The International Journal of Biostatistics
Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.
2 Issues per year
IMPACT FACTOR 2014: 0.741
5-year IMPACT FACTOR: 1.475
SCImago Journal Rank (SJR): 1.039
Source Normalized Impact per Paper (SNIP): 0.746
Mathematical Citation Quotient 2013: 0.04
Volume 11 (2015)
Volume 7 (2011)
Volume 5 (2009)
Volume 4 (2008)
Volume 3 (2007)
Volume 2 (2006)
Volume 1 (2005)
Most Downloaded Articles
- An Introduction to Causal Inference by Pearl, Judea
- Sample Size Estimation for Repeated Measures Analysis in Randomized Clinical Trials with Missing Data by Lu, Kaifeng/ Luo, Xiaohui and Chen, Pei-Yun
- Survival Models in Health Economic Evaluations: Balancing Fit and Parsimony to Improve Prediction by Jackson, Christopher H/ Sharples, Linda D and Thompson, Simon G
- Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study by Xiao, Yongling/ Abrahamowicz, Michal and Moodie, Erica E. M.
- Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach by Radice, Rosalba/ Ramsahai, Roland/ Grieve, Richard/ Kreif, Noemi/ Sadique, Zia and Sekhon, Jasjeet S.
Bias Analysis to Guide New Data Collection
1Aarhus University Hospital
2Brigham and Women's Hospital and Harvard Medical School
Citation Information: The International Journal of Biostatistics. Volume 8, Issue 2, Pages 1–23, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1345, January 2012
- Published Online:
Bias analysis serves multiple objectives in epidemiologic data analysis. The objectives most often emphasized are quantification of uncertainty due to systematic errors and reduction in overconfidence by specifying hypotheses that compete with the causal hypothesis. A third objective is the utility of bias analysis to identify strategies for new data collection that will be productive in evaluating the validity of an association. The authors illustrate the value of this objective using two examples. The first example examines the value of comprehensive CYP2D6 genotyping in a study of tamoxifen resistance. Tamoxifen is metabolized primarily by CYP2D6 to more active forms. More than thirty polymorphisms in the CYP2D6 gene reduce its function. We genotyped the most prevalent CYP2D6 polymorphism and found a null association between genotype and breast cancer recurrence in a Danish population. One possibility is that incomplete genotyping of the multiple functional polymorphisms introduced non-differential misclassification and biased the association toward the null. We used bias analysis to evaluate the plausibility of this explanation and to guide a decision about devoting study resources toward more comprehensive genotyping of other polymorphisms in the CYP2D6 gene. The second example examines the association between vitamin K antagonist (VKA) therapy and the incidence of 24 site-specific cancers, using heart valve replacement as an instrumental variable. Earlier studies suggested a protective association between VKA anticoagulants and the incidence of cancer. We observed a null-centered distribution of associations, which may be due to non-differential misclassification of VKA therapy by the instrument. We used bias analysis to evaluate whether this misclassification was likely to explain the null-centered distribution of associations and to guide decisions about conducting a more expensive validation study. In the first example, the bias analysis showed that new data collection would be required to resolve the uncertainty, whereas the second example showed that new data collection was unlikely to be a productive use of scarce study resources.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.