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Health Services & Outcomes Research Methodology

Anna D Sinaiko, Timothy J Layton, Sherri Rose, Thomas G McGuire
While family purchase of health insurance may benefit insurance markets by pooling individual risk into family groups, the correlation across illness types in families could exacerbate adverse selection. We analyze the impact of family pooling on risk for health insurers to inform policy about family-level insurance plans. Using data on 8,927,918 enrollees in fee-for-service commercial health plans in the 2013 Truven MarketScan database, we compare the distribution of annual individual health spending across four pooling scenarios: (1) "Individual" where there is no pooling into families; (2) "real families" where costs are pooled within families; (3) "random groups" where costs are pooled within randomly generated small groups that mimic families in group size; and (4) "the Sims" where costs are pooled within random small groups which match families in demographics and size...
December 2017: Health Services & Outcomes Research Methodology
Elizabeth A Gilbert, Robert T Krafty, Richard J Bleicher, Brian L Egleston
Prognostic scores have been proposed as outcome based confounder adjustment scores akin to propensity scores. However, prognostic scores have not been widely used in the substantive literature. Instead, comorbidity scores, which are limited versions of prognostic scores, have been used extensively by clinical and health services researchers. A comorbidity is an existing disease an individual has in addition to a primary condition of interest, such as cancer. Comorbidity scores are used to reduce the dimension of a vector of comorbidity variables into a single scalar variable...
December 2017: Health Services & Outcomes Research Methodology
Layla Parast, Daniel F McCaffrey, Lane F Burgette, Fernando Hoces de la Guardia, Daniela Golinelli, Jeremy N V Miles, Beth Ann Griffin
While propensity score weighting has been shown to reduce bias in treatment effect estimation when selection bias is present, it has also been shown that such weighting can perform poorly if the estimated propensity score weights are highly variable. Various approaches have been proposed which can reduce the variability of the weights and the risk of poor performance, particularly those based on machine learning methods. In this study, we closely examine approaches to fine-tune one machine learning technique (generalized boosted models [GBM]) to select propensity scores that seek to optimize the variance-bias trade-off that is inherent in most propensity score analyses...
December 2017: Health Services & Outcomes Research Methodology
Rebecca A Hubbard, Eric Johnson, Jessica Chubak, Karen J Wernli, Aruna Kamineni, Andy Bogart, Carolyn M Rutter
Exposures derived from electronic health records (EHR) may be misclassified, leading to biased estimates of their association with outcomes of interest. An example of this problem arises in the context of cancer screening where test indication, the purpose for which a test was performed, is often unavailable. This poses a challenge to understanding the effectiveness of screening tests because estimates of screening test effectiveness are biased if some diagnostic tests are misclassified as screening. Prediction models have been developed for a variety of exposure variables that can be derived from EHR, but no previous research has investigated appropriate methods for obtaining unbiased association estimates using these predicted probabilities...
June 2017: Health Services & Outcomes Research Methodology
James A Sidney, Ashlin Jones, Carter Coberley, James E Pope, Aaron Wells
The objective of this research is to advance the evaluation and monetization of well-being improvement programs, offered by population health management companies, by presenting a novel method that robustly monetizes the entirety of well-being improvement within a population. This was achieved by utilizing two employers' well-being assessments with medical and pharmacy administrative claims (2010-2011) across a large national employer (n = 50,647) and regional employer (n = 6170) data sets. This retrospective study sought to monetize both direct and indirect value of well-being improvement across a population whose medical costs are covered by an employer, insurer, and/or government entity...
2017: Health Services & Outcomes Research Methodology
Megan S Schuler, Wanghuan Chu, Donna Coffman
Propensity score methods (e.g., matching, weighting, subclassification) provide a statistical approach for balancing dissimilar exposure groups on baseline covariates. These methods were developed in the context of data with no hierarchical structure or clustering. Yet in many applications the data have a clustered structure that is of substantive importance, such as when individuals are nested within healthcare providers or within schools. Recent work has extended propensity score methods to a multilevel setting, primarily focusing on binary exposures...
December 2016: Health Services & Outcomes Research Methodology
Hong Zhao, Brian P Hobbs, Haijun Ma, Qi Jiang, Bradley P Carlin
Randomization eliminates selection bias, and attenuates imbalance among study arms with respect to prognostic factors, both known and unknown. Thus, information arising from randomized clinical trials (RCTs) is typically considered the gold standard for comparing therapeutic interventions in confirmatory studies. However, RCTs are limited in contexts wherein patients who are willing to accept a random treatment assignment represent only a subset of the patient population. By contrast, observational studies (OSs) often enroll patient cohorts that better reflect the broader patient population...
September 2016: Health Services & Outcomes Research Methodology
Erika L Moen, Andrea M Austin, Julie P Bynum, Jonathan S Skinner, A James O'Malley
The application of social network analysis to the organization of healthcare delivery is a relatively new area of research that may not be familiar to health services statisticians and other methodologists. We present a methodological introduction to social network analysis with a case study of physicians' adherence to clinical guidelines regarding use of implantable cardioverter defibrillators (ICDs) for the prevention of sudden cardiac death. We focus on two hospital referral regions (HRRs) in Indiana, Gary and South Bend, characterized by different rates of evidence-based ICD use (86% and 66%, respectively)...
September 2016: Health Services & Outcomes Research Methodology
Donald Hedeker, Robin J Mermelstein, Hakan Demirtas, Michael L Berbaum
In health studies, questionnaire items are often scored on an ordinal scale, for example on a Likert scale. For such questionnaires, item response theory (IRT) models provide a useful approach for obtaining summary scores for subjects ( i.e. , the model's random subject effect) and characteristics of the items ( e.g. , item difficulty and discrimination). In this article, we describe a model that allows the items to additionally exhibit different within-subject variance, and also includes a subject-level random effect to the within-subject variance specification...
September 2016: Health Services & Outcomes Research Methodology
Joseph Donohoe, Vincent Marshall, Xi Tan, Fabian T Camacho, Roger Anderson, Rajesh Balkrishnan
PURPOSE: This study evaluated spatial access to mammography centers in Appalachia using both traditional access measures and the two-step floating catchment area (2SFCA) method. METHODS: Ratios of county mammography centers to women age 45 and older, driving time to nearest mammography facility, and various 2SFCA approaches were compared throughout Pennsylvania, Ohio, Kentucky, and North Carolina. RESULTS: Closest travel time measures favored urban areas...
June 2016: Health Services & Outcomes Research Methodology
Nicholas C Henderson, Thomas A Louis, Chenguang Wang, Ravi Varadhan
Evaluation of heterogeneity of treatment effect (HTE) is an essential aspect of personalized medicine and patient-centered outcomes research. Our goal in this article is to promote the use of Bayesian methods for subgroup analysis and to lower the barriers to their implementation by describing the ways in which the companion software beanz can facilitate these types of analyses. To advance this goal, we describe several key Bayesian models for investigating HTE and outline the ways in which they are well-suited to address many of the commonly cited challenges in the study of HTE...
2016: Health Services & Outcomes Research Methodology
Demissie Alemayehu, Marc L Berger
The explosion of data sources, accompanied by the evolution of technology and analytical techniques, has created considerable challenges and opportunities for drug development and healthcare resource utilization. We present a systematic overview these phenomena, and suggest measures to be taken for effective integration of the new developments in the traditional medical research paradigm and health policy decision making. Special attention is paid to pertinent issues in emerging areas, including rare disease drug development, personalized medicine, Comparative Effectiveness Research, and privacy and confidentiality concerns...
2016: Health Services & Outcomes Research Methodology
Stephen O'Neill, Noémi Kreif, Richard Grieve, Matthew Sutton, Jasjeet S Sekhon
Difference-in-differences (DiD) estimators provide unbiased treatment effect estimates when, in the absence of treatment, the average outcomes for the treated and control groups would have followed parallel trends over time. This assumption is implausible in many settings. An alternative assumption is that the potential outcomes are independent of treatment status, conditional on past outcomes. This paper considers three methods that share this assumption: the synthetic control method, a lagged dependent variable (LDV) regression approach, and matching on past outcomes...
2016: Health Services & Outcomes Research Methodology
Peter Congdon
Analysis of healthy life expectancy is typically based on a binary distinction between health and ill-health. By contrast, this paper considers spatial modelling of disease free life expectancy taking account of the number of chronic conditions. Thus the analysis is based on population sub-groups with no disease, those with one disease only, and those with two or more diseases (multiple morbidity). Data on health status is accordingly modelled using a multinomial likelihood. The analysis uses data for 258 small areas in north London, and shows wide differences in the disease burden related to multiple morbidity...
2016: Health Services & Outcomes Research Methodology
Adam Steventon, Richard Grieve, Jasjeet S Sekhon
Various approaches have been used to select control groups in observational studies: (1) from within the intervention area; (2) from a convenience sample, or randomly chosen areas; (3) from areas matched on area-level characteristics; and (4) nationally. The consequences of the decision are rarely assessed but, as we show, it can have complex impacts on confounding at both the area and individual levels. We began by reanalyzing data collected for an evaluation of a rapid response service on rates of unplanned hospital admission...
2015: Health Services & Outcomes Research Methodology
Sonja Lumme, Reijo Sund, Alastair H Leyland, Ilmo Keskimäki
In this paper, we introduce several statistical methods to evaluate the uncertainty in the concentration index ( C ) for measuring socioeconomic equality in health and health care using aggregated total population register data. The C is a widely used index when measuring socioeconomic inequality, but previous studies have mainly focused on developing statistical inference for sampled data from population surveys. While data from large population-based or national registers provide complete coverage, registration comprises several sources of error...
2015: Health Services & Outcomes Research Methodology
Oksana Pugach, Donald Hedeker, Robin Mermelstein
A bivariate mixed-effects location-scale model is proposed for estimation of means, variances, and covariances of two continuous outcomes measured concurrently in time and repeatedly over subjects. Modeling the two outcomes jointly allows examination of BS and WS association between the outcomes and whether the associations are related to covariates. The variance-covariance matrices of the BS and WS effects are modeled in terms of covariates, explaining BS and WS heterogeneity. The proposed model relaxes assumptions on the homogeneity of the within-subject (WS) and between-subject (BS) variances...
December 2014: Health Services & Outcomes Research Methodology
Megan S Schuler, Jeannie-Marie S Leoutsakos, Elizabeth A Stuart
Confounding is widely recognized in settings where all variables are fully observed, yet recognition of and statistical methods to address confounding in the context of latent class regression are slowly emerging. In this study we focus on confounding when regressing a distal outcome on latent class; extending standard confounding methods is not straightforward when the treatment of interest is a latent variable. We describe a recent 1-step method, as well as two 3-step methods (modal and pseudoclass assignment) that incorporate propensity score weighting...
December 2014: Health Services & Outcomes Research Methodology
Elizabeth A Stuart, Haiden A Huskamp, Kenneth Duckworth, Jeffrey Simmons, Zirui Song, Michael Chernew, Colleen L Barry
Difference-in-difference (DD) methods are a common strategy for evaluating the effects of policies or programs that are instituted at a particular point in time, such as the implementation of a new law. The DD method compares changes over time in a group unaffected by the policy intervention to the changes over time in a group affected by the policy intervention, and attributes the "difference-in-differences" to the effect of the policy. DD methods provide unbiased effect estimates if the trend over time would have been the same between the intervention and comparison groups in the absence of the intervention...
December 1, 2014: Health Services & Outcomes Research Methodology
Robert D Gibbons, Marcelo Coca Perraillon, Jong Bae Kim
The need to harmonize different outcome metrics is a common problem in research synthesis and economic evaluation of health interventions and technology. The purpose of this paper is to describe the use of multidimensional item response theory (IRT) to equate different scales which purport to measure the same construct at the item level. We provide an overview of multidimensional item response theory in general and the bi-factor model which is particularly relevant for applications in this area. We show how both the underlying true scores of two or more scales that are intended to measure the same latent variable can be equated and how the item responses from one scale can be used to predict the item responses for a scale that was not administered but are necessary for the purpose of economic evaluations...
December 1, 2014: Health Services & Outcomes Research Methodology
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