for model construction & external validation

Iglesias AI et al. J Clin Epidemiology, January 2014

CDC paper: Improving reporting of genetic risk prediction studies: GRIPS statement.

PLoS Medicine (2011)

Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies.

Nilanjan Chatterjee, Nature Genetics (2013)

The problems with genomic prediction. Need it be stated again?

By Ken Weiss. The Mermaid Tale blog post, Jan 6

CDC blog post (2011): Shall we have pie or stew? Common complex diseases are due to genes and environment and their complex interactions

2014 Jan 7. pii: S0895-4356(13)00433-2. doi: 10.1016/j.jclinepi.2013.10.006. [Epub ahead of print]

Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

Iglesias AI1, Mihaescu R1, Ioannidis JP2, Khoury MJ3, Little J4, van Duijn CM1, Janssens AC5.

Author information

Abstract

OBJECTIVES:

Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement.

STUDY DESIGN AND SETTING:

We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted.

RESULTS:

Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model.

CONCLUSION:

Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models.