Terrance Dean Savitsky

The frequency and cost of treatment perceived to be futile in critical care.

IMPORTANCE Physicians often perceive as futile intensive care interventions that prolong life without achieving an effect that the patient can appreciate as a benefit. The prevalence and cost of critical care perceived to be futile have not been prospectively quantified. OBJECTIVE To quantify the prevalence and cost of treatment perceived to be futile in adult critical care. DESIGN, SETTING, AND PARTICIPANTS To develop a common definition of futile care, we convened a focus group of clinicians who care for critically ill patients. On a daily basis for 3 months, we surveyed critical care specialists in 5 intensive care units (ICUs) at an academic health care system to identify patients whom the physicians believed were receiving futile treatment. Using a multivariate model, we identified patient and clinician characteristics associated with patients perceived to be receiving futile treatment. We estimated the total cost of futile treatment by summing the charges of each day of receiving perceived futile treatment and converting to costs. MAIN OUTCOME AND MEASURE Prevalence of patients perceived to be receiving futile treatment. RESULTS During a 3-month period, there were 6916 assessments by 36 critical care specialists of 1136 patients. Of these patients, 904 (80%) were never perceived to be receiving futile treatment, 98 (8.6%) were perceived as receiving probably futile treatment, 123 (11%) were perceived as receiving futile treatment, and 11 (1%) were perceived as receiving futile treatment only on the day they transitioned to palliative care. The patients with futile treatment assessments received 464 days of treatment perceived to be futile in critical care (range, 1-58 days), accounting for 6.7% of all assessed patient days in the 5 ICUs studied. Eighty-four of the 123 patients perceived as receiving futile treatment died before hospital discharge and 20 within 6 months of ICU care (6-month mortality rate of 85%), with survivors remaining in severely compromised health states. The cost of futile treatment in critical care was estimated at

Collaborative learning framework for online stakeholder engagement

2.6 million. CONCLUSIONS AND RELEVANCE In 1 health system, treatment in critical care that is perceived to be futile is common and the cost is substantial.

Bayesian Hierarchical Multivariate Formulation with Factor Analysis for Nested Ordinal Data

Public and stakeholder engagement can improve the quality of both research and policy decision making. However, such engagement poses significant methodological challenges in terms of collecting and analysing input from large, diverse groups.

Bayesian nonparametric hierarchical modeling for multiple membership data in grouped attendance interventions

This article devises a Bayesian multivariate formulation for analysis of ordinal data that records teacher classroom performance along multiple dimensions to assess aspects characterizing good instruction. Study designs for scoring teachers seek to measure instructional performance over multiple classroom measurement event sessions at varied occasions using disjoint intervals within each session and employment of multiple ratings on intervals scored by different raters; a design which instantiates a nesting structure with each level contributing a source of variation in recorded scores. We generally possess little a priori knowledge of the existence or form of a sparse generating structure for the multivariate dimensions at any level in the nesting that would permit collapsing over dimensions as is done under univariate modeling. Our approach composes a Bayesian data augmentation scheme that introduces a latent continuous multivariate response linked to the observed ordinal scores with the latent response mean constructed as an additive multivariate decomposition of nested level means that permits the extraction of de-noised continuous teacher-level scores and the associated correlation matrix. A semi-parametric extension facilitates inference for teacher-level dependence among the dimensions of classroom performance under multi-modality induced by sub-groupings of rater perspectives. We next replace an inverse Wishart prior specified for the teacher covariance matrix over dimensions of instruction with a factor analytic structure to allow the simultaneous assessment of an underlying sparse generating structure. Our formulation for Bayesian factor analysis employs parameter expansion with an accompanying post-processing sign re-labeling step of factor loadings that together reduce posterior correlations among sampled parameters to improve parameter mixing in our Markov chain Monte Carlo (MCMC) scheme. We evaluate the performance of our formulation on simulated data and make an application for the assessment of the teacher covariance structure with a dataset derived from a study of middle and high school algebra teachers.

Between Large-N and Small-N Analyses: Historical Comparison of Thirty Insurgency Case Studies

We develop a dependent Dirichlet process (DDP) model for repeated measures multiple membership (MM) data. This data structure arises in studies under which an intervention is delivered to each client through a sequence of elements which overlap with those of other clients on different occasions. Our interest concentrates on study designs for which the overlaps of sequences occur for clients who receive an intervention in a shared or grouped fashion whose memberships may change over multiple treatment events. Our motivating application focuses on evaluation of the effectiveness of a group therapy intervention with treatment delivered through a sequence of cognitive behavioral therapy session blocks, called modules. An open-enrollment protocol permits entry of clients at the beginning of any new module in a manner that may produce unique MM sequences across clients. We begin with a model that composes an addition of client and multiple membership module random effect terms, which are assumed independent. Our MM DDP model relaxes the assumption of conditionally independent client and module random effects by specifying a collection of random distributions for the client effect parameters that are indexed by the unique set of module attendances. We demonstrate how this construction facilitates examining heterogeneity in the relative effectiveness of group therapy modules over repeated measurement occasions.

Bayesian nonparametric multiresolution estimation for the American Community Survey

Abstract The authors study the 30 insurgencies occurring between 1978 and 2008 using four methods crossing the qualitative/quantitative divide. The four approaches are narrative, bivariate comparison, comparative qualitative analysis, and K-medoids clustering. The quantification of qualitative data allows the authors to compare more cases than they could “hold in their heads” under a traditional small-n qualitative approach, improving the quality of the overall narrative and helping to ensure that the quantitative analyses respected the nuance of the detailed case histories. Structured data-mining reduces the dimensionality of possible explanatory factors relative to the available observations to expose patterns in the data in ways more common in large-n studies. The four analytic approaches produced similar and mutually supporting findings, leading to robust conclusions.

Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R

Bayesian hierarchical methods implemented for small area estimation focus on reducing the noise variation in published government official statistics by borrowing information among dependent response values. Even the most flexible models confine parameters defined at the finest scale to link to each data observation in a one-to-one construction. We propose a Bayesian multiresolution formulation that utilizes an ensemble of observations at a variety of coarse scales in space and time to additively nest parameters we define at a finer scale, which serve as our focus for estimation. Our construction is motivated by and applied to the estimation of

Inferring constructs of effective teaching from classroom observations: An application of Bayesian exploratory factor analysis without restrictions

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Bayesian hierarchical semiparametric modelling of longitudinal post‐treatment outcomes from open enrolment therapy groups

year period employment levels, indexed by county, from statistics published at coarser areal domains and multi-year intervals in the American Community Survey (ACS). We construct a nonparametric mixture of Gaussian processes as the prior on a set of regression coefficients of county-indexed latent functions over multiple survey years. We evaluate a modified Dirichlet process prior that incorporates county-year predictors as the mixing measure. Each county-year parameter of a latent function is estimated from multiple coarse scale observations in space and time to which it links. The multiresolution formulation is evaluated on synthetic data and applied to the ACS.

Bayesian Semi- and Non-parametric Models for Longitudinal Data with Multiple Membership Effects in R.

We present growfunctions for R that offers Bayesian nonparametric estimation models for analysis of dependent, noisy time series data indexed by a collection of domains. This data structure arises from combining periodically published government survey statistics, such as are reported in the Current Population Study (CPS). The CPS publishes monthly, by-state estimates of employment levels, where each state expresses a noisy time series. Published state-level estimates from the CPS are composed from household survey responses in a model-free manner and express high levels of volatility due to insufficient sample sizes. Existing software solutions borrow information over a modeled time-based dependence to extract a de-noised time series for each domain. These solutions, however, ignore the dependence among the domains that may be additionally leveraged to improve estimation efficiency. The growfunctions package offers two fully nonparametric mixture models that simultaneously estimate both a time and domain-indexed dependence structure for a collection of time series: (1) A Gaussian process (GP) construction, which is parameterized through the covariance matrix, estimates a latent function for each domain. The covariance parameters of the latent functions are indexed by domain under a Dirichlet process prior that permits estimation of the dependence among functions across the domains: (2) An intrinsic Gaussian Markov random field prior construction provides an alternative to the GP that expresses different computation and estimation properties. In addition to performing denoised estimation of latent functions from published domain estimates, growfunctions allows estimation of collections of functions for observation units (e.g., households), rather than aggregated domains, by accounting for an informative sampling design under which the probabilities for inclusion of observation units are related to the response variable. growfunctions includes plot functions that allow visual assessments of the fit performance and dependence structure of the estimated functions. Computational efficiency is achieved by performing the sampling for estimation functions using compiled C++.