Schell, MJ. 2010. Identifying Key Statistical Papers From 1985 to 2002 Using Citation Data for Applied Biostatisticians. AMERICAN STATISTICIAN 64 (4): 310-317

[Eugene Garfield]

Schell, MJ. 2010. Identifying Key Statistical Papers From 1985 to 2002 Using 
Citation Data for Applied Biostatisticians. AMERICAN STATISTICIAN 64 (4): 
310-317..

Author Full Name(s): Schell, Michael J.
Language: English
Document Type: Article

Author Keywords: Applied fraction; Citation count; Statistical practice
KeyWords Plus: SMOOTHING PARAMETER-ESTIMATION; PROPORTIONAL 
HAZARDS MODEL; LONGITUDINAL DATA-ANALYSIS; FALSE DISCOVERY RATE; 
LOGISTIC-REGRESSION; MULTIPLE IMPUTATION; INTERVAL ESTIMATION; 
MAXIMUM-LIKELIHOOD; SURVIVAL ANALYSIS; PUBLICATION BIAS

Abstract: Dissemination of ideas from theory to practice is a significant 
challenge in statistics. Quick identification of articles useful to practitioners 
would greatly assist in this dissemination, thereby improving science. This 
article uses the citation count history of articles to identify key papers from 
1985 to 2002 from 12 statistics journals for applied biostatisticians. One feature 
requiring attention in order to appropriately rank an article's impact is 
assessment of the citation accrual patterns over time. Citation counts in 
statistics differ dramatically from fields such as medicine. In statistics, most 
articles receive few citations, with 15-year-old articles from five key journals 
receiving a median of 13 citations compared to 66 in the Journal of Clinical 
Oncology. However, statistics articles in the top 2%-3% continue to gain 
citations at a high rate past 15 years, exceeding those in JCO, whose counts 
slow dramatically around 8 years past publication. Articles with the highest 
expected applied uses 20 years post publication were identified using joinpoint 
regression. In this evaluation, the fraction of citations that represent applied 
use was defined and estimated. The false discovery rate, quantification of 
heterogeneity in meta-analysis, and generalized estimating equations rank as 
the ideas with the greatest estimated applied impact.

Addresses: Univ S Florida, H Lee Moffitt Canc Ctr & Res Inst, Dept Biostat, 
Tampa, FL 33612 USA
Reprint Address: Schell, MJ, Univ S Florida, H Lee Moffitt Canc Ctr & Res Inst, 
Dept Biostat, Tampa, FL 33612 USA.

E-mail Address: michael.schell at moffitt.org
ISSN: 0003-1305
DOI: 10.1198/tast.2010.08250
fulltext: http://pubs.amstat.org/doi/pdf/10.1198/tast.2010.08250