Mark E. Glickman

Inadequate management of blood pressure in a hypertensive population.

BACKGROUND Many patients with hypertension have inadequate control of their blood pressure. Improving the treatment of hypertension requires an understanding of the ways in which physicians manage this condition and a means of assessing the efficacy of this care. METHODS We examined the care of 800 hypertensive men at five Department of Veterans Affairs sites in New England over a two-year period. Their mean (+/-SD) age was 65.5+/-9.1 years, and the average duration of hypertension was 12.6+/-5.3 years. We used recursive partitioning to assess the probability that antihypertensive therapy would be increased at a given clinic visit using several variables. We then used these predictions to define the intensity of treatment for each patient during the study period, and we examined the associations between the intensity of treatment and the degree of control of blood pressure. RESULTS Approximately 40 percent of the patients had a blood pressure of > or =160/90 mm Hg despite an average of more than six hypertension-related visits per year. Increases in therapy occurred during 6.7 percent of visits. Characteristics associated with an increase in antihypertensive therapy included increased levels of both systolic and diastolic blood pressure at that visit (but not previous visits), a previous change in therapy, the presence of coronary artery disease, and a scheduled visit. Patients who had more intensive therapy had significantly (P<0.01) better control of blood pressure. During the two-year period, systolic blood pressure declined by 6.3 mm Hg among patients with the most intensive treatment, but increased by 4.8 mm Hg among the patients with the least intensive treatment. CONCLUSIONS In a selected population of older men, blood pressure was poorly controlled in many. Those who received more intensive medical therapy had better control. Many physicians are not aggressive enough in their approach to hypertension.

Statistical methods for profiling providers of medical care : Issues and applications

Abstract Recent public debate on costs and effectiveness of health care in the United States has generated a growing emphasis on “profiling” of medical care providers. The process of profiling involves comparing resource use and quality of care among medical providers to a community or a normative standard. This is valuable for targeting quality improvement strategies. For example, hospital profiles may be used to determine whether institutions deviate in important ways in the process of care they deliver. In this article we propose a class of performance indices to profile providers. These indices are based on posterior tail probabilities of relevant model parameters that indicate the degree of poor performance by a provider. We apply our performance indices to profile hospitals on the basis of 30-day mortality rates for a cohort of elderly heart attack patients. The analysis used data from 96 acute care hospitals located in one state and accounted for patient and hospital characteristics using a hierarc...

False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies.

OBJECTIVES Procedures for controlling the false positive rate when performing many hypothesis tests are commonplace in health and medical studies. Such procedures, most notably the Bonferroni adjustment, suffer from the problem that error rate control cannot be localized to individual tests, and that these procedures do not distinguish between exploratory and/or data-driven testing vs. hypothesis-driven testing. Instead, procedures derived from limiting false discovery rates may be a more appealing method to control error rates in multiple tests. STUDY DESIGN AND SETTING Controlling the false positive rate can lead to philosophical inconsistencies that can negatively impact the practice of reporting statistically significant findings. We demonstrate that the false discovery rate approach can overcome these inconsistencies and illustrate its benefit through an application to two recent health studies. RESULTS The false discovery rate approach is more powerful than methods like the Bonferroni procedure that control false positive rates. Controlling the false discovery rate in a study that arguably consisted of scientifically driven hypotheses found nearly as many significant results as without any adjustment, whereas the Bonferroni procedure found no significant results. CONCLUSION Although still unfamiliar to many health researchers, the use of false discovery rate control in the context of multiple testing can provide a solid basis for drawing conclusions about statistical significance.

Parameter estimation in large dynamic paired comparison experiments

Summary. Paired comparison data in which the abilities or merits of the objects being compared may be changing over time can be modelled as a non-linear state space model. When the population of objects being compared is large, likelihood-based analyses can be too computationally cumbersome to carry out regularly. This presents a problem for rating populations of chess players and other large groups which often consist of tens of thousands of competitors. This problem is overcome through a computationally simple non-iterative algorithm for fitting a particular dynamic paired comparison model. The algorithm, which improves over the commonly used algorithm of Elo by incorporating the variability in parameter estimates, can be performed regularly even for large populations of competitors. The method is evaluated on simulated data and is applied to ranking the best chess players of all time, and to ranking the top current tennis-players.

Mental and Physical Health Status and Alcohol and Drug Use Following Return From Deployment to Iraq or Afghanistan

OBJECTIVES We examined (1) mental and physical health symptoms and functioning in US veterans within 1 year of returning from deployment, and (2) differences by gender, service component (Active, National Guard, other Reserve), service branch (Army, Navy, Air Force, Marines), and deployment operation (Operation Enduring Freedom/Operation Iraqi Freedom [OEF/OIF]). METHODS We surveyed a national sample of 596 OEF/OIF veterans, oversampling women to make up 50% of the total, and National Guard and Reserve components to each make up 25%. Weights were applied to account for stratification and nonresponse bias. RESULTS Mental health functioning was significantly worse compared with the general population; 13.9% screened positive for probable posttraumatic stress disorder, 39% for probable alcohol abuse, and 3% for probable drug abuse. Men reported more alcohol and drug use than did women, but there were no gender differences in posttraumatic stress disorder or other mental health domains. OIF veterans reported more depression or functioning problems and alcohol and drug use than did OEF veterans. Army and Marine veterans reported worse mental and physical health than did Air Force or Navy veterans. CONCLUSIONS Continuing identification of veterans at risk for mental health and substance use problems is important for evidence-based interventions intended to increase resilience and enhance treatment.

Multivariate Stochastic Volatility via Wishart Processes

Financial models for asset and derivatives pricing, risk management, portfolio optimization, and asset allocation rely on volatility forecasts. Time-varying volatility models, such as generalized autoregressive conditional heteroscedasticity and stochastic volatility (SVOL), have been successful in improving forecasts over constant volatility models. We develop a new multivariate SVOL framework for modeling financial data that assumes covariance matrices stochastically varying through a Wishart process. In our formulation, scalar variances naturally extend to covariance matrices rather than to vectors of variances as in traditional SVOL models. Model fitting is performed using Markov chain Monte Carlo simulation from the posterior distribution. Because of the models complexity, an efficiently designed Gibbs sampler is described that produces inferences with a manageable amount of computation. Our approach is illustrated on a multivariate time series of monthly industry portfolio returns. A test of the economic value of our model found that minimum-variance portfolios based on our SVOL covariance forecasts outperformed out-of-sample portfolios based on alternative covariance models, such as dynamic conditional correlations and factor-based covariances.

A State-Space Model for National Football League Scores

Abstract This article develops a predictive model for National Football League (NFL) game scores using data from the period 1988–1993. The parameters of primary interest—measures of team strength—are expected to vary over time. Our model accounts for this source of variability by modeling football outcomes using a state-space model that assumes team strength parameters follow a first-order autoregressive process. Two sources of variation in team strengths are addressed in our model; week-to-week changes in team strength due to injuries and other random factors, and season-to-season changes resulting from changes in personnel and other longer-term factors. Our model also incorporates a home-field advantage while allowing for the possibility that the magnitude of the advantage may vary across teams. The aim of the analysis is to obtain plausible inferences concerning team strengths and other model parameters, and to predict future game outcomes. Iterative simulation is used to obtain samples from the joint ...

Suicide-related behaviors in older patients with new anti-epileptic drug use: data from the VA hospital system

BackgroundThe U.S. Food and Drug Administration (FDA) recently linked antiepileptic drug (AED) exposure to suicide-related behaviors based on meta-analysis of randomized clinical trials. We examined the relationship between suicide-related behaviors and different AEDs in older veterans receiving new AED monotherapy from the Veterans Health Administration (VA), controlling for potential confounders.MethodsVA and Medicare databases were used to identify veterans 66 years and older, who received a) care from the VA between 1999 and 2004, and b) an incident AED (monotherapy) prescription. Previously validated ICD-9-CM codes were used to identify suicidal ideation or behavior (suicide-related behaviors cases), epilepsy, and other conditions previously associated with suicide-related behaviors. Each case was matched to controls based on prior history of suicide-related behaviors, year of AED prescription, and epilepsy status.ResultsThe strongest predictor of suicide-related behaviors (N = 64; Controls N = 768) based on conditional logistic regression analysis was affective disorder (depression, anxiety, or post-traumatic stress disorder (PTSD); Odds Ratio 4.42, 95% CI 2.30 to 8.49) diagnosed before AED treatment. Increased suicide-related behaviors were not associated with individual AEDs, including the most commonly prescribed AED in the US - phenytoin.ConclusionOur extensive diagnostic and treatment data demonstrated that the strongest predictor of suicide-related behaviors for older patients newly treated with AED monotherapy was a previous diagnosis of affective disorder. Additional, research using a larger sample is needed to clearly determine the risk of suicide-related behaviors among less commonly used AEDs.

Dynamic paired comparison models with stochastic variances

In paired comparison experiments, the worth or merit of a unit is measured through comparisons against other units. When paired comparison outcomes are collected over time and the merits of the units may be changing, it is often convenient to assume the data follow a non-linear state-space model. Typical paired comparison state-space models that assume a fixed (unknown) autoregressive variance do not account for the possibility of sudden changes in the merits. This is a particular concern, for example, in modeling cognitive ability in human development; cognitive ability not only changes over time, but also can change abruptly. We explore a particular extension of conventional state-space models for paired comparison data that allows the state variance to vary stochastically. Models of this type have recently been developed and applied to modeling financial data, but can be seen to have applicability in modeling paired comparison data. A filtering algorithm is also derived that can be used in place of likelihood-based computations when the number of objects being compared is large. Applications to National Football League game outcomes and chess game outcomes are presented.

Factor Multivariate Stochastic Volatility via Wishart Processes

This paper proposes a high dimensional factor multivariate stochastic volatility (MSV) model in which factor covariance matrices are driven by Wishart random processes. The framework allows for unrestricted specification of intertemporal sensitivities, which can capture the persistence in volatilities, kurtosis in returns, and correlation breakdowns and contagion effects in volatilities. The factor structure allows addressing high dimensional setups used in portfolio analysis and risk management, as well as modeling conditional means and conditional variances within the model framework. Owing to the complexity of the model, we perform inference using Markov chain Monte Carlo simulation from the posterior distribution. A simulation study is carried out to demonstrate the efficiency of the estimation algorithm. We illustrate our model on a data set that includes 88 individual equity returns and the two Fama–French size and value factors. With this application, we demonstrate the ability of the model to address high dimensional applications suitable for asset allocation, risk management, and asset pricing.