28 research outputs found
Selection of the Optimal Personalized Treatment from Multiple Treatments with Right-censored Multivariate Outcome Measures
We propose a novel personalized concept for the optimal treatment selection
for a situation where the response is a multivariate vector, that could contain
right-censored variables such as survival time. The proposed method can be
applied with any number of treatments and outcome variables, under a broad set
of models. Following a working semiparametric Single Index Model that relates
covariates and responses, we first define a patient-specific composite score,
constructed from individual covariates. We then estimate conditional means of
each response, given the patient score, correspond to each treatment, using a
nonparametric smooth estimator. Next, a rank aggregation technique is applied
to estimate an ordering of treatments based on ranked lists of treatment
performance measures given by conditional means. We handle the right-censored
data by incorporating the inverse probability of censoring weighting to the
corresponding estimators. An empirical study illustrates the performance of the
proposed method in finite sample problems. To show the applicability of the
proposed procedure for real data, we also present a data analysis using HIV
clinical trial data, that contained a right-censored survival event as one of
the endpoints
West Nile virus infection in birds and mosquitoes, New York State, 2000.
West Nile (WN) virus was found throughout New York State in 2000, with the epicenter in New York City and surrounding counties. We tested 3,403 dead birds and 9,954 mosquito pools for WN virus during the transmission season. Sixty-three avian species, representing 30 families and 14 orders, tested positive for WN virus. The highest proportion of dead birds that tested positive for WN virus was in American Crows in the epicenter (67% positive, n=907). Eight mosquito species, representing four genera, were positive for WN virus. The minimum infection rate per 1,000 mosquitoes (MIR) was highest for Culex pipiens in the epicenter: 3.53 for the entire season and 7.49 for the peak week of August 13. Staten Island had the highest MIR (11.42 for Cx. pipiens), which was associated with the highest proportion of dead American Crows that tested positive for WN virus (92%, n=48) and the highest number of human cases (n=10)
Variable selection by stepwise slicing in nonparametric regression
We consider variable selection issue in a nonparametric regression setting. Two stepwise procedures based on variance estimators are proposed for selecting the significant variables in a general nonparametric regression model. These procedures do not require multidimensional smoothing at intermediate steps and they are based on formal tests of hypotheses as opposed to existing methods in the literature. Asymptotic properties are examined and empirical results are given.Design variables Nonparametric test Smoothing
A bound on the 1-error of a nonparametric density estimator with censored data
A bound on the expected 1-distance between a density and its estimator, based on randomly right censored data, is given for any sample size. It is noted that the leading terms in the bound are comparable to the terms in the uncensored case. Also, these terms agree with the asymptotic results for density estimation with censored data.Censored data Density estimator Error bounds
Bandwidth selection for power optimality in a test of equality of regression curves
We consider the bandwidth selection in a test of equality of regression curves given by King et al. (1991). We propose two sub-sample methods that determine data-based bandwidths maximizing the power while keeping the asymptotic size of the test to be fixed at a given level. The optimality is proved and some simulation results are presented.Kernel estimator Design variables Nonparametric test
Characterization and classification of lupus patients based on plasma thermograms
<div><p>Objective</p><p>Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent health conditions and SLE diagnostic criteria.</p><p>Methods</p><p>Thermograms were visualized graphically for important differences between covariates and summarized using various measures. A modified linear discriminant analysis was used to segregate SLE versus control subjects on the basis of the thermograms. Classification accuracy was measured based on multiple training/test splits of the data and compared to classification based on SLE serological markers.</p><p>Results</p><p>Median sensitivity, specificity, and overall accuracy based on classification using plasma thermograms was 86%, 83%, and 84% compared to 78%, 95%, and 86% based on a combination of five antibody tests. Combining thermogram and serology information together improved sensitivity from 78% to 86% and overall accuracy from 86% to 89% relative to serology alone. Predictive accuracy of thermograms for distinguishing SLE and osteoarthritis / rheumatoid arthritis patients was comparable. Both gender and anemia significantly interacted with disease status for plasma thermograms (p<0.001), with greater separation between SLE and control thermograms for females relative to males and for patients with anemia relative to patients without anemia.</p><p>Conclusion</p><p>Plasma thermograms constitute an additional biomarker which may help improve diagnosis of SLE patients, particularly when coupled with standard diagnostic testing. Differences in thermograms according to patient sex, ethnicity, clinical and environmental factors are important considerations for application of thermograms in a clinical setting.</p></div
Demographics and comorbidities / other conditions by case status.
<p>Demographics and comorbidities / other conditions by case status.</p
Boxplots of summary statistics calculated for thermograms of lupus patients and controls.
<p>Top Row (from left to right): Total area under the curve, width at half height, and height at maximum temperature. Middle Row: Excess specific heat capacity () at Peak 1 (62–67°C), Peak 2 (69–73°C), and Peak 3 (75–80°C). Bottom Row: Temperature at the maximum peak (T<sub>max</sub>), first moment temperature (T<sub>FM</sub>), and ratio of at Peak 1 to at Peak 2.</p