Lower socioeconomic status may help explain racial disparities in asthma and atopic dermatitis prevalence: A mediation analysis.

BACKGROUND: Racial disparities in atopic disease (atopic dermatitis [AD], asthma, and allergies) prevalence are well documented. Despite strong associations between race and socioeconomic deprivation in the United States, and socioeconomic status (SES) and atopic diseases, the extent to which SES explains these disparities is not fully understood. OBJECTIVE: We sought to identify racial disparities in childhood atopic disease prevalence and determine what proportion of those disparities is mediated by SES. METHODS: This study used the National Health Interview Survey (2011-2018) to investigate AD, asthma, and respiratory allergy prevalence in Black and White children and the extent to which measures of SES explain any identified disparities. RESULTS: By race, prevalences were as follows: AD, White 11.8% (95% CI: 11.4%, 12.2%) and Black 17.4% (95% CI: 16.6%, 18.3%); asthma prevalence, White 7.4% (95% CI: 7.0%, 7.7%) and Black 14.3% (95% CI: 13.5%, 15.0%); respiratory allergy, White 11.4% (95% CI: 11.0%, 11.9%) and Black 10.9% (95% CI: 10.3%, 11.6%). The percentage of the disparity between racial groups and disease prevalence explained by a multivariable measure of SES was 25% (95% CI: 15%, 36%) for Black versus White children with AD and 47% (95% CI: 40%, 54%) for Black versus White children with asthma. CONCLUSIONS: In a nationally representative US population, Black children had higher prevalence of AD and asthma than White children did and similar prevalence of respiratory allergy; a multivariable SES measure explained a proportion of the association between Black versus White race and AD and a much larger proportion for asthma.

Doubly robust evaluation of high-dimensional surrogate markers.

When evaluating the effectiveness of a treatment, policy, or intervention, the desired measure of efficacy may be expensive to collect, not routinely available, or may take a long time to occur. In these cases, it is sometimes possible to identify a surrogate outcome that can more easily, quickly, or cheaply capture the effect of interest. Theory and methods for evaluating the strength of surrogate markers have been well studied in the context of a single surrogate marker measured in the course of a randomized clinical study. However, methods are lacking for quantifying the utility of surrogate markers when the dimension of the surrogate grows. We propose a robust and efficient method for evaluating a set of surrogate markers that may be high-dimensional. Our method does not require treatment to be randomized and may be used in observational studies. Our approach draws on a connection between quantifying the utility of a surrogate marker and the most fundamental tools of causal inference-namely, methods for robust estimation of the average treatment effect. This connection facilitates the use of modern methods for estimating treatment effects, using machine learning to estimate nuisance functions and relaxing the dependence on model specification. We demonstrate that our proposed approach performs well, demonstrate connections between our approach and certain mediation effects, and illustrate it by evaluating whether gene expression can be used as a surrogate for immune activation in an Ebola study.

Statistical Methods to Evaluate Surrogate Markers.

BACKGROUND: There is tremendous interest in evaluating surrogate markers given their potential to decrease study time, costs, and patient burden. OBJECTIVES: The purpose of this statistical workshop article is to describe and illustrate how to evaluate a surrogate marker of interest using the proportion of treatment effect (PTE) explained as a measure of the quality of the surrogate marker for: (1) a setting with a general fully observed primary outcome (eg, biopsy score); and (2) a setting with a time-to-event primary outcome which may be censored due to study termination or early drop out (eg, time to diabetes). METHODS: The methods are motivated by 2 randomized trials, one among children with nonalcoholic fatty liver disease where the primary outcome was a change in biopsy score (general outcome) and another study among adults at high risk for Type 2 diabetes where the primary outcome was time to diabetes (time-to-event outcome). The methods are illustrated using the Rsurrogate package with a detailed R code provided. RESULTS: In the biopsy score outcome setting, the estimated PTE of the examined surrogate marker was 0.182 (95% confidence interval [CI]: 0.121, 0.240), that is, the surrogate explained only 18.2% of the treatment effect on the biopsy score. In the diabetes setting, the estimated PTE of the surrogate marker was 0.596 (95% CI: 0.404, 0.760), that is, the surrogate explained 59.6% of the treatment effect on diabetes incidence. CONCLUSIONS: This statistical workshop provides tools that will support future researchers in the evaluation of surrogate markers.

A rank-based approach to evaluate a surrogate marker in a small sample setting.

In clinical studies of chronic diseases, the effectiveness of an intervention is often assessed using "high cost" outcomes that require long-term patient follow-up and/or are invasive to obtain. While much progress has been made in the development of statistical methods to identify surrogate markers, that is, measurements that could replace such costly outcomes, they are generally not applicable to studies with a small sample size. These methods either rely on nonparametric smoothing which requires a relatively large sample size or rely on strict model assumptions that are unlikely to hold in practice and empirically difficult to verify with a small sample size. In this paper, we develop a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. The method developed in this paper is motivated by a small study of children with nonalcoholic fatty liver disease (NAFLD), a diagnosis for a range of liver conditions in individuals without significant history of alcohol intake. Specifically, we examine whether change in alanine aminotransferase (ALT; measured in blood) is a surrogate marker for change in NAFLD activity score (obtained by biopsy) in a trial, which compared Vitamin E ($n=50$) versus placebo ($n=46$) among children with NAFLD.

Flexible evaluation of surrogate markers with Bayesian model averaging.

When long-term follow up is required for a primary endpoint in a randomized clinical trial, a valid surrogate marker can help to estimate the treatment effect and accelerate the decision process. Several model-based methods have been developed to evaluate the proportion of the treatment effect that is explained by the treatment effect on the surrogate marker. More recently, a nonparametric approach has been proposed allowing for more flexibility by avoiding the restrictive parametric model assumptions required in the model-based methods. While the model-based approaches suffer from potential mis-specification of the models, the nonparametric method fails to give desirable estimates when the sample size is small, or when the range of the data does not follow certain conditions. In this paper, we propose a Bayesian model averaging approach to estimate the proportion of treatment effect explained by the surrogate marker. Our procedure offers a compromise between the model-based approach and the nonparametric approach by introducing model flexibility via averaging over several candidate models and maintains the strength of parametric models with respect to inference. We compare our approach with previous model-based methods and the nonparametric method. Simulation studies demonstrate the advantage of our method when surrogate supports are inconsistent and sample sizes are small. We illustrate our method using data from the Diabetes Prevention Program study to examine hemoglobin A1c as a surrogate marker for fasting glucose.

Assessing heterogeneity in surrogacy using censored data.

Determining whether a surrogate marker can be used to replace a primary outcome in a clinical study is complex. While many statistical methods have been developed to formally evaluate a surrogate marker, they generally do not provide a way to examine heterogeneity in the utility of a surrogate marker. Similar to treatment effect heterogeneity, where the effect of a treatment varies based on a patient characteristic, heterogeneity in surrogacy means that the strength or utility of the surrogate marker varies based on a patient characteristic. The few methods that have been recently developed to examine such heterogeneity cannot accommodate censored data. Studies with a censored outcome are typically the studies that could most benefit from a surrogate because the follow-up time is often long. In this paper, we develop a robust nonparametric approach to assess heterogeneity in the utility of a surrogate marker with respect to a baseline variable in a censored time-to-event outcome setting. In addition, we propose and evaluate a testing procedure to formally test for heterogeneity at a single time point or across multiple time points simultaneously. Finite sample performance of our estimation and testing procedure are examined in a simulation study. We use our proposed method to investigate the complex relationship between change in fasting plasma glucose, diabetes, and sex hormones using data from the diabetes prevention program study.

Testing for heterogeneity in the utility of a surrogate marker.

In studies that require long-term and/or costly follow-up of participants to evaluate a treatment, there is often interest in identifying and using a surrogate marker to evaluate the treatment effect. While several statistical methods have been proposed to evaluate potential surrogate markers, available methods generally do not account for or address the potential for a surrogate to vary in utility or strength by patient characteristics. Previous work examining surrogate markers has indicated that there may be such heterogeneity, that is, that a surrogate marker may be useful (with respect to capturing the treatment effect on the primary outcome) for some subgroups, but not for others. This heterogeneity is important to understand, particularly if the surrogate is to be used in a future trial to replace the primary outcome. In this paper, we propose an approach and estimation procedures to measure the surrogate strength as a function of a baseline covariate W and thus examine potential heterogeneity in the utility of the surrogate marker with respect to W. Within a potential outcome framework, we quantify the surrogate strength/utility using the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate. We propose testing procedures to test for evidence of heterogeneity, examine finite sample performance of these methods via simulation, and illustrate the methods using AIDS clinical trial data.

Robust approach to combining multiple markers to improve surrogacy.

Identifying effective and valid surrogate markers to make inference about a treatment effect on long-term outcomes is an important step in improving the efficiency of clinical trials. Replacing a long-term outcome with short-term and/or cheaper surrogate markers can potentially shorten study duration and reduce trial costs. There is sizable statistical literature on methods to quantify the effectiveness of a single surrogate marker. Both parametric and nonparametric approaches have been well developed for different outcome types. However, when there are multiple markers available, methods for combining markers to construct a composite marker with improved surrogacy remain limited. In this paper, building on top of the optimal transformation framework of Wang et al. (2020), we propose a novel calibrated model fusion approach to optimally combine multiple markers to improve surrogacy. Specifically, we obtain two initial estimates of optimal composite scores of the markers based on two sets of models with one set approximating the underlying data distribution and the other directly approximating the optimal transformation function. We then estimate an optimal calibrated combination of the two estimated scores which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained by the final combined score. This approach is unique in that it identifies an optimal combination of the multiple surrogates without strictly relying on parametric assumptions while borrowing modeling strategies to avoid fully nonparametric estimation which is subject to the curse of dimensionality. Our identified optimal transformation can also be used to directly quantify the surrogacy of this identified combined score. Theoretical properties of the proposed estimators are derived, and the finite sample performance of the proposed method is evaluated through simulation studies. We further illustrate the proposed method using data from the Diabetes Prevention Program study.

Using a surrogate with heterogeneous utility to test for a treatment effect.

The primary benefit of identifying a valid surrogate marker is the ability to use it in a future trial to test for a treatment effect with shorter follow-up time or less cost. However, previous work has demonstrated potential heterogeneity in the utility of a surrogate marker. When such heterogeneity exists, existing methods that use the surrogate to test for a treatment effect while ignoring this heterogeneity may lead to inaccurate conclusions about the treatment effect, particularly when the patient population in the new study has a different mix of characteristics than the study used to evaluate the utility of the surrogate marker. In this article, we develop a novel test for a treatment effect using surrogate marker information that accounts for heterogeneity in the utility of the surrogate. We compare our testing procedure to a test that uses primary outcome information (gold standard) and a test that uses surrogate marker information, but ignores heterogeneity. We demonstrate the validity of our approach and derive the asymptotic properties of our estimator and variance estimates. Simulation studies examine the finite sample properties of our testing procedure and demonstrate when our proposed approach can outperform the testing approach that ignores heterogeneity. We illustrate our methods using data from an AIDS clinical trial to test for a treatment effect using CD4 count as a surrogate marker for RNA.

Exploring Which Patients Use Their Closest Emergency Departments Using Geocoded Data.

BACKGROUND: Each year, roughly 20% of U.S. adults visit an emergency department (ED), but little is known about patients' choice of ED. OBJECTIVES: Examine the discretion patients have to choose among EDs, characteristics associated with ED choice, and relationship between ED choice and self-reported care experiences of ED patients. METHODS: We surveyed adult patients discharged to the community (DTC) in January-March 2018 from 16 geographically dispersed hospital-based EDs, geocoded patient and hospital-based ED addresses within 100 miles of patient addresses, and calculated travel distances. We examined the likelihood of visiting the closest ED based on patient and ED characteristics. Linear regression models examined the association of choosing the closest ED with seven measures of patient experience of care (scaled 0-100), adjusting for patient characteristics. RESULTS: 43.6% of 4647 responding patients visited the ED nearest their home (on average, 5.7 miles away). Patients who chose a farther ED had more urgent conditions, were more educated, and were less likely to be non-Hispanic White. They were significantly more likely to have visited an ED in a higher-rated, metropolitan, network hospital with major teaching status, a cardiac intensive care unit, and a certified trauma center. Patients who chose a farther ED were more likely to recommend that ED, with "medium-to-large" differences in scores (+4.3% more selected "definitely yes", p < 0.05). CONCLUSIONS: Fewer than half of patients visited the closest ED. Patients who chose a farther ED tended to seek higher-rated hospitals and report more favorable experiences.

Dynamic landmark prediction for mixture data.

In kin-cohort studies, clinicians want to provide their patients with the most current cumulative risk of death arising from a rare deleterious mutation. Estimating the cumulative risk is difficult when the genetic mutation status is unknown and only estimated probabilities of a patient having the mutation are available. We estimate the cumulative risk for this scenario using a novel nonparametric estimator that incorporates covariate information and dynamic landmark prediction. Our estimator has improved prediction accuracy over existing estimators that ignore covariate information. It is built within a dynamic landmark prediction framework whereby we can obtain personalized dynamic predictions over time. Compared to current standards, a simple transformation of our estimator provides more efficient estimates of marginal distribution functions in settings where patient-specific predictions are not the main goal. We show our estimator is unbiased and has more predictive accuracy compared to methods that ignore covariate information and landmarking. Applying our method to a Huntington disease study of mortality, we develop dynamic survival prediction curves incorporating gender and familial genetic information.

Racial/Ethnic Differences in Emergency Department Utilization and Experience.

BACKGROUND: Previous work has demonstrated racial/ethnic differences in emergency department (ED) utilization, but less is known about racial/ethnic differences in the experience of care received during an ED visit. OBJECTIVE: To examine differences in self-reported healthcare utilization and experiences with ED care by patients' race/ethnicity. DESIGN: Adult ED patients discharged to community (DTC) were surveyed (response rate: 20.25%) using the Emergency Department Patient Experience of Care (EDPEC) DTC Survey. Linear regression was used to estimate case-mix-adjusted differences in patient experience between racial/ethnic groups. PARTICIPANTS: 3122 survey respondents who were discharged from the EDs of 50 hospitals nationwide January-March 2016. MAIN MEASURES: Six measures: getting timely care, doctor and nurse communication, communication about medications, receipt of sufficient information about test results, whether hospital staff discussed the patient's ability to receive follow-up care, and willingness to recommend the ED. KEY RESULTS: Black and Hispanic patients were significantly more likely than White patients to report visiting the ED for an ongoing health condition (40% Black, 30% Hispanic, 28% White, p<0.001), report having visited an ED 3+ times in the last 6 months (26% Black, 25% Hispanic, 19% White, p<0.001), and report not having a usual source of care (19% Black, 19% Hispanic, 8% White, p<0.001). Compared with White patients, Hispanic patients more often reported that hospital staff talked with them about their ability to receive needed follow-up care (+7.2 percentile points, p=0.038) and recommended the ED (+7.2 points, p=0.037); Hispanic and Black patients reported better doctor and nurse communication (+6.4 points, p=0.008; +4 points, p=0.036, respectively). CONCLUSIONS: Hispanic and Black ED patients reported higher ED utilization, lacked a usual source of care, and reported better experience with ED care than White patients. Results may reflect differences in care delivery by staff and/or different expectations of ED care among Hispanic and Black patients.

Robust methods to correct for measurement error when evaluating a surrogate marker.

The identification of valid surrogate markers of disease or disease progression has the potential to decrease the length and costs of future studies. Most available methods that assess the value of a surrogate marker ignore the fact that surrogates are often measured with error. Failing to adjust for measurement error can erroneously identify a useful surrogate marker as not useful or vice versa. We investigate and propose robust methods to correct for the effect of measurement error when evaluating a surrogate marker using multiple estimators developed for parametric and nonparametric estimates of the proportion of treatment effect explained by the surrogate marker. In addition, we quantify the attenuation bias induced by measurement error and develop inference procedures to allow for variance and confidence interval estimation. Through a simulation study, we show that our proposed estimators correct for measurement error in the surrogate marker and that our inference procedures perform well in finite samples. We illustrate these methods by examining a potential surrogate marker that is measured with error, hemoglobin A1c, using data from the Diabetes Prevention Program clinical trial.

Estimation of the proportion of treatment effect explained by a high-dimensional surrogate.

Clinical studies examining the effectiveness of a treatment with respect to some primary outcome often require long-term follow-up of patients and/or costly or burdensome measurements of the primary outcome of interest. Identifying a surrogate marker for the primary outcome of interest may allow one to evaluate a treatment effect with less follow-up time, less cost, or less burden. While much clinical and statistical work has focused on identifying and validating surrogate markers, available approaches tend to focus on settings in which only a single surrogate marker is of interest. Limited work has been done to accommodate the high-dimensional surrogate marker setting where the number of potential surrogates is greater than the sample size. In this article, we develop methods to estimate the proportion of treatment effect explained by high-dimensional surrogates. We study the asymptotic properties of our proposed estimator, propose inference procedures, and examine finite sample performance via a simulation study. We illustrate our proposed methods using data from a randomized study comparing a novel whey-based oral nutrition supplement with a standard supplement with respect to change in body fat percentage over 12 weeks, where the surrogate markers of interest are gene expression probesets.

National travel distances for emergency care.

BACKGROUND: Most emergency department (ED) patients arrive by their own transport and, for various reasons, may not choose the nearest ED. How far patients travel for ED treatment may reflect both patients' access to care and severity of illness. In this study, we aimed to examine the travel distance and travel time between a patient's home and ED they visited and investigate how these distances/times vary by patient and hospital characteristics. METHODS: We randomly sampled and collected data from 14,812 patients discharged to the community (DTC) between January and March 2016 from 50 hospital-based EDs nationwide. We geocoded and calculated the distance and travel time between patient and hospital-based ED addresses, examined the travel distances/ times between patients' home and the ED they visited, and used mixed-effects regression models to investigate how these distances/times vary by patient and hospital characteristics. RESULTS: Patients travelled an average of 8.0 (SD = 10.9) miles and 17.3 (SD = 18.0) driving minutes to the ED. Patients travelled significantly farther to avoid EDs in lower performing hospitals (p < 0.01) and in the West (p < 0.05) and Midwest (p < 0.05). Patients travelled farther when visiting EDs in rural areas. Younger patients travelled farther than older patients. CONCLUSIONS: Understanding how far patients are willing to travel is indicative of whether patient populations have adequate access to ED services. By showing that patients travel farther to avoid a low-performing hospital, we provide evidence that DTC patients likely do exercise some choice among EDs, indicating some market incentives for higher-quality care, even for some ED admissions. Understanding these issues will help policymakers better define access to ED care and assist in directing quality improvement efforts. To our knowledge, our study is the most comprehensive nationwide characterization of patient travel for ED treatment to date.

Hospice Care Experiences Among Cancer Patients and Their Caregivers.

BACKGROUND: Little is known about the current quality of care for hospice cancer patients and how it varies across hospice programs in the USA. OBJECTIVE: To examine hospice care experiences among decedents with a primary cancer diagnosis and their family caregivers, comparing quality across settings of hospice care. DESIGN: We analyzed data from the Consumer Assessment of Healthcare Providers and Systems Hospice Survey (32% response rate). Top-box outcomes (0-100) were calculated overall and by care setting, adjusting for survey mode and patient case mix. PARTICIPANTS: Two hundred seventeen thousand five hundred ninety-six caregiver respondents whose family member had a primary cancer diagnosis and died in 2017 or 2018 while receiving hospice care from 2,890 hospices nationwide. MAIN MEASURES: Outcomes (0-100 scale) included 8 National Quality Forum-endorsed quality measures, as well as responses to 4 survey questions assessing whether needs were met for specific symptoms (pain, dyspnea, constipation, anxiety/sadness). KEY RESULTS: Quality measure scores ranged from 74.9 (Getting Hospice Care Training measure) to 89.5 (Treating Family Member with Respect measure). The overall score for Getting Help for Symptoms was 75.1 with item scores within this measure ranging from 60.6 (getting needed help for feelings of anxiety or sadness) to 84.5 (getting needed help for pain). Measure scores varied significantly across settings and differences were large in magnitude, with caregivers of decedents who received care in a nursing home (NH) or assisted living facility (ALF) setting consistently reporting poorer quality of care. CONCLUSIONS: Important opportunities exist to improve hospice care for symptom palliation and providing training for caregivers when their family members are at home or in an ALF setting. Efforts to improve care for cancer patients in the NH and ALF setting are especially needed.

Evaluation of longitudinal surrogate markers.

The use of surrogate markers to examine the effectiveness of a treatment has the potential to decrease study length and identify effective treatments more quickly. Most available methods to investigate the usefulness of a surrogate marker involve restrictive parametric assumptions and tend to focus on settings where the surrogate is measured at a single point in time. However, in many clinical settings, the potential surrogate marker is often measured repeatedly over time, and thus, the surrogate marker information is a trajectory of measurements. In addition, it is often difficult in practice to correctly specify the relationship between a treatment, primary outcome, and surrogate marker trajectory. In this paper, we propose a model-free definition for the proportion of the treatment effect on the primary outcome that is explained by the treatment effect on the longitudinal surrogate markers. We propose three novel flexible methods to estimate this proportion, develop the asymptotic properties of our estimators, and investigate the robustness of the estimators under multiple settings via a simulation study. We apply our proposed procedures to an AIDS clinical trial dataset to examine a trajectory of CD4 counts as a potential surrogate.

Evaluating multiple surrogate markers with censored data.

The utilization of surrogate markers offers the opportunity to reduce the length of required follow-up time and/or costs of a randomized trial examining the effectiveness of an intervention or treatment. There are many available methods for evaluating the utility of a single surrogate marker including both parametric and nonparametric approaches. However, as the dimension of the surrogate marker increases, a completely nonparametric procedure becomes infeasible due to the curse of dimensionality. In this paper, we define a quantity to assess the value of multiple surrogate markers in a time-to-event outcome setting and propose a robust estimation approach for censored data. We focus on surrogate markers that are measured at some landmark time, t0 , which occurs earlier than the end of the study. Our approach is based on a dimension reduction procedure with an option to incorporate weights to guard against potential misspecification of the working model, resulting in three different proposed estimators, two of which can be shown to be double robust. We examine the finite sample performance of the estimators under various scenarios using a simulation study. We illustrate the estimation and inference procedures using data from the Diabetes Prevention Program (DPP) to examine multiple potential surrogate markers for diabetes.

Quantifying the feasibility of shortening clinical trial duration using surrogate markers.

The potential benefit of using a surrogate marker in place of a long-term primary outcome is very attractive in terms of the impact on study length and cost. Many available methods for quantifying the effectiveness of a surrogate endpoint either rely on strict parametric modeling assumptions or require that the primary outcome and surrogate marker are fully observed that is, not subject to censoring. Moreover, available methods for quantifying surrogacy typically provide a proportion of treatment effect explained (PTE) measure and do not directly address the important questions of whether and how the trial can be ended earlier using the surrogate marker. In this article, we specifically address these important questions by proposing a PTE measure to quantify the feasibility of ending trials early based on endpoint information collected at an earlier landmark point t0 in a time-to-event outcome setting. We provide a framework for deriving an optimally predicted outcome for individual patients at t0 based on a combination of surrogate marker and event time information in the presence of censoring. We propose a non-parametric estimator for the PTE measure and derive the asymptotic properties of our estimators. Finite sample performance of our estimators are illustrated via extensive simulation studies and a real data application examining the potential of hemoglobin A1c and fasting plasma glucose to predict treatment effects on long term diabetes risk based on the Diabetes Prevention Program study.

Quantifying the bias due to observed individual confounders in causal treatment effect estimates.

It is often of interest to use observational data to estimate the causal effect of a target exposure or treatment on an outcome. When estimating the treatment effect, it is essential to appropriately adjust for selection bias due to observed confounders using, for example, propensity score weighting. Selection bias due to confounders occurs when individuals who are treated are substantially different from those who are untreated with respect to covariates that are also associated with the outcome. A comparison of the unadjusted, naive treatment effect estimate with the propensity score adjusted treatment effect estimate provides an estimate of the selection bias due to these observed confounders. In this article, we propose methods to identify the observed covariate that explains the largest proportion of the estimated selection bias. Identification of the most influential observed covariate or covariates is important in resource-sensitive settings where the number of covariates obtained from individuals needs to be minimized due to cost and/or patient burden and in settings where this covariate can provide actionable information to healthcare agencies, providers, and stakeholders. We propose straightforward parametric and nonparametric procedures to examine the role of observed covariates and quantify the proportion of the observed selection bias explained by each covariate. We demonstrate good finite sample performance of our proposed estimates using a simulation study and use our procedures to identify the most influential covariates that explain the observed selection bias in estimating the causal effect of alcohol use on progression of Huntington's disease, a rare neurological disease.