A post-hoc Unweighted Analysis of Counter-Matched Case-Control Data

Rakovski Cyril () and Langholz Bryan
Additional contact information
Rakovski Cyril: Department of Mathematics, Chapman University, Orange, CA 92866, USA
Langholz Bryan: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA

The International Journal of Biostatistics, 2015, vol. 11, issue 2, 223-232

Abstract: Informative sampling based on counter-matching risk set subjects on exposure correlated with a variable of interest has been shown to be an efficient alternative to simple random sampling; however, the opposite is true when correlation between the two covariates is absent. Thus, the counter-matching design will entail substantial gains in statistical efficiency compared to simple random sampling at a first stage of analyses focused by design on variables correlated with the counter-matching variable but will lose efficiency at a second stage of analyses aimed at variables independent of the counter-matching variable and not conceived as a part of the initial study. In an effort to recover efficiency in such second stage of analyses scenarios, we considered a naive analysis of the effect of a dichotomous covariate on the disease rates in the population that ignores the underlying counter-matching sampling design. We derive analytical expressions for the bias and variance and show that when the counter-matching and the new dichotomous variable of interest are uncorrelated and a multiplicative main effects model holds, such an analysis is advantageous over the standard “weighted” approach, especially when the counter-matching variable is rare and in such scenarios the efficiency gains exceeds 80%. Moreover, we consider all possible conceptual violations of the required assumptions and show that moderate departures from the above-mentioned requirements lead to negligible levels of bias; numerical values for the bias under common scenarios are provided. The method is illustrated via an analysis of BRCA1/2 deleterious mutations in the radiation treatment counter-matched WECARE study of second breast cancer.

Keywords: efficiency; partial likelihood; proportional hazards; sampling (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2014-0018 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:11:y:2015:i:2:p:223-232:n:1

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2014-0018

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().