Doubly-robust Nonparametric Inference on the Average Treatment Effect

Doubly-robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double-robustness does not readily extend to inference. We present a general theoretical study of the behavior of doubly-robust estimators of an average treatment effect when one of the nuisance parameters is inconsistently estimated. We contrast different approaches for constructing such estimators and investigate the extent to which they may be modified to also allow doubly-robust inference. We find that while targeted maximum likelihood estimation can be used to solve this problem very naturally, common alternative frameworks appear to be inappropriate for this purpose. We provide a theoretical study and a numerical evaluation of the alternatives considered. Our simulations highlight the need and usefulness of these approaches in practice, while our theoretical developments have broad implications for the construction of estimators that permit doubly-robust inference in other problems

Potential Influence of Advance Care Planning and Palliative Care Consultation on ICU Costs for Patients With Chronic and Serious Illness.

OBJECTIVES: To estimate the potential ICU-related cost savings if in-hospital advance care planning and ICU-based palliative care consultation became standard of care for patients with chronic and serious illness. DESIGN AND SETTING: Decision analysis using literature estimates and inpatient administrative data from Premier. PATIENTS: Patients with chronic, life-limiting illness admitted to a hospital within the Premier network. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Using Premier data (2008-2012), ICU resource utilization and costs were tracked over a 1-year time horizon for 2,097,563 patients with chronic life-limiting illness. Using a Markov microsimulation model, we explored the potential cost savings from the hospital system perspective under a variety of scenarios by varying the interventions\u27 efficacies and availabilities. Of 2,097,563 patients, 657,825 (31%) used the ICU during the 1-year time horizon; mean ICU spending per patient was 11.3k (SD, 17.6k). In the base-case analysis, if in-hospital advance care planning and ICU-based palliative care consultation were systematically provided, we estimated a mean reduction in ICU costs of 2.8k (SD, 14.5k) per patient and an ICU cost saving of 25%. Among the simulated patients who used the ICU, the receipt of both interventions could have resulted in ICU cost savings of 1.9 billion, representing a 6% reduction in total hospital costs for these patients. CONCLUSIONS: In-hospital advance care planning and palliative care consultation have the potential to result in significant cost savings. Studies are needed to confirm these findings, but our results provide guidance for hospitals and policymakers

Online Cross-Validation-Based Ensemble Learning

Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to identify the algorithm with the best performance. We show that by basing estimates on the cross-validation-selected algorithm, we are asymptotically guaranteed to perform as well as the true, unknown best-performing algorithm. We provide extensions of this approach including online estimation of the optimal ensemble of candidate online estimators. We illustrate the practical performance of our methods using simulations and a real data example where we make streaming predictions of infectious disease incidence using data from a large database

Assessing trends in vaccine efficacy by pathogen genetic distance

Preventive vaccines are an effective public health intervention for reducing the burden of infectious diseases, but have yet to be developed for several major infectious diseases. Vaccine sieve analysis studies whether and how the efficacy of a vaccine varies with the genetics of the infectious pathogen, which may help guide future vaccine development and deployment. A standard statistical approach to sieve analysis compares the effect of the vaccine to prevent infection and disease caused by pathogen types defined dichotomously as genetically near or far from a reference pathogen strain inside the vaccine construct. For example, near may be defined by amino acid identity at all amino acid positions considered in a multiple alignment and far defined by at least one amino acid difference. An alternative approach is to study the efficacy of the vaccine as a function of genetic distance from a pathogen to a reference vaccine strain where the distance cumulates over the set of amino acid positions. We propose a nonparametric method for estimating and testing the trend in the effect of a vaccine across genetic distance. We illustrate the operating characteristics of the estimator via simulation and apply the method to a recent preventive malaria vaccine efficacy trial

Patterns of Cost for Patients Dying in the Intensive Care Unit and Implications for Cost Savings of Palliative Care Interventions.

BACKGROUND: Terminal intensive care unit (ICU) stays represent an important target to increase value of care. OBJECTIVE: To characterize patterns of daily costs of ICU care at the end of life and, based on these patterns, examine the role for palliative care interventions in enhancing value. DESIGN: Secondary analysis of an intervention study to improve quality of care for critically ill patients. SETTING/PATIENTS: 572 patients who died in the ICU between 2003 and 2005 at a Level-1 trauma center. METHODS: Data were linked with hospital financial records. Costs were categorized into direct fixed, direct variable, and indirect costs. Patterns of daily costs were explored using generalized estimating equations stratified by length of stay, cause of death, ICU type, and insurance status. Estimates from the literature of effects of palliative care interventions on ICU utilization were used to simulate potential cost savings under different time horizons and reimbursement models. MAIN RESULTS: Mean cost for a terminal ICU stay was 39.3K ± 45.1K. Direct fixed costs represented 45% of total hospital costs, direct variable costs 20%, and indirect costs 34%. Day of admission was most expensive (mean 9.6K ± 7.6K); average cost for subsequent days was 4.8K ± 3.4K and stable over time and patient characteristics. CONCLUSIONS: Terminal ICU stays display consistent cost patterns across patient characteristics. Savings can be realized with interventions that align care with patient preferences, helping to prevent unwanted ICU utilization at end of life. Cost modeling suggests that implications vary depending on time horizon and reimbursement models