Paul R. Rosenbaum

Ruoqi Yu, Dylan Small, Paul R. Rosenbaum (2021), The Information in Covariate Imbalance in Studies of Hormone Replacement Therapy, Annals of Applied Statistics, 15 (4), pp. 2023-2042.

Ruoqi Yu, Dylan Small, David Harding, Jose Alvarez, Paul R. Rosenbaum (2021), Optimal Matching for Observational Studies that Integrate Quantitative and Qualitative Research, Statistics and Public Policy , 8 (p.p. 42-52).

Paul R. Rosenbaum, Replication and Evidence Factors in Observational Studies (Chapman and Hall/CRC Monographs on Statistics and Applied Probability) (:, 2021)

Abstract: Book in the series: Chapman and Hall/CRC Monographs on Statistics and Applied Probability.

Siddharth Jain, Paul R. Rosenbaum, Joseph Reiter, Geoffrey Hoffman, Dylan Small, Jinkyung Ha, Alexander S. Hill, David A. Wolk, Timothy G. Gaulton, Mark Neuman, Roderic G. Eckenhoff, Lee Fleisher, Jeffrey H. Silber (2021), Using Medicare claims in identifying Alzheimer’s disease and related dementias, Alzheimer's & Dementia, 17 (3), pp. 515-524.

Bikram Karmakar, Dylan Small, Paul R. Rosenbaum (2021), Reinforced Designs: Multiple Instruments Plus Control Groups as Evidence Factors in an Observational Study of the Effectiveness of Catholic Schools, Journal of the American Statistical Association, 116 (533), pp. 82-92.

Abstract: Absent randomization, causal conclusions gain strength if several independent evidence factors concur. We develop a method for constructing evidence factors from several instruments plus a direct comparison of treated and control groups, and we evaluate the methods performance in terms of design sensitivity and simulation. In the application, we consider the effectiveness of Catholic versus public high schools, constructing three evidence factors fromthree past strategies for studying this question, namely: (i) having nearby access to a Catholic school as an instrument, (ii) being Catholic as an instrument for attending Catholic school, and (iii) a direct comparison of students in Catholic and public high schools. Although these three analyses use the same data,we: (i) construct three essentially independent statistical tests of no effect that require very different assumptions, (ii) study the sensitivity of each test to the assumptions underlying that test, (iii) examine the degree to which independent tests dependent upon different assumptions concur, (iv) pool evidence across independent factors. In the application, we conclude that the ostensible benefit of Catholic education depends critically on the validity of one instrument, and is therefore quite fragile.

Paul R. Rosenbaum (2020), Combining planned and discovered comparisons in observational studies, Biostatistics, 21 (3), pp. 384-399. 10.1093/biostatistics/kxy055

Rachel R. Kelz, Morgan M. Sellers, Bijan A. Niknam, James E. Sharpe, Paul R. Rosenbaum, Alexander S. Hill, Hong Zhou, Lauren L. Hochman, Karl Y. Bilimoria, Kamal Itani, Patrick S. Romano, Jeffrey H. Silber (2020), A national comparison of operative outcomes of new and experienced surgeons, Annals of Surgery, (to appear) ().

Ruoqi Yu, Jeffrey H. Silber, Paul R. Rosenbaum (2020), Matching methods for observational studies derived from large administrative databases, Statistical Science, 18. 10.1214/19-STS699

Abstract: (Authors: Ruoqi Yu, Jeffrey Silber, and Paul R. Rosenbaum) We propose new optimal matching techniques for large administrative data sets. In current practice, very large matched samples are constructed by subdividing the population and solving a series of smaller problems, for instance, matching men to men and separately matching women to women. Without simplification of some kind, the time required to optimally match T treated individuals to T controls selected from C ≥ T potential controls grows much faster than linearly with the number of people to be matched—the required time is of order O{(T +C)^3}—so splitting one large problem into many small problems greatly accelerates the computations. This common practice has several disadvantages that we describe. In its place, we propose a single match, using everyone, that accelerates the computations in a different way. In particular, we use an iterative form of Glover’s algorithm for a doubly convex bipartite graph to determine an optimal caliper for the propensity score, radically reducing the number of candidate matches; then we optimally match in a large but much sparser graph. In this graph, a modified form of near-fine balance can be used on a much larger scale, improving its effectiveness. We illustrate the method using data from US Medicaid, matching children receiving surgery at a children’s hospital to similar children receiving surgery at a hospital that mostly treats adults. In the example, we form 38,841 matched pairs from 159,527 potential controls, controlling for 29 covariates plus 463 Principal Surgical Procedures, plus 973 Principal Diagnoses. The method is implemented in an R package bigmatch available from CRAN.

Paul R. Rosenbaum, Design of Observational Studies, 2nd edition (: Springer, 2020)

Abstract: ProductFlyer_9783030464042

Raiden B. Hasegawa, Sameer K. Deshpande, Dylan Small, Paul R. Rosenbaum (2020), Causal inference with two versions of treatment, Journal of Educational and Behavioral Statistics, 45 (4), pp. 426-445.