# Publications of Rudas, T.

Informative Allocation and Consistent Treatment Selection. Statistical methodology. 2010;7(3):323-37.

Informative Allocation and Consistent Treatment Selection

When data in the form of a 2×2 treatment by response table are available, the better out of the two treatments is often selected using the odds ratio (cross product ratio). Such decisions do not depend on the allocation of the observations in the different treatment categories and may exhibit a counter-intuitive reversal property, called Simpson’s paradox. In cases when those receiving different treatments are potentially different, as in observational studies or in designed experiments with dropout or noncompliance, decisions taking into account the difference in observed allocations may be useful. Using an approach that postulates certain desirable properties of decision functions and derives further characteristics from these, a new decision procedure based on the cross sum ratio is investigated. This procedure is not only sensitive to allocation but also turns out to be the only selection procedure that avoids Simpson’s paradox. In addition to these logical advantages, the probability of wrong decision when using the cross sum ratio tends to be smaller than when using the cross product ratio. The application of the new decision procedure is illustrated by the re-analysis of data sets, some of which exhibit Simpson’s paradox when analyzed using the cross product ratio. Finally, generalizations of the decision procedures to 2×J decision tables are considered.

Handbook of probability : theory and applications. Rudas T, editor. Thousand Oaks: Sage; 2008.
Probability. In: Lavrakas P, editor. Encyclopedia of survey research methods. Thousand Oaks: SAGE Publications; 2008.
Probability Theory in Statistics. In: Handbook of probability : theory and applications. Thousand Oaks: Sage Publications; 2008. p. 69-84.
Invariant Hierarchical Clustering Schemes. In: Batyrshin I, editor. Perception-based data mining and decision making in economics and finance. Berlin: Springer; 2006. p. 181-206.
A hibahatár a becsült mennyiség függvényében : a mért pártpreferenciák téves értelmezésének egyik forrása. In: Angelusz R, Tardos R, editors. Mérésről mérésre : a választáskutatás módszertani kérdései : MA tankönyv. Budapest: Demokrácia Kutatások Magyar Központja Közhasznú Alapítvány; 2006. p. 17-40.
Odds and odds ratios. In: Everitt B, Howell DC, editors. Encyclopedia of statistics in behavioral science. Hoboken, N.J: Wiley; 2005. p. 1462-7.
Mixture Models of Missing Data. Quality & Quantity. 2005;39:19-36.
Probability theory : a primer. Vol 142. Thousand Oaks: Sage; 2004.
Hogyan olvassunk közvélemény-kutatásokat?. Budapest: Uj Mandátum; 1998.
Analysis of Contingency Tables Using Graphical Displays Based on the Mixture Index of Fit. In: Blasius J, Greenacre M, editors. Visualization of Categorical Data. San Diego: Academic Press; 1998. p. 425-39.
Odds ratios in the analysis of contingency tables. Vol 119. Thousand Oaks: Sage; 1998.
Estimating the importance of differential item functioning. Vol 95-3. Princeton, N.J.: Educational Testing Service; 1995.
DISTAN 2.0 Manual. Budapest: TÁRKI; 1992.
Társadalomkutatási módszertani tanulmányok. Vol 1. Budapest: TÁRKI; 1988.
Contingency tables with prescribed conditional odds ratios or prescribed log-linear parameters. Vol 88-1. Mainz: Fachbereich Mathematik, Johannes-Gutenberg-Universität; 1988.