45 results match your criteria Health Services and Outcomes Research Methodology [Journal]

  • Page 1 of 1

Implications of family risk pooling for individual health insurance markets.

Health Serv Outcomes Res Methodol 2017 Dec 26;17(3-4):219-236. Epub 2017 May 26.

Department of Health Care Policy, Harvard Medical School.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-017-0170-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5796434PMC
December 2017
4 Reads

On the Use of Summary Comorbidity Measures for Prognosis and Survival Treatment Effect Estimation.

Health Serv Outcomes Res Methodol 2017 Dec 21;17(3-4):237-255. Epub 2017 Jun 21.

Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Temple University Health System, Philadelphia, Pennsylvania, 19111, U.S.A., Telephone: (215) 214-3917.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-017-0171-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5697800PMC
December 2017
3 Reads

Optimizing Variance-Bias Trade-off in the TWANG Package for Estimation of Propensity Scores.

Health Serv Outcomes Res Methodol 2017 Dec 26;17(3-4):175-197. Epub 2016 Dec 26.

RAND Corporation, 1200 South Hayes Street Arlington, Virginia 22202 USA.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0168-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5667923PMC
December 2017
5 Reads

Accounting for misclassification in electronic health records-derived exposures using generalized linear finite mixture models.

Health Serv Outcomes Res Methodol 2017 Jun 3;17(2):101-112. Epub 2016 Jun 3.

Department of Biostatistics & Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania (Rebecca A. Hubbard); Group Health Research Institute, Seattle, Washington (Eric Johnson, Jessica Chubak, Karen J. Wernli, Aruna Kamineni); Department of Epidemiology, University of Washington, Seattle, Washington (Jessica Chubak); RAND Corporation, Santa Monica, California (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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0149-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608281PMC
June 2017
12 Reads

Combining Non-randomized and Randomized Data in Clinical Trials Using Commensurate Priors.

Health Serv Outcomes Res Methodol 2016 Sep 6;16(3):154-171. Epub 2016 Aug 6.

Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN 55455 USA.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0155-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5404417PMC
September 2016
11 Reads

The well-being valuation model: a method for monetizing the nonmarket good of individual well-being.

Health Serv Outcomes Res Methodol 2017 25;17(1):84-100. Epub 2016 Oct 25.

Center for Health Research, Healthways, Inc., 701 Cool Springs Boulevard, Franklin, TN 37067 USA.

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 ( = 50,647) and regional employer ( = 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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0161-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5306175PMC
October 2016
4 Reads

Propensity score weighting for a continuous exposure with multilevel data.

Health Serv Outcomes Res Methodol 2016 Dec 25;16(4):271-292. Epub 2016 Aug 25.

Department of Epidemiology and Biostatistics, Temple University, Philadelphia, PA 19122.

Propensity score methods (e.g., matching, weighting, subclassification) provide a statistical approach for balancing dissimilar exposure groups on baseline covariates. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0157-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5157938PMC
December 2016
4 Reads

Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research.

Health Serv Outcomes Res Methodol 2016 20;16(4):213-233. Epub 2016 Sep 20.

Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA.

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 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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0159-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5097788PMC
September 2016
14 Reads

An analysis of patient-sharing physician networks and implantable cardioverter defibrillator therapy.

Health Serv Outcomes Res Methodol 2016 Sep 27;16(3):132-153. Epub 2016 Jun 27.

The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, One Medical Center Dr., Lebanon, NH 03756; The Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, One Medical Center Dr., Lebanon, NH 03756.

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). Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0152-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5010235PMC
September 2016
6 Reads

Big Data: transforming drug development and health policy decision making.

Health Serv Outcomes Res Methodol 2016;16:92-102. Epub 2016 Mar 5.

Pfizer Inc., 235 East 42nd Street, New York, NY 10017 USA.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0144-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987387PMC
March 2016
5 Reads

A Mixed-effects Location-Scale Model for Ordinal Questionnaire Data.

Health Serv Outcomes Res Methodol 2016 Sep 11;16(3):117-131. Epub 2016 Apr 11.

University of Illinois at Chicago.

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 (, the model's random subject effect) and characteristics of the items (, 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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0145-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996469PMC
September 2016
2 Reads

Evaluating and Comparing Methods for Measuring Spatial Access to Mammography Centers in Appalachia (Re-Revised).

Health Serv Outcomes Res Methodol 2016 Jun 12;16(1):22-40. Epub 2016 Jan 12.

Department of Public Health Sciences and Emily Couric Cancer Center, School of Medicine, University of Virginia, Hospital West, Jefferson Park Avenue, Charlottesville, VA 22901-0793.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0143-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945133PMC
June 2016
8 Reads

Estimating causal effects: considering three alternatives to difference-in-differences estimation.

Health Serv Outcomes Res Methodol 2016;16:1-21. Epub 2016 May 7.

Department of Political Science and Department of Statistics, University of California at Berkeley, Berkeley, CA USA.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-016-0146-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4869762PMC
May 2016
11 Reads

Area variations in multiple morbidity using a life table methodology.

Authors:
Peter Congdon

Health Serv Outcomes Res Methodol 2016;16:58-74. Epub 2016 Jan 8.

School of Geography and Life Sciences Institute, Queen Mary University of London, London, UK.

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). Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-015-0142-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4867778PMC
January 2016
3 Reads

A comparison of alternative strategies for choosing control populations in observational studies.

Health Serv Outcomes Res Methodol 2015;15(3-4):157-181. Epub 2015 Jan 30.

Travers Department of Political Science and Department of Statistics, UC Berkeley, 210 Barrows Hall #1950, Berkeley, CA 94720-1950 USA.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-014-0135-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4565881PMC
January 2015
3 Reads

A Monte Carlo method to estimate the confidence intervals for the concentration index using aggregated population register data.

Health Serv Outcomes Res Methodol 2015;15(2):82-98. Epub 2015 Feb 18.

The Social and Health Systems Research Unit, The Department of Health and Social Care Systems, The National Institute for Health and Welfare (THL), P.O. Box 30, 00271 Helsinki, Finland ; School of Health Sciences, University of Tampere, Tampere, 33014 Finland.

In this paper, we introduce several statistical methods to evaluate the uncertainty in the concentration index () for measuring socioeconomic equality in health and health care using aggregated total population register data. The 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. Read More

View Article

Download full-text PDF

Source
http://link.springer.com/10.1007/s10742-015-0137-1
Publisher Site
http://dx.doi.org/10.1007/s10742-015-0137-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4426159PMC
February 2015
6 Reads

A Bivariate Mixed-Effects Location-Scale Model with application to Ecological Momentary Assessment (EMA) data.

Health Serv Outcomes Res Methodol 2014 Dec;14(4):194-212

Institute for Health Research and Policy, University of Illinois at Chicago ; Department of Psychology, University of Illinois at Chicago.

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. Read More

View Article

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4273572PMC
December 2014
9 Reads

ADDRESSING CONFOUNDING WHEN ESTIMATING THE EFFECTS OF LATENT CLASSES ON A DISTAL OUTCOME.

Health Serv Outcomes Res Methodol 2014 Dec;14(4):232-254

Department of Mental Health, Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-014-0122-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269287PMC
December 2014
3 Reads

Using propensity scores in difference-in-differences models to estimate the effects of a policy change.

Health Serv Outcomes Res Methodol 2014 Dec;14(4):166-182

Johns Hopkins Bloomberg School of Public Health.

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. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-014-0123-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267761PMC
December 2014
10 Reads

Using instrumental variables to estimate a Cox's proportional hazards regression subject to additive confounding.

Health Serv Outcomes Res Methodol 2014 Jun;14(1-2):54-68

Geisel School of Medicine at Dartmouth, Hanover, NH, USA.

The estimation of treatment effects is one of the primary goals of statistics in medicine. Estimation based on observational studies is subject to confounding. Statistical methods for controlling bias due to confounding include regression adjustment, propensity scores and inverse probability weighted estimators. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-014-0117-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4261749PMC
June 2014
18 Reads

Item Response Theory Approaches to Harmonization and Research Synthesis.

Health Serv Outcomes Res Methodol 2014 Dec;14(4):213-231

University of Chicago.

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. Read More

View Article

Download full-text PDF

Source
http://link.springer.com/content/pdf/10.1007/s10742-014-0125
Web Search
http://link.springer.com/10.1007/s10742-014-0125-x
Publisher Site
http://dx.doi.org/10.1007/s10742-014-0125-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4244312PMC
December 2014
8 Reads

State Investments in Psychiatric Innovation: Investigating Unmeasured State Factors.

Health Serv Outcomes Res Methodol 2014 Jun;14(1-2):34-53

Department of Health Policy and Management, UNC Gillings School of Global Public Health ; Center for Health Services Research in Primary Care, Department of Veterans Affairs.

We apply three separate panel data estimation methods to examine the diffusion of technologies at the state-level. These methods include the Hausman-Taylor random effects model, the fixed effects vector decomposition (FEVD), and generalized estimating equations (GEE). We discuss the assumptions required of each and assess the stability of our policy results across the three models for a longitudinal study of the diffusion of newer psychotropic technologies. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-014-0116-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226516PMC
June 2014
3 Reads

Assessing the accuracy of profiling methods for identifying top providers: performance of mental health care providers.

Health Serv Outcomes Res Methodol 2013 Mar 18;13(1):1-17. Epub 2012 Sep 18.

Department of Biostatistics, University of Washington, Seattle, WA, USA.

Provider profiling as a means to describe and compare the performance of health care professionals has gained momentum in the past decade. As a key component of pay-for-performance programs profiling has been increasingly used to identify top-performing providers. However, rigorous examination of the performance of statistical methods for profiling when used to classify top-performing providers is lacking. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-012-0099-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3616677PMC
March 2013
10 Reads
3 Citations

Evaluating long-term effects of a psychiatric treatment using instrumental variable and matching approaches.

Authors:
Bo Lu Sue Marcus

Health Serv Outcomes Res Methodol 2012 Dec 5;12(4):288-301. Epub 2012 Oct 5.

Evaluating treatment effects in non-randomized studies is challenging due to the potential unmeasured confounding and complex form of observed confounding. Propensity score based approaches, such as matching or weighting, are commonly used to handle observed confounding variables. The instrumental variable (IV) method is known to guard against unmeasured confounding if a good instrument can be identified. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-012-0101-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3587666PMC
December 2012
9 Reads

Joint modeling of multiple longitudinal cost outcomes using multivariate generalized linear mixed models.

Health Serv Outcomes Res Methodol 2013 Mar;13(1):39-57

Division of Biostatistics and Epidemiology, Medical University of South Carolina, Cannon Place Suite 303, Charleston, SC 29425, USA, Center for Disease Prevention and Health Interventions for Diverse Population, Ralph H. Johnson Veterans Affairs Medical Center, 109 Bee St, Research Service, Charleston, SC 29401-5799, USA, Center for Health Disparities Research, Medical University of South Carolina, 135 Rutledge Ave. Room 280H, Charleston, SC 29425-0593, USA.

The common approach to modeling healthcare cost data is to use aggregated total cost from multiple categories or sources (e.g. inpatient, outpatient, prescriptions, etc. Read More

View Article

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6290916PMC
March 2013
1 Read

Applying standardized drug terminologies to observational healthcare databases: a case study on opioid exposure.

Health Serv Outcomes Res Methodol 2013 Mar 27;13(1):58-67. Epub 2012 Oct 27.

Janssen Pharmaceutical Research & Development, L.L.C. 920 Route 202, Raritan, NJ 08869 USA.

Observational healthcare databases represent a valuable resource for health economics, outcomes research, quality of care, drug safety, epidemiology and comparative effectiveness research. The methods used to identify a population for study in an observational healthcare database with the desired drug exposures of interest are complex and not consistent nor apparent in the published literature. Our research evaluates three drug classification systems and their impact on prevalence in the analysis of observational healthcare databases using opioids as a case in point. Read More

View Article

Download full-text PDF

Source
http://link.springer.com/10.1007/s10742-012-0102-1
Publisher Site
http://dx.doi.org/10.1007/s10742-012-0102-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3566397PMC
March 2013
5 Reads

Instrumental variable specifications and assumptions for longitudinal analysis of mental health cost offsets.

Authors:
A James O'Malley

Health Serv Outcomes Res Methodol 2012 Dec 25;12(4):254-272. Epub 2012 Sep 25.

Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115-5899 USA.

Instrumental variables (IVs) enable causal estimates in observational studies to be obtained in the presence of unmeasured confounders. In practice, a diverse range of models and IV specifications can be brought to bear on a problem, particularly with longitudinal data where treatment effects can be estimated for various functions of current and past treatment. However, in practice the empirical consequences of different assumptions are seldom examined, despite the fact that IV analyses make strong assumptions that cannot be conclusively tested by the data. Read More

View Article

Download full-text PDF

Source
http://link.springer.com/10.1007/s10742-012-0097-7
Publisher Site
http://dx.doi.org/10.1007/s10742-012-0097-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3515775PMC
December 2012
4 Reads

Near/far matching: a study design approach to instrumental variables.

Health Serv Outcomes Res Methodol 2012 Dec 9;12(4):237-253. Epub 2012 Jun 9.

Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA. Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA. Department of Veterans Affairs' Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA, USA.

Classic instrumental variable techniques involve the use of structural equation modeling or other forms of parameterized modeling. In this paper we use a nonparametric, matching-based instrumental variable methodology that is based on a study design approach. Similar to propensity score matching, though unlike classic instrumental variable approaches, near/far matching is capable of estimating causal effects when the outcome is not continuous. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-012-0091-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4831129PMC
December 2012
4 Reads

Bias and variance trade-offs when combining propensity score weighting and regression: with an application to HIV status and homeless men.

Health Serv Outcomes Res Methodol 2012 Jun;12(2-3):104-118

RAND, 1776 Main Street, Santa Monica, CA 90407, USA,

The quality of propensity scores is traditionally measured by assessing how well they make the distributions of covariates in the treatment and control groups match, which we refer to as "good balance". Good balance guarantees less biased estimates of the treatment effect. However, the cost of achieving good balance is that the variance of the estimates increases due to a reduction in effective sample size, either through the introduction of propensity score weights or dropping cases when propensity score matching. Read More

View Article

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433039PMC
June 2012
11 Reads

Assessing the Sensitivity of Treatment Effect Estimates to Differential Follow-Up Rates: Implications for Translational Research.

Health Serv Outcomes Res Methodol 2012 Jun;12(2-3):84-103

RAND Corporation, 1200 South Hayes Street, Arlington, VA 22202, x 5188.

We develop a new tool for assessing the sensitivity of findings on treatment effectiveness to differential follow-up rates in the two treatment conditions being compared. The method censors the group with the higher response rate to create a synthetic respondent group that is then compared with the observed cases in the other condition to estimate a treatment effect. Censoring is done under various assumptions about the strength of the relationship between follow-up and outcomes to determine how informative differential dropout can alter inferences relative to estimates from models that assume the data are missing at random. Read More

View Article

Download full-text PDF

Source
http://link.springer.com/10.1007/s10742-012-0089-7
Publisher Site
http://dx.doi.org/10.1007/s10742-012-0089-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433078PMC
June 2012
5 Reads

Degrees of health disparities: Health status disparities between young adults with high school diplomas, sub-baccalaureate degrees, and baccalaureate degrees.

Authors:
J Rosenbaum

Health Serv Outcomes Res Methodol 2012 Jun;12(2-3):156-168

Maryland Population Research Center, 0124N Cole Student Activities Building, University of Maryland, College Park, Maryland, 20742, Tel: 301-405-6403, ,

Community colleges have increased post-secondary educational access for disadvantaged youth, but it is unknown how community college degrees fit into the educational gradient of health status disparities. Using data from high school graduates in the National Longitudinal Study of Adolescent Health, we compared young adults ages 26-31 whose highest degrees were high school diplomas (n=5584), sub-baccalaureate credentials (sub-BAs include community college certificates and associate's degrees) (n=2415), and baccalaureate degrees (BAs) (n=3303) on measures of hypertension, obesity, smoking, sleep problems, dyslipidemia, and depression. Comparisons used multivariate Poisson regression with robust standard errors after exact and nearest-neighbor Mahalanobis matching within propensity score calipers on 23 baseline factors measured in 1995. Read More

View Article

Download full-text PDF

Source
http://link.springer.com/10.1007/s10742-012-0094-x
Publisher Site
http://dx.doi.org/10.1007/s10742-012-0094-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3417309PMC
June 2012
6 Reads

Using AIC in Multiple Linear Regression framework with Multiply Imputed Data.

Health Serv Outcomes Res Methodol 2012 Jun;12(2-3):219-233

Department of Statistics, University of Connecticut, Storrs, CT, USA.

Many model selection criteria proposed over the years have become common procedures in applied research. However, these procedures were designed for complete data. Complete data is rare in applied statistics, in particular in medical, public health and health policy settings. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-012-0088-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3412358PMC
June 2012
17 Reads

Joint modeling of longitudinal outcomes and survival using latent growth modeling approach in a mesothelioma trial.

Health Serv Outcomes Res Methodol 2012 Jun 5;12(2-3):182-199. Epub 2012 Jun 5.

Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN 46285 USA.

Joint modeling of longitudinal and survival data can provide more efficient and less biased estimates of treatment effects through accounting for the associations between these two data types. Sponsors of oncology clinical trials routinely and increasingly include patient-reported outcome (PRO) instruments to evaluate the effect of treatment on symptoms, functioning, and quality of life. Known publications of these trials typically do not include jointly modeled analyses and results. Read More

View Article

Download full-text PDF

Source
http://link.springer.com/10.1007/s10742-012-0092-z
Publisher Site
http://dx.doi.org/10.1007/s10742-012-0092-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384782PMC
June 2012
6 Reads

A graphical method for assessing risk factor threshold values using the generalized additive model: the multi-ethnic study of atherosclerosis.

Health Serv Outcomes Res Methodol 2012 Mar;12(1):62-79

RAND, Pittsburgh, PA 15213, USA

Continuous variable dichotomization is a popular technique used in the estimation of the effect of risk factors on health outcomes in multivariate regression settings. Researchers follow this practice in order to simplify data analysis, which it unquestionably does. However thresholds used to dichotomize those variables are usually ad-hoc, based on expert opinions, or mean, median or quantile splits and can add bias to the effect of the risk factors on specific outcomes and underestimate such effect. Read More

View Article

Download full-text PDF

Source
http://link.springer.com/10.1007/s10742-012-0082-1
Publisher Site
http://dx.doi.org/10.1007/s10742-012-0082-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3351005PMC
March 2012
8 Reads

Comparing treatments via the propensity score: stratification or modeling?

Health Serv Outcomes Res Methodol 2012 Mar;12(1):29-43

Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.

In observational studies of treatments or interventions, propensity score (PS) adjustment is often useful for controlling bias in estimation of treatment effects. Regression on PS is used most often and can be highly efficient, but it can lead to biased results when model assumptions are violated. The validity of stratification on PS depends on fewer model assumptions, but this approach is less efficient than regression adjustment when the regression assumptions hold. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-012-0080-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4238307PMC
March 2012
4 Reads

ESTIMATING TREATMENT EFFECTS ON HEALTHCARE COSTS UNDER EXOGENEITY: IS THERE A 'MAGIC BULLET'?

Health Serv Outcomes Res Methodol 2011 Jul;11(1-2):1-26

Department of Health Services and PORPP, University of Washington, 1959 NE Pacific St, Box 357660, Seattle WA 98195-7600, and the NBER, Massachusetts, , Tel: 206 616 2986.

Methods for estimating average treatment effects, under the assumption of no unmeasured confounders, include regression models; propensity score adjustments using stratification, weighting, or matching; and doubly robust estimators (a combination of both). Researchers continue to debate about the best estimator for outcomes such as health care cost data, as they are usually characterized by an asymmetric distribution and heterogeneous treatment effects,. Challenges in finding the right specifications for regression models are well documented in the literature. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-011-0072-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3244728PMC
July 2011
4 Reads

GENES AS INSTRUMENTS FOR STUDYING RISK BEHAVIOR EFFECTS: AN APPLICATION TO MATERNAL SMOKING AND OROFACIAL CLEFTS.

Health Serv Outcomes Res Methodol 2011 Jul;11(1-2):54-78

Assistant Professor, Dept. of Health Management and Policy, College of Public Health, University of Iowa, 200 Hawkins Drive, E205 GH, Iowa City, IA 52242 USA, ;

This study uses instrumental variable (IV) models with genetic instruments to assess the effects of maternal smoking on the child's risk of orofacial clefts (OFC), a common birth defect. The study uses genotypic variants in neurotransmitter and detoxification genes relateded to smoking as instruments for cigarette smoking before and during pregnancy. Conditional maximum likelihood and two-stage IV probit models are used to estimate the IV model. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-011-0071-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3216039PMC
July 2011
7 Reads

Creating a parsimonious typology of physician financial incentives.

Health Serv Outcomes Res Methodol 2009 Dec;9(4):213-233

Department Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02115, USA.

In order to create an empirically derived parsimonious typology of physician financial incentives that will be useful for future research, we used data from the nationally representative 2004-2005 Community Tracking Study Physician Survey (N = 6,628). Linear regression analyses informed by economic theory were used to identify the combinations of incentives associated with an overall financial incentive to expand services to individual patients. The approach was validated using two nonparametric methods (CART analysis and data mining techniques) and by examining the relationship between the resulting typology and other measures of physician behavior including hours worked, visit volume, and specialty-adjusted income. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-010-0057-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2956986PMC
December 2009
6 Reads

Adjusting for Health Status in Non-Linear Models of Health Care Disparities.

Health Serv Outcomes Res Methodol 2009 Mar;9(1):1-21

Center for Multicultural Mental Health Research, Cambridge Health Alliance - Harvard Medical School, 120 Beacon Street, 4 floor, Somerville, MA 02143, 617-503-8449.

This article compared conceptual and empirical strengths of alternative methods for estimating racial disparities using non-linear models of health care access. Three methods were presented (propensity score, rank and replace, and a combined method) that adjust for health status while allowing SES variables to mediate the relationship between race and access to care. Applying these methods to a nationally representative sample of blacks and non-Hispanic whites surveyed in the 2003 and 2004 Medical Expenditure Panel Surveys (MEPS), we assessed the concordance of each of these methods with the Institute of Medicine (IOM) definition of racial disparities, and empirically compared the methods' predicted disparity estimates, the variance of the estimates, and the sensitivity of the estimates to limitations of available data. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-008-0039-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2845167PMC
March 2009
6 Reads

The Analysis of Social Networks.

Health Serv Outcomes Res Methodol 2008 Dec;8(4):222-269

Associate Professor of Statistics, Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115-5899.

Many questions about the social organization of medicine and health services involve interdependencies among social actors that may be depicted by networks of relationships. Social network studies have been pursued for some time in social science disciplines, where numerous descriptive methods for analyzing them have been proposed. More recently, interest in the analysis of social network data has grown among statisticians, who have developed more elaborate models and methods for fitting them to network data. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-008-0041-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2799303PMC
December 2008
2 Reads

Ranking USRDS provider specific SMRs from 1998-2001.

Health Serv Outcomes Res Methodol 2009 Mar;9(1):22-38

Department of Public Health, University of Massachusetts Amherst, Rm 411 Arnold House, 715 N. Pleasant Rd., Amherst, MA 01003, USA.

Provider profiling (ranking/percentiling) is prevalent in health services research. Bayesian models coupled with optimizing a loss function provide an effective framework for computing non-standard inferences such as ranks. Inferences depend on the posterior distribution and should be guided by inferential goals. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-008-0040-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2664159PMC
March 2009
6 Reads

Causal Mediation Analyses for Randomized Trials.

Health Serv Outcomes Res Methodol 2008 ;8(2):57-76

Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, January 22, 2008.

In the context of randomized intervention trials, we describe causal methods for analyzing how post-randomization factors constitute the process through which randomized baseline interventions act on outcomes. Traditionally, such mediation analyses have been undertaken with great caution, because they assume that the mediating factor is also randomly assigned to individuals in addition to the randomized baseline intervention (i.e. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-008-0028-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2688317PMC
January 2008
6 Reads

Predicting the Length of Stay of Patients Admitted for Intensive Care Using a First Step Analysis.

Health Serv Outcomes Res Methodol 2006 Dec;6(3-4):127-138

The University of Texas Health Science Center at Houston, School of Public Health, Division of Biostatistics, 80 Fort Brown, SPH Rm N.200, Brownsville, TX 78520, USA.

For patients admitted to intensive care units (ICU), the length of stay in different destinations after the first day of ICU admission, has not been systematically studied. We aimed to estimate the average length of stay (LOS) of such patients in Colombia, using a discrete time Markov process. We used the maximum likelihood method and Markov chain modeling to estimate the average LOS in the ICU and at each destination after discharge from intensive care. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10742-006-0009-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1828134PMC
December 2006
8 Reads

Methods for Estimating and Interpreting Provider-Specific Standardized Mortality Ratios.

Health Serv Outcomes Res Methodol 2003 ;4(3):135-149

United States Renal Data System, Minneapolis Medical Research Foundation, Minneapolis, MN, USA

Standardized Mortality Ratios (SMRs) are widely used as a measurement of quality of care for profiling and otherwise comparing medical care providers. Invalid estimation or inappropriate interpretation may have serious local and national consequences. Estimating an SMR entails producing provider-specific expected deaths via a statistical model and then computing the "observed/expected" ratio. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1023/B:HSOR.0000031400.77979.b6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2709867PMC
January 2003
5 Reads
  • Page 1 of 1