Paul R. Rosenbaum’s research while affiliated with University of Pennsylvania and other places

In experimental design, aliasing of effects occurs in fractional factorial experiments, where certain low order factorial effects are indistinguishable from certain high order interactions: low order contrasts may be orthogonal to one another, while their higher order interactions are aliased and not identified. In observational studies, aliasing occurs when certain combinations of covariates -- e.g., time period and various eligibility criteria for treatment -- perfectly predict the treatment that an individual will receive, so a covariate combination is aliased with a particular treatment. In this situation, when a contrast among several groups is used to estimate a treatment effect, collections of individuals defined by contrast weights may be balanced with respect to summaries of low-order interactions between covariates and treatments, but necessarily not balanced with respect to summaries of high-order interactions between covariates and treatments. We develop a theory of aliasing in observational studies, illustrate that theory in an observational study whose aliasing is more robust than conventional difference-in-differences, and develop a new form of matching to construct balanced confounded factorial designs from observational data.

What is the best way to split one stratum into two to maximally reduce the within-stratum imbalance in many covariates? We formulate this as an integer program and approximate the solution by randomized rounding of a linear program. A linear program may assign a fraction of a person to each refined stratum. Randomized rounding views fractional people as probabilities, assigning intact people to strata using biased coins. Randomized rounding is a well-studied theoretical technique for approximating the optimal solution of certain insoluble integer programs. When the number of people in a stratum is large relative to the number of covariates, we prove the following new results: (i) randomized rounding to split a stratum does very little randomizing, so it closely resembles the linear programming relaxation without splitting intact people; (ii) the linear relaxation and the randomly rounded solution place lower and upper bounds on the unattainable integer programming solution; and because of (i), these bounds are often close, thereby ratifying the usable randomly rounded solution. We illustrate using an observational study that balanced many covariates by forming matched pairs composed of 2016 patients selected from 5735 using a propensity score. Instead, we form 5 propensity score strata and refine them into 10 strata, obtaining excellent covariate balance while retaining all patients. An R package optrefine at CRAN implements the method. Supplementary materials are available online.

Background Observational studies of anesthetic neurotoxicity may be biased because children requiring anesthesia commonly have medical conditions associated with neurobehavioral problems. This study takes advantage of a natural experiment associated with appendicitis, in order to determine if anesthesia and surgery in childhood were specifically associated with subsequent neurobehavioral outcomes. Methods We identified 134,388 healthy children with appendectomy and examined the incidence of subsequent externalizing or behavioral disorders (conduct, impulse control, oppositional defiant, or attention-deficit/hyperactivity disorder); or internalizing or mood/anxiety disorders (depression, anxiety, or bipolar disorder) when compared to 671,940 matched healthy controls as identified in Medicaid data between 2001-2018. For comparison, we also examined 154,887 otherwise healthy children admitted to the hospital for pneumonia, cellulitis, and gastroenteritis, of which only 8% received anesthesia, and compared them to 774,435 matched healthy controls. We also examined the difference-in-differences between matched appendectomy patients and their controls and matched medical admission patients and their controls. Results Compared to controls, children with appendectomy were more likely to have subsequent behavioral disorders (the hazard ratio (HR) was 1.04 (95% CI 1.01, 1.06), P = 0.0010), and mood/anxiety disorders (HR: 1.15 (95% CI 1.13, 1.17), P < 0.0001). Relative to controls, children with medical admissions were also more likely to have subsequent behavioral (HR: 1.20 (95% CI 1.18, 1.22), P < 0.0001), and mood/anxiety (HR: 1.25 (95% CI 1.23, 1.27), P < 0.0001) disorders. Comparing the difference between matched appendectomy patients and their matched controls to the difference between matched medical patients and their matched controls, medical patients had more subsequent neurobehavioral problems than appendectomy patients. Conclusions Although there is an association between neurobehavioral diagnoses and appendectomy, this association is not specific to anesthesia exposure, and is stronger in medical admissions. Medical admissions, generally without anesthesia exposure, displayed significantly higher rates of these disorders than appendectomy-exposed patients.

Importance In surgical patients, it is well known that higher hospital procedure volume is associated with better outcomes. To our knowledge, this volume-outcome association has not been studied in ambulatory surgery centers (ASCs) in the US. Objective To determine if low-volume ASCs have a higher rate of revisits after surgery, particularly among patients with multimorbidity. Design, Setting, and Participants This matched case-control study used Medicare claims data and analyzed surgeries performed during 2018 and 2019 at ASCs. The study examined 2328 ASCs performing common ambulatory procedures and analyzed 4751 patients with a revisit within 7 days of surgery (defined to be either 1 of 4735 revisits or 1 of 16 deaths without a revisit). These cases were each closely matched to 5 control patients without revisits (23 755 controls). Data were analyzed from January 1, 2018, through December 31, 2019. Main Outcomes and Measures Seven-day revisit in patients (cases) compared with the matched patients without the outcome (controls) in ASCs with low volume (less than 50 procedures over 2 years) vs higher volume (50 or more procedures). Results Patients at a low-volume ASC had a higher odds of a 7-day revisit vs patients who had their surgery at a higher-volume ASC (odds ratio [OR], 1.21; 95% CI, 1.09-1.36; P = .001). The odds of revisit for patients with multimorbidity were higher at low-volume ASCs when compared with higher-volume ASCs (OR, 1.57; 95% CI, 1.27-1.94; P < .001). Among patients with multimorbidity in low-volume ASCs, for those who underwent orthopedic procedures, the odds of revisit were 84% higher (OR, 1.84; 95% CI, 1.36-2.50; P < .001) vs higher-volume centers, and for those who underwent general surgery or other procedures, the odds of revisit were 36% higher (OR, 1.36; 95% CI, 1.01-1.83; P = .05) vs a higher-volume center. The findings were not statistically significant for patients without multimorbidity. Conclusions and Relevance In this observational study, the surgical volume of an ASC was an important indicator of patient outcomes. Older patients with multimorbidity should discuss with their surgeon the optimal location of their care.

We define a “flagship hospital” as the largest academic hospital within a hospital referral region and a “flagship system” as a system that contains a flagship hospital and its affiliates. It is not known if patients admitted to an affiliate hospital, and not to its main flagship hospital, have better outcomes than those admitted to a hospital outside the flagship system but within the same hospital referral region. To compare mortality at flagship hospitals and their affiliates to matched control patients not in the flagship system but within the same hospital referral region. A matched cohort study The study used hospitalizations for common medical conditions between 2018-2019 among older patients age ≥ 66 years. We analyzed 118,321 matched pairs of Medicare patients admitted with pneumonia (N=57,775), heart failure (N=42,531), or acute myocardial infarction (N=18,015) in 35 flagship hospitals, 124 affiliates, and 793 control hospitals. 30-day (primary) and 90-day (secondary) all-cause mortality. 30-day mortality was lower among patients in flagship systems versus control hospitals that are not part of the flagship system but within the same hospital referral region (difference= -0.62%, 95% CI [-0.88%, -0.37%], P<0.001). This difference was smaller in affiliates versus controls (-0.43%, [-0.75%, -0.11%], P=0.008) than in flagship hospitals versus controls (-1.02%, [-1.46%, -0.58%], P<0.001; difference-in-difference -0.59%, [-1.13%, -0.05%], P=0.033). Similar results were found for 90-day mortality. The study used claims-based data. In aggregate, within a hospital referral region, patients treated at the flagship hospital, at affiliates of the flagship hospital, and in the flagship system as a whole, all had lower mortality rates than matched controls outside the flagship system. However, the mortality advantage was larger for flagship hospitals than for their affiliates.

Objective To compare general surgery outcomes at flagship systems, flagship hospitals, and flagship hospital affiliates versus matched controls. Summary Background Data It is unknown whether flagship hospitals perform better than flagship hospital affiliates for surgical patients. Methods Using Medicare claims for 2018 to 2019, we matched patients undergoing inpatient general surgery in flagship system hospitals to controls who underwent the same procedure at hospitals outside the system but within the same region. We defined a “flagship hospital” within each region as the major teaching hospital with the highest patient volume that is also part of a hospital system; its system was labeled a “flagship system.” We performed 4 main comparisons: patients treated at any flagship system hospital versus hospitals outside the flagship system; flagship hospitals versus hospitals outside the flagship system; flagship hospital affiliates versus hospitals outside the flagship system; and flagship hospitals versus affiliate hospitals. Our primary outcome was 30-day mortality. Results We formed 32,228 closely matched pairs across 35 regions. Patients at flagship system hospitals (32,228 pairs) had lower 30-day mortality than matched control patients [3.79% vs. 4.36%, difference=−0.57% (−0.86%, −0.28%), P <0.001]. Similarly, patients at flagship hospitals (15,571/32,228 pairs) had lower mortality than control patients. However, patients at flagship hospital affiliates (16,657/32,228 pairs) had similar mortality to matched controls. Flagship hospitals had lower mortality than affiliate hospitals [difference-in-differences=−1.05% (−1.62%, −0.47%), P <0.001]. Conclusions Patients treated at flagship hospitals had significantly lower mortality rates than those treated at flagship hospital affiliates. Hence, flagship system affiliation does not alone imply better surgical outcomes.

In an observational study of the effects caused by a treatment, a second control group is used in an effort to detect bias from unmeasured covariates, and the investigator is content if no evidence of bias is found. This strategy is not entirely satisfactory: two control groups may differ significantly, yet the difference may be too small to invalidate inferences about the treatment, or the control groups may not differ yet nonetheless fail to provide a tangible strengthening of the evidence of a treatment effect. Is a firmer conclusion possible? Is there a way to analyze a second control group such that the data might report measurably strengthened evidence of cause and effect, that is, insensitivity to larger unmeasured biases? Evidence factor analyses are not commonly used with a second control group: most analyses compare the treated group to each control group, but analyses of that kind are partially redundant; so, they do not constitute evidence factors. An alternative analysis is proposed here, one that does yield two evidence factors, and with a carefully designed test statistic, is capable of extracting strong evidence from the second factor. The new technical work here concerns the development of a test statistic with high design sensitivity and high Bahadur efficiency in a sensitivity analysis for the second factor. A study of binge drinking as a cause of high blood pressure is used as an illustration.