Jasjeet S. Sekhon

Referral to an Extracorporeal Membrane Oxygenation Center and Mortality Among Patients With Severe 2009 Influenza A(H1N1)

CONTEXT Extracorporeal membrane oxygenation (ECMO) can support gas exchange in patients with severe acute respiratory distress syndrome (ARDS), but its role has remained controversial. ECMO was used to treat patients with ARDS during the 2009 influenza A(H1N1) pandemic. OBJECTIVE To compare the hospital mortality of patients with H1N1-related ARDS referred, accepted, and transferred for ECMO with matched patients who were not referred for ECMO. DESIGN, SETTING, AND PATIENTS A cohort study in which ECMO-referred patients were defined as all patients with H1N1-related ARDS who were referred, accepted, and transferred to 1 of the 4 adult ECMO centers in the United Kingdom during the H1N1 pandemic in winter 2009-2010. The ECMO-referred patients and the non-ECMO-referred patients were matched using data from a concurrent, longitudinal cohort study (Swine Flu Triage study) of critically ill patients with suspected or confirmed H1N1. Detailed demographic, physiological, and comorbidity data were used in 3 different matching techniques (individual matching, propensity score matching, and GenMatch matching). MAIN OUTCOME MEASURE Survival to hospital discharge analyzed according to the intention-to-treat principle. RESULTS Of 80 ECMO-referred patients, 69 received ECMO (86.3%) and 22 died (27.5%) prior to discharge from the hospital. From a pool of 1756 patients, there were 59 matched pairs of ECMO-referred patients and non-ECMO-referred patients identified using individual matching, 75 matched pairs identified using propensity score matching, and 75 matched pairs identified using GenMatch matching. The hospital mortality rate was 23.7% for ECMO-referred patients vs 52.5% for non-ECMO-referred patients (relative risk [RR], 0.45 [95% CI, 0.26-0.79]; P = .006) when individual matching was used; 24.0% vs 46.7%, respectively (RR, 0.51 [95% CI, 0.31-0.81]; P = .008) when propensity score matching was used; and 24.0% vs 50.7%, respectively (RR, 0.47 [95% CI, 0.31-0.72]; P = .001) when GenMatch matching was used. The results were robust to sensitivity analyses, including amending the inclusion criteria and restricting the location where the non-ECMO-referred patients were treated. CONCLUSION For patients with H1N1-related ARDS, referral and transfer to an ECMO center was associated with lower hospital mortality compared with matched non-ECMO-referred patients.

When Natural Experiments Are Neither Natural nor Experiments

Natural experiments help to overcome some of the obstacles researchers face when making causal inferences in the social sciences. However, even when natural interventions are randomly assigned, some of the treatment–control comparisons made available by natural experiments may not be valid. We offer a framework for clarifying the issues involved, which are subtle and often overlooked. We illustrate our framework by examining four different natural experiments used in the literature. In each case, random assignment of the intervention is not sufficient to provide an unbiased estimate of the causal effect. Additional assumptions are required that are problematic. For some examples, we propose alternative research designs that avoid these conceptual difficulties.

Endogeneity in Probit Response Models

We look at conventional methods for removing endogeneity bias in regression models, including the linear model and the probit model. It is known that the usual Heckman two-step procedure should not be used in the probit model: from a theoretical perspective, it is unsatisfactory, and likelihood methods are superior. However, serious numerical problems occur when standard software packages try to maximize the biprobit likelihood function, even if the number of covariates is small. We draw conclusions for statistical practice. Finally, we prove the conditions under which parameters in the model are identifiable. The conditions for identification are delicate; we believe these results are new.

Quality Meets Quantity: Case Studies, Conditional Probability, and Counterfactuals

In contrast to statistical methods, a number of case study methods—collectively referred to as Mill’s methods, used by generations of social science researchers—only consider deterministic relationships. They do so to their detriment because heeding the basic lessons of statistical inference can prevent serious inferential errors. Of particular importance is the use of conditional probabilities to compare relevant counterfactuals. A prominent example of work using Mill’s methods is Theda Skocpol’s States and Social Revolutions. Barbara Geddes’s widely assigned critique of Skocpol’s claim of a causal relationship between foreign threat and social revolution is valid if this relationship is considered to be deterministic. If, however, we interpret Skocpol’s hypothesized causal relationship to be probabilistic, Geddes’s data support Skocpol’s hypothesis. But Skocpol, unlike Geddes, failed to provide the data necessary to compare conditional probabilities. Also problematic for Skocpol is the fact that when one makes causal inferences, conditional probabilities are of interest only insofar as they provide information about relevant counterfactuals.

The relative performance of targeted maximum likelihood estimators.

There is an active debate in the literature on censored data about the relative performance of model based maximum likelihood estimators, IPCW-estimators, and a variety of double robust semiparametric efficient estimators. Kang and Schafer (2007) demonstrate the fragility of double robust and IPCW-estimators in a simulation study with positivity violations. They focus on a simple missing data problem with covariates where one desires to estimate the mean of an outcome that is subject to missingness. Responses by Robins, et al. (2007), Tsiatis and Davidian (2007), Tan (2007) and Ridgeway and McCaffrey (2007) further explore the challenges faced by double robust estimators and offer suggestions for improving their stability. In this article, we join the debate by presenting targeted maximum likelihood estimators (TMLEs). We demonstrate that TMLEs that guarantee that the parametric submodel employed by the TMLE procedure respects the global bounds on the continuous outcomes, are especially suitable for dealing with positivity violations because in addition to being double robust and semiparametric efficient, they are substitution estimators. We demonstrate the practical performance of TMLEs relative to other estimators in the simulations designed by Kang and Schafer (2007) and in modified simulations with even greater estimation challenges.

Cemented, cementless, and hybrid prostheses for total hip replacement: cost effectiveness analysis

Objective To compare the cost effectiveness of the three most commonly chosen types of prosthesis for total hip replacement. Design Lifetime cost effectiveness model with parameters estimated from individual patient data obtained from three large national databases. Setting English National Health Service. Participants Adults aged 55 to 84 undergoing primary total hip replacement for osteoarthritis. Interventions Total hip replacement using either cemented, cementless, or hybrid prostheses. Main outcome measures Cost (£), quality of life (EQ-5D-3L, where 0 represents death and 1 perfect health), quality adjusted life years (QALYs), incremental cost effectiveness ratios, and the probability that each prosthesis type is the most cost effective at alternative thresholds of willingness to pay for a QALY gain. Results Lifetime costs were generally lowest with cemented prostheses, and postoperative quality of life and lifetime QALYs were highest with hybrid prostheses. For example, in women aged 70 mean costs were £6900 (

A MATCHING METHOD FOR IMPROVING COVARIATE BALANCE IN COST-EFFECTIVENESS ANALYSES.

11 000; €8200) for cemented prostheses, £7800 for cementless prostheses, and £7500 for hybrid prostheses; mean postoperative EQ-5D scores were 0.78, 0.80, and 0.81, and the corresponding lifetime QALYs were 9.0, 9.2, and 9.3 years. The incremental cost per QALY for hybrid compared with cemented prostheses was £2500. If the threshold willingness to pay for a QALY gain exceeded £10 000, the probability that hybrid prostheses were most cost effective was about 70%. Hybrid prostheses have the highest probability of being the most cost effective in all subgroups, except in women aged 80, where cemented prostheses were most cost effective. Conclusions Cemented prostheses were the least costly type for total hip replacement, but for most patient groups hybrid prostheses were the most cost effective. Cementless prostheses did not provide sufficient improvement in health outcomes to justify their additional costs.

Evaluating Health Care Programs by Combining Cost with Quality of Life Measures: A Case Study Comparing Capitation and Fee for Service

In cost-effectiveness analyses (CEA) that use randomized controlled trials (RCTs), covariates of prognostic importance may be imbalanced and warrant adjustment. In CEA that use non-randomized studies (NRS), the selection on observables assumption must hold for regression and matching methods to be unbiased. Even in restricted circumstances when this assumption is plausible, a key concern is how to adjust for imbalances in observed confounders. If the propensity score is misspecified, the covariates in the matched sample will be imbalanced, which can lead to conditional bias. To address covariate imbalance in CEA based on RCTs and NRS, this paper considers Genetic Matching. This matching method uses a search algorithm to directly maximize covariate balance. We compare Genetic and propensity score matching in Monte Carlo simulations and two case studies, CEA of pulmonary artery catheterization, based on an RCT and an NRS. The simulations show that Genetic Matching reduces the conditional bias and root mean squared error compared with propensity score matching. Genetic Matching achieves better covariate balance than the unadjusted analyses of the RCT data. In the NRS, Genetic Matching improves on the balance obtained from propensity score matching and gives substantively different estimates of incremental cost-effectiveness. We conclude that Genetic Matching can improve balance on measured covariates in CEA that use RCTs and NRS, but with NRS, this will be insufficient to reduce bias; the selection on observables assumption must also hold.

Law and Data : The Butterfly Ballot Episode

OBJECTIVE To demonstrate cost-effectiveness analysis (CEA) for evaluating different reimbursement models. DATA SOURCES/STUDY SETTING The CEA used an observational study comparing fee for service (FFS) versus capitation for Medicaid cases with severe mental illness (n=522). Under capitation, services were provided either directly (direct capitation [DC]) by not-for-profit community mental health centers (CMHC), or in a joint venture between CMHCs and a for-profit managed behavioral health organization (MBHO). STUDY DESIGN A nonparametric matching method (genetic matching) was used to identify those cases that minimized baseline differences across the groups. Quality-adjusted life years (QALYs) were reported for each group. Incremental QALYs were valued at different thresholds for a QALY gained, and combined with cost estimates to plot cost-effectiveness acceptability curves. PRINCIPAL FINDINGS QALYs were similar across reimbursement models. Compared with FFS, the MBHO model had incremental costs of -

Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach

1,991 and the probability that this model was cost-effective exceeded 0.90. The DC model had incremental costs of