Paul R. Rosenbaum's research while affiliated with University of Pennsylvania and other places
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Publications (344)
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 invali...
Objectives
Evaluate whether hospital factors, including nurse resources, explain racial differences in Medicare black and white patient surgical outcomes and whether disparities changed over time.
Design
Retrospective tapered-match.
Setting
571 hospitals at two time points (Early Era 2003–2005; Recent Era 2013–2015).
Participants
6752 black pati...
Background:
Surgery for older Americans is increasingly being performed at ambulatory surgery centers (ASCs) rather than hospital outpatient departments (HOPDs), while rates of multimorbidity have increased.
Objective:
To determine whether there are differential outcomes in older patients undergoing surgical procedures at ASCs versus HOPDs.
Res...
Background:
Multimorbidity in surgery is common and associated with worse postoperative outcomes. However, conventional multimorbidity definitions (≥2 comorbidities) label the vast majority of older patients as multimorbid, limiting clinical usefulness. We sought to develop and validate better surgical specialty-specific multimorbidity definitions...
Fluoroquinolones are widely prescribed antibiotics that carry a US Food and Drug Administration warning about possible side-effects on the central and peripheral nervous system. We compare 436,891 patients with sinusitis treated with fluoroquinolones to two control groups treated with azithromycin or amoxicillin. In addition to looking for nervous...
Background
The term “multimorbidity” identifies high-risk, complex patients and is conventionally defined as ≥2 comorbidities. However, this labels almost all older patients as multimorbid, making this definition less useful for physicians, hospitals, and policymakers.Objective
Develop new medical condition-specific multimorbidity definitions for p...
The design of any study, whether experimental or observational, that is intended to estimate the causal effects of a treatment condition relative to a control condition, refers to those activities that precede any examination of outcome variables. As defined in our 1983 article (Rosenbaum & Rubin, 1983), the propensity score is the unit-level condi...
In causal inference, natural strata are a new compromise between conventional strata and matching in a fixed ratio, say pair matching or matching two controls to each treated individual. Like matching in a fixed ratio, natural strata: (a) do not require weights, (b) balance many measured covariates beyond those that define the strata and (c) provid...
Are weak associations between a treatment and a binary outcome always sensitive to small unmeasured biases in observational studies? This possibility is often discussed in epidemiology. The familiar Mantel‐Haenszel test for a 2×2×S$$ 2\times 2\times S $$ contingency table exaggerates sensitivity to unmeasured biases when the population odds ratios...
A new transformation is proposed for treated-minus-control matched pair differences that leaves the center of their distribution untouched, but symmetrically and smoothly transforms and shortens the tails. In this way, the center of the distribution is interpretable, undistorted and uncompressed, yet outliers are clear and distinct along the periph...
Observational studies of causal effects often use multivariate matching to control imbalances in measured covariates. For instance, using network optimization, one may seek the closest possible pairing for key covariates among all matches that balance a propensity score and finely balance a nominal covariate, perhaps one with many categories. This...
Multivariate matching has two goals: (i) to construct treated and control groups that have similar distributions of observed covariates, and (ii) to produce matched pairs or sets that are homogeneous in a few key covariates. When there are only a few binary covariates, both goals may be achieved by matching exactly for these few covariates. Commonl...
In an observational study, the treatment received and the outcome exhibited may be associated in the absence of an effect caused by the treatment, even after controlling for observed covariates. Two tactics are common: (i) a test for unmeasured bias may be obtained using a secondary outcome for which the effect is known, and (ii) a sensitivity anal...
A quantitative study of treatment effects may form many matched pairs of a treated subject and an untreated control who look similar in terms of covariates measured prior to treatment. When treatments are not randomly assigned, one inevitable concern is that individuals who look similar in measured covariates may be dissimilar in unmeasured covaria...
Objective:
To determine if surgery and anesthesia in the elderly may promote Alzheimer's Disease and Related Dementias (ADRD).
Background:
There is a substantial conflicting literature concerning the hypothesis that surgery and anesthesia promotes ADRD. Much of the literature is confounded by indications for surgery or has small sample size. Thi...
Introduction:
This study develops a measure of Alzheimer's disease and related dementias (ADRD) using Medicare claims.
Methods:
Validation resembles the approach of the American Psychological Association, including (1) content validity, (2) construct validity, and (3) predictive validity.
Results:
We found that four items-a Medicare claim reco...
Background
Nursing resources, such as staffing ratios and skill mix, vary across hospitals. Better nursing resources have been linked to better patient outcomes but are assumed to increase costs. The value of investments in nursing resources, in terms of clinical benefits relative to costs, is unclear.Objective
To determine whether there are differ...
Design sensitivity is used to quantify the effectiveness of devices discussed in Chap. 5. Several of those devices anticipate a particular pattern of results, perhaps coherence among several outcomes, or a dose–response relationship. To what extent do these considerations reduce sensitivity to unmeasured biases? Must a coherent pattern of associati...
The basic tools of multivariate matching are introduced, including the propensity score, distance matrices, calipers imposed using a penalty function, optimal matching, matching with multiple controls, and full matching. The tools are illustrated with a tiny example from genetic toxicology (n = 46), an example that is so small that one can keep tra...
“Make your theories elaborate” in observational studies, argued R.A. Fisher, so that the many predictions of such a theory may disambiguate the association between treatment and outcome. How should one plan the analysis of an observational study to check the predictions of an elaborate theory?
In an experiment, power and sample size calculations anticipate the outcome of a statistical test that will be performed when the experimental data are available for analysis. In parallel, in an observational study, the power of a sensitivity analysis anticipates the outcome of a sensitivity analysis that will be performed when the observational da...
When a treatment may be given at various times, it is important to form matched pairs or sets in which subjects are similar prior to treatment but avoid matching on events that were subsequent to treatment. This is done using risk-set matching, in which a newly treated subject at time t is matched to one or more controls who are not yet treated at...
Before R.A. Fisher introduced randomized experimentation, the literature on causal inference emphasized reduction of heterogeneity of experimental units. To what extent is heterogeneity relevant to causal claims in observational studies when random assignment of treatments is unethical or infeasible?
Transparency means making evidence evident. An observational study that is not transparent may be overwhelming or intimidating, but it is unlikely to be convincing. Several aspects of transparency are briefly discussed.
Three design tasks may usefully follow matching and precede planning of the analysis. Splitting the sample of I pairs into a small planning sample and a large analysis sample may aid in planning the analysis in a manner that increases the design sensitivity. If there will be analytic adjustments for some unmatched variables, it is prudent to check...
A counterclaim disputes the claim that an association between treatment received and outcome exhibited reflects an effect caused by the treatment. Some counterclaims undermine themselves. A supplemental statistical analysis may demonstrate this.
Observational studies differ from experiments in that randomization is not used to assign treatments. How were treatments assigned? This chapter introduces two simple models for treatment assignment in observational studies. The first model is useful but naïve: it says that people who look comparable are comparable. The second model speaks to a cen...
Optimal matching without groups, or optimal nonbipartite matching, offers many additional options for matched designs in both observational studies and experiments. One starts with a square, symmetric distance matrix with one row and one column for each subject recording the distance between any two subjects. Then the subjects are divided into pair...
Having constructed a matched control group, one must check that it is satisfactory, in the sense of balancing the observed covariates. If some covariates are not balanced, then adjustments are made to bring them into balance. Three adjustments are near-exact matching, exact matching, and the use of small penalties. Exact matching has a special role...
Fine balance means constraining a match to balance a nominal variable, without restricting who is matched to whom, when matching to minimize a distance between treated and control subjects. It may be applied to: (1) a nominal variable with many levels that is difficult to balance using propensity scores, (2) a rare binary variable that is difficult...
What features of the design of an observational study affect its ability to distinguish a treatment effect from bias due to an unmeasured covariate uij? This topic, which is the focus of Part III of the book, is sketched in informal terms in the current chapter. An opportunity is an unusual setting in which there is less confounding with unobserved...
Large effects in moderate to large studies are typically insensitive to small and moderate unobserved biases, but the concept of a “large effect” is vague. What if most subjects are not much affected by treatment, but a small fraction, perhaps 10% or 20% of subjects, are strongly affected? On average, such an effect may be small, but not at all sma...
Simple calculations in the statistical language R illustrate the computations involved in one simple form of multivariate matching. The focus is on how matching is done, not on the many aspects of the design of an observational study. The process is made tangible by describing it in detail, step-by-step, closely inspecting intermediate results; how...
This introductory chapter mentions some of the issues that arise in observational studies and describes a few well designed studies. Section 1.7 outlines the book, describes its structure, and suggests alternative ways to read it.
An observational study is an empiric investigation of treatment effects when random assignment to treatment or control is not feasible. Because observational studies are structured to resemble simple randomized experiments, an understanding of the role randomization plays in experiments is important as background. As a prelude to the discussion of...
The choice of one test statistic rather than another affects the design sensitivity, as it affects the power and efficiency of a randomization test. This is demonstrated by computing the design sensitivity for several competing test statistics in the same sampling situation. Familiar test statistics, such as Wilcoxon’s signed rank statistic and the...
In a well-designed experiment or observational study, competing theories make conflicting predictions. Several examples, some quite old, are used to illustrate. Also discussed are: the goals of replication, empirical studies of reasons for effects, and the importance of systemic knowledge in eliminating errors.
An observational study has two evidence factors if it permits two essentially independent tests of the null hypothesis of no treatment effect, where each test is unaffected by some unmeasured bias that would invalidate the other test. Because the two tests are essentially independent, the evidence they provide—their hypothesis tests and sensitivity...
As a prelude to several chapters describing the construction of a matched control group, the current chapter presents an example of a matched observational study as it might (and did) appear in a scientific journal. When reporting a matched observational study, the matching methods are described very briefly in the Methods section. In more detail,...
In an observational study matched for observed covariates, an association between treatment received and outcome exhibited may indicate not an effect caused by the treatment, but merely some bias in the allocation of treatments to individuals within matched pairs. The evidence that distinguishes moderate biases from causal effects is unevenly dispe...
We show that the strength of an instrument is incompletely characterized by the proportion of compliers, and we propose and evaluate new methods that extract more information from certain settings with comparatively few compliers. Specifically, we demonstrate that, for a fixed small proportion of compliers, the presence of an equal number of always...
Background
There are known clinical benefits associated with investments in nursing. Less is known about their value.
Aims
To compare surgical patient outcomes and costs in hospitals with better versus worse nursing resources and to determine if value differs across these hospitals for patients with different mortality risks.
Methods
Retrospectiv...
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 method’s performance in terms of design sensitivity and simulation. In the application, we c...
A study has two evidence factors if it permits two statistically independent inferences about one treatment effect such that each factor is immune to some bias that would invalidate the other factor. Because the two factors are statistically independent, the evidence they provide may be combined using methods associated with meta-analysis for indep...
This second edition of Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical o...
Background:
Teaching hospitals typically pioneer investment in new technology and cultivate workforce characteristics generally associated with better quality, but the value of this extra investment is unclear.
Objective:
Compare outcomes and costs between major teaching and non-teaching hospitals by closely matching on patient characteristics....
Objective:
To compare outcomes and costs between major teaching and nonteaching hospitals on a national scale by closely matching on patient procedures and characteristics.
Background:
Teaching hospitals have been shown to often have better quality than nonteaching hospitals, but cost and value associated with teaching hospitals remains unclear....
Using a small example as an illustration, this article reviews multivariate matching from the perspective of a working scientist who wishes to make effective use of available methods. The several goals of multivariate matching are discussed. Matching tools are reviewed, including propensity scores, covariate distances, fine balance, and related met...
Background:
Children with complex chronic conditions (CCCs) utilize a disproportionate share of hospital resources.
Objective:
We asked whether some hospitals display a significantly different pattern of resource utilization than others when caring for similar children with CCCs admitted for medical diagnoses.
Research design:
Using Pediatric...
Objective:
To determine whether outcomes achieved by new surgeons are attributable to inexperience or to differences in the context in which care is delivered and patient complexity.
Background:
Although prior studies suggest that new surgeon outcomes are worse than those of experienced surgeons, factors that underlie these phenomena are poorly...
Multivariate matching in observational studies tends to view covariate differences symmetrically: a difference in age of 10 years is thought equally problematic whether the treated subject is older or younger than the matched control. If matching is correcting an imbalance in age, such that treated subjects are typically older than controls, then t...
MINI: Duty hour reform resulted in substantial changes in surgical education. In this difference-in-differences study, we examine the outcomes of patients treated by new surgeons who trained before and after duty reform. New surgeons trained after the duty hour reform achieved similar clinical results to those trained before the reform when compare...
Observational or nonrandomized studies of treatment effects are often constructed with the aid of polynomial-time algorithms that optimally form matched treatment-control pairs or matched sets. Because each observational comparison may potentially be affected by bias, investigators often reinforce a single comparison with an additional comparison t...
Sensitivity bounds for randomization inferences exist in several important cases, such as matched pairs with any type of outcome or binary outcomes with any type of stratification, but computationally feasible bounds for any outcome in any stratification are not currently available. For instance, with 20 strata, some large, others small, there is n...
Policy Points
• Patients with low socioeconomic status (SES) experience poorer survival rates after diagnosis of breast cancer, even when enrolled in Medicare and Medicaid.
• Most of the difference in survival is due to more advanced cancer on presentation and the general poor health of lower SES patients, while only a very small fraction of the S...
In observational studies of treatment effects, it is common to have several outcomes, perhaps of uncertain quality and relevance, each purporting to measure the effect of the treatment. A single planned combination of several outcomes may increase both power and insensitivity to unmeasured bias when the plan is wisely chosen, but it may miss opport...
Background:
There are numerous definitions of multimorbidity (MM). None systematically examines specific comorbidity combinations accounting for multiple testing when exploring large datasets.
Objectives:
Develop and validate a list of all single, double, and triple comorbidity combinations, with each individual qualifying comorbidity set (QCS)...
Background
Coronary atherosclerosis raises the risk of acute myocardial infarction (AMI), and is usually included in AMI risk‐adjustment models. Percutaneous coronary intervention (PCI) does not cause atherosclerosis, but may contribute to the notation of atherosclerosis in administrative claims. We investigated how adjustment for atherosclerosis a...
Table S1. Creation of Study Cohort
Table S2. Characteristics of the Study Cohort
Table S3. Hierarchical Models With and Without Atherosclerosis
Table S4. Logistic Models Predicting 30‐Day Mortality
Table S5. Directly Standardized Analysis of Logistic Models Comparing Outcomes at PCI Hospitals and Non‐PCI Hospitals
Table S6. Adjusted Outcome Ra...
The general structure of evidence factors is examined in terms of the knit product of two permutation groups. An observational or nonrandomized study of treatment effects has two evidence factors if it permits two (nearly) independent tests of the null hypothesis of no treatment effect and two (nearly) independent sensitivity analyses for those tes...
Causal effects are commonly defined as comparisons of the potential outcomes under treatment and control, but this definition is threatened by the possibility that the treatment or control condition is not well-defined, existing instead in more than one version. A simple, widely applicable analysis is proposed to address the possibility that the tr...
Weak instruments produce causal inferences that are sensitive to small failures of the assumptions underlying an instrumental variable, so strong instruments are preferred. The possibility of strengthening an instrument at the price of a reduced sample size has been proposed in the statistical literature and used in the medical literature, but ther...
Kidney transplant recipients often receive antibody induction. Previous studies of induction therapy were often limited by short follow-up and/or absence of information about complications. After linking Organ Procurement and Transplantation Network data with Medicare claims, we compared outcomes between three induction therapies for kidney recipie...
We discuss observational studies that test many causal hypotheses, either hypotheses about many outcomes or many treatments. To be credible an observational study that tests many causal hypotheses must demonstrate that its conclusions are neither artifacts of multiple testing nor of small biases from nonrandom treatment assignment. In a sense that...
Background:
With increasing Medicaid coverage, it has become especially important to determine if racial differences exist within the Medicaid system. We asked if disparities exist in hospital practice and patient outcomes between matched black and white Medicaid children with chronic conditions undergoing surgery.
Study design:
A matched cohort...
Effect modification occurs when the magnitude or stability of a treatment effect varies as a function of an observed covariate. Generally, larger and more stable treatment effects are insensitive to larger biases from unmeasured covariates, so a causal conclusion may be considerably firmer if effect modification is noted when it occurs. We propose...
Bayesian models are increasingly fit to large administrative data sets and then used to make individualized recommendations. In particular, Medicare's Hospital Compare webpage provides information to patients about specific hospital mortality rates for a heart attack or Acute Myocardial Infarction (AMI). Hospital Compare's current recommendations a...
Background and objectives:
Black children with asthma comprise one-third of all asthma patients in Medicaid. With increasing Medicaid coverage, it has become especially important to monitor Medicaid for differences in hospital practice and patient outcomes by race.
Methods:
A multivariate matched cohort design, studying 11 079 matched pairs of c...
Objectives:
With differential payment between Medicaid and Non-Medicaid services, we asked whether style-of-practice differs between similar Medicaid and Non-Medicaid children with complex chronic conditions (CCCs) undergoing surgery.
Summary of background data:
Surgery in children with CCCs accounts for a disproportionately large percentage of...
In a sensitivity analysis in an observational study with a binary outcome, is it better to use all of the data or to focus on subgroups that are expected to experience the largest treatment effects? The answer depends on features of the data that may be difficult to anticipate, a trade-off between unknown effect-sizes and known sample sizes. We pro...
In an observational study of the effects caused by treatments, a sensitivity analysis asks about the magnitude of bias from unmeasured covariates that would need to be present to alter the conclusions of a naive analysis that presumes adjustments for measured covariates remove all biases. When there are two or more outcomes in an observational stud...
Fisher tested the fit of Gaussian linear models using replicated observations. We refine this method by (1) constructing near-replicates using an optimal nonbipartite matching and (2) defining a distance that focuses on predictors important to the model's predictions. Near-replicates may not exist unless the predictor set is low-dimensional; the te...
Importance:
Asthma is the most prevalent chronic illness among children, remaining a leading cause of pediatric hospitalizations and representing a major financial burden to many health care systems.
Objective:
To implement a new auditing process examining whether differences in hospital practice style may be associated with potential resource s...
Background and objectives:
With American children experiencing increased Medicaid coverage, it has become especially important to determine if practice patterns differ between Medicaid and non-Medicaid patients. Auditing such potential differences must carefully compare like patients to avoid falsely identifying suspicious practice patterns. We as...
There is effect modification if the magnitude or stability of a treatment effect varies systematically with the level of an observed covariate. \ A larger or more stable treatment effect is typically less sensitive to bias from unmeasured covariates, so it is important to recognize effect modification when it is present. \ We illustrate a recent pr...
Objective:
To improve the predictions provided by Medicare's Hospital Compare (HC) to facilitate better informed decisions regarding hospital choice by the public.
Data sources/setting:
Medicare claims on all patients admitted for Acute Myocardial Infarction between 2009 through 2011.
Study design:
Cohort analysis using a Bayesian approach, co...
Modern methods construct a matched sample by minimizing the total cost of a flow in a network, finding a pairing of treated and control individuals that minimizes the sum of within-pair covariate distances subject to constraints that ensure distributions of covariates are balanced. In aggregate, these methods work well; however, they can exhibit a...
Objective:
To develop a method to allow a hospital to compare its performance using its entire patient population to the outcomes of very similar patients treated elsewhere.
Data sources/setting:
Medicare claims in orthopedics and common general, gynecologic, and urologic surgery from Illinois, New York, and Texas from 2004 to 2006.
Study desig...
Importance
The literature suggests that hospitals with better nursing work environments provide better quality of care. Less is known about value (cost vs quality).Objectives
To test whether hospitals with better nursing work environments displayed better value than those with worse nursing environments and to determine patient risk groups associ...
Bayesian models are increasing fit to large administrative data sets and then
used to make individualized recommendations. For instance, Medicare's Hospital
Compare webpage provides information to patients about specific hospital
mortality rates for a heart attack or Acute Myocardial Infarction (AMI).
Hospital Compare's current recommendations are...
An effect modifier is a pretreatment covariate that affects the magnitude of the treatment effect or its stability. When there
is effect modification, an overall test that ignores an effect modifier may be more sensitive to unmeasured bias than a test
that combines results from subgroups defined by the effect modifier. If there is effect modificati...
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...
The informal folklore of observational studies claims that if an irrelevant observed covariate is left uncontrolled, say unmatched, then it will influence treatment assignment in haphazard ways, thereby diminishing the biases from unmeasured covariates. We prove a result along these lines: it is true, in a certain sense, to a limited degree, under...
Claims based on observational studies that a treatment has certain effects are often met with counterclaims asserting that the treatment is without effect, that associations are produced by biased treatment assignment. Some counterclaims undermine themselves in the following specific sense: presuming the counterclaim to be true may strengthen the s...
The propensity score is the conditional probability of exposure to treatment rather than control given observed covariates, or more generally, the conditional probability of selection into a group given observed covariates. It is used in an effort to adjust for nonrandom treatment assignment or nonrandom selection. Matching or stratifying on the sc...
Citations
... However, they are in line with numbers being reported for participants taking part in a study using the ClinSearch Acceptability Score Test ® conducted by Vallet et al. [39]. The rather vulnerable profile of included old participants might be due to the recruitment site being a hospital, thereby including sicker people compared to the general population [40]. As older adults are commonly dependent on the intake of medicines, these rather vulnerable participants are a good point of reference. ...
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... The results in Table 6 show that before matching, the mean bias was 21.3 for both the matching algorithms used and after matching, the mean bias was reduced to 3.5, 3.9 and 5.2 for nearest neighbor, kernel and radius/caliper matching, respectively. The percentage reduction bias according to Rosenbaum and Rubin [46] is recommended to be above 20%, and for all the matching methods used gave a higher percentage reduction biasness of 84%, 82% and 76% with nearest neighbor, kernel and radius matching indicating a robust reduction in selection bias. The pseudo-R square also reduced after matching from original value of 0.088 to 0.006, 0.004, and 0.007, respectively, for nearest neighbor, kernel and radius matching methods. ...
... Two venerable strategies for detecting unmeasured confounding in observational studies use multiple control groups or supplementary outcomes for which the treatment is thought to have no effect; see Campbell (1969), Reynolds and West (1987), Rosenbaum (1987Rosenbaum ( , 1989Rosenbaum ( , 2002Rosenbaum ( , 2020Rosenbaum ( , 2022, Tchetgen Tchetgen (2014), and Weiss (2002), among others. Here, we employ both strategies, in a new, near-optimal study design. ...
... Popular matching methods include Rosenbaum (1989), Iacus et al. (2011), and Diamond and Sekhon (2013). Recent optimal matching methods that capitalize on modern optimization using network flows include Hansen and Klopfer (2006), Pimentel et al. (2015), Yu et al. (2020), and Yu and Rosenbaum (2022). For recent optimization matching methods that leverage modern optimization using generic integer programming, see Zubizarreta (2012), Zubizarreta et al. (2014), and Cohn and Zubizarreta (2022). ...
... Nogueira et al. (2022) found that the racial disparities narrowed but still remained significant after further matching on income. This approach is known as tapered matching in the statistical matching literature (see, e.g., Daniel et al., 2008;Rosenbaum and Silber, 2013;Yu et al., 2021). Tapered matching constructs multiple matched comparison groups that increasingly resemble the treated group by controlling for additional covariates and is intended to answer various research questions about the root causes of the observed outcome difference in two comparison groups. ...
... More recently, Zhang et al. (2021) proposed a minimum-cost network flow algorithm built upon a tripartite graph for multivariate matching. The network infrastructure of a generic tripartite network is depicted in Figure 2 and can be formalized as follows. ...
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... Under a similar matched pairs framework, Rosenbaum (2021) considers a sensitivity analysis for a single primary outcome and shows how a null control outcome can sometimes increase the evidence for the robustness of the primary outcome. One potential concern with their sensitivity analyses for weighting estimators, is that the sensitivity analyses are implicitly based on the overly conservative assumption that unmeasured confounders explain nearly all the variation in the outcomes. ...
... Auditability allows domain experts to validate the estimation procedure, argue about the violation of key assumptions, and determine whether the analysis is trustworthy. Parikh et al. [2022a] and Yu et al. [2021] showed that the audit of matched groups using external unstructured data is crucial in high-stakes healthcare and social science scenarios. Since causal analyses often depend on untestable assumptions, it is critical to determine whether all important confounders are accounted for, if data are processed correctly, and whether the treatment and control units in the matched groups are cohesive enough to be comparable [Parikh et al., 2022c]. ...
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... However, the potential biases in one study design would hopefully not influence estimates of the other approach: the biases are presumed to be unrelated. When biases are unrelated, it may be possible to obtain two or more estimates using different estimation strategies from the same single study sample; these have been referred to as 'evidence factors' [67], which can be meta-analysed to increase statistical power. An example is a study based on the UK Biobank of the effect of years of education on health and health behaviours [68,69]. ...
... Besides, elderly surgical patients are more likely to have preexisting AD or be at risk for developing it (Işik, 2015). On the other hand, there are clinical studies that fail to support the association of anesthesia and surgery with AD (Avidan and Evers, 2011;Sprung et al., 2013Sprung et al., , 2020Aiello Bowles et al., 2016;Silber et al., 2020). However, some animal experiments have been reported where individuals with AD may develop cognitive impairment after anesthesia and surgery, but the mechanism is not clear (Zhang et al., 2017;Miao et al., 2018;Kim et al., 2021). ...
