Design Sensitivity and Efficiency in Observational Studies

By Paul R. Rosenbaum. 2010

90 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f22e69479774eab3a7169a9945fdfb0bf5521a64

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Citations in the corpus

On Sensitivity Value of Pair-Matched Observational Studies (2017, background): “Rosenbaum (2004) proposed to select the test statistics to maximize the design sensitivity ̃ or equivalently μF in the transformed scale; see also Rosenbaum (2011, 2010b).”
Effect Modification and Design Sensitivity in Observational Studies (2013, methodology): “The 1560 pairs were formed by matching for age and gender using the pairmatch function in the optmatch package in R (Hansen 2007) applied to a distance matrix that combined a caliper on an estimated propensity score with a rank-based Mahalanobis distance (Rosenbaum 2010b, sec.”
Matching for Balance, Pairing for Heterogeneity in an Observational Study of the Effectiveness of For-Profit and Not-For-Profit High Schools in Chile (2014, methodology): “…then with τ = 1/2, the design sensitivity is Γ̃ = 3.2 for Wilcoxon’s signed-rank statistic and Γ̃ = 3.6 for Brown’s (1981) combined quantile average, so at Γ = 3.4, the power of Wilcoxon’s statistic is tending to 0 as I →∞ while the power of Brown’s statistic is tending to 1; see Rosenbaum (2010b).”
Sensitivity Analysis in Observational Research: Introducing the E-Value (2017, methodology): “, that some third factor related to both the treatment and the outcome might explain their association, with no true causal effect(12-22).”
Testing one hypothesis twice in observational studies (2012, background): “Excellent power when only a subset of treated subjects respond to treatment; see Conover and Salsburg (1988) and Rosenbaum (2007; 2010a, §16).”