title: The cross-cut statistic and its sensitivity to bias in observational studies with ordered doses of treatment.
abstract: A common practice with ordered doses of treatment and ordered responses, perhaps recorded in a contingency table with ordered rows and columns, is to cut or remove a cross from the table, leaving the outer corners--that is, the high-versus-low dose, high-versus-low response corners--and from these corners to compute a risk or odds ratio. This little remarked but common practice seems to be motivated by the oldest and most familiar method of sensitivity analysis in observational studies, proposed by Cornfield et al. (1959), which says that to explain a population risk ratio purely as bias from an unobserved binary covariate, the prevalence ratio of the covariate must exceed the risk ratio. Quite often, the largest risk ratio, hence the one least sensitive to bias by this standard, is derived from the corners of the ordered table with the central cross removed. Obviously, the corners use only a portion of the data, so a focus on the corners has consequences for the standard error as well as for bias, but sampling variability was not a consideration in this early and familiar form of sensitivity analysis, where point estimates replaced population parameters. Here, this cross-cut analysis is examined with the aid of design sensitivity and the power of a sensitivity analysis.
authors: Department of Statistics, University of Pennsylvania, Philadelphia 19104-6340, U.S.A.
authors: Paul R
authors: PR
authors: Rosenbaum
mesh_terms: D015982:Bias; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D016903:Drug Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D064887:Observational Studies as Topic / Q000379:methods*; D016017:Odds Ratio; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Design sensitivity; Doses of treatment; Observational studies; Power of a sensitivity analysis; Sensitivity analysis