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Diagnostic accuracy, a measure of diagnostic tests for correctly identifying patients with or without a target disease, plays an important role in evidence-based medicine. Diagnostic accuracy of a new test ideally should be evaluated by comparing to a gold standard; however, in many medical applications it may be invasive, costly, or even unethical to obtain a gold standard for particular diseases. When the accuracy of a new candidate test under evaluation is assessed by comparison to an imperfect reference test, bias is expected to occur and result in either overestimates or underestimates of its true accuracy. In addition, diagnostic test studies often involve repeated measurements of the same patient, such as the paired eyes or multiple teeth, and generally lead to correlated and clustered data. Using the conventional statistical methods to estimate diagnostic accuracy can be biased by ignoring the within-cluster correlations. Despite numerous statistical approaches have been proposed to tackle this problem, the methodology to deal with correlated and clustered data in the absence of a gold standard is limited. In this article, we propose a method based on the composite likelihood function to derive simple and intuitive closed-form solutions for estimates of diagnostic accuracy, in terms of sensitivity and specificity. Through simulation studies, we illustrate the relative advantages of the proposed method over the existing methods that simply treat an imperfect reference test as a gold standard in correlated and clustered data. Compared with the existing methods, the proposed method can reduce not only substantial bias, but also the computational burden. Moreover, to demonstrate the utility of this approach, we apply the proposed method to the study of National-Eye-Institute-funded Telemedicine Approaches to Evaluating of Acute-Phase Retinopathy of Prematurity (e-ROP), for estimating accuracies of both the ophthalmologist examination and the image evaluation.

In prognosis studies to evaluate association between a continuous biomarker and a survival outcome, investigators often classify subjects into two subclasses of the high- and low-expression groups and apply simple survival analysis techniques of the Kaplan-Meier method and the logrank test. The high- and low-expressions are defined according to whether or not the observation of the biomarker is higher than the cut-off value, which is heterogeneous across studies. The heterogeneous definitions of the cut-off value make it difficult to apply the standard meta-analysis techniques. We propose a method to estimate the concordance index for a survival outcome synthesizing published prognosis studies, in which the Kaplan-Meier estimates for the high- and low-expression groups are reported. We illustrate our proposed method with a real dataset for meta-analysis of prognosis studies evaluating Ki-67 in early breast cancer and evaluate its performance with a simulation study.

Background:. We aimed to describe and analyze the pre-hospital emergency medical service (EMS) in Beijing and provide information for the government and medical institutions to optimize EMS. Methods:. We collected all pre-hospital emergency data in Beijing from 2008 to 2017. The chief complaint in each case was classified according to the Medical Priority Dispatch System (MPDS). The sites’ administrative districts were determined through geo-encoding of addresses and then classified into four functional regions. We analyzed the demand for EMS, emergency response times (ERT), and disease spectrum for Beijing as a whole, and for each functional region. Results:. A total of 4,192,870 pre-hospital EMS cases met the inclusion criteria, with a significant increase (P

Most types of spinal cord injury (SCI) observed in humans can be replicated in adult rat models, which are widely used for laboratory studies of SCI rehabilitation. To ensure the effectiveness and efficiency of an SCI rat model, the minimal time spent performing the laminectomy procedure and the damage caused to the body are of great importance. We describe and evaluate the effectiveness and advantages of a laminectomy auxiliary device (LAD) for removing the rat vertebral lamina without injuring the spinal cord. The incision size, success rate, operation duration, body weight, BBB score, step detection, latency and amplitude of transcranial electrical motor-evoked potentials (tceMEPs), and serum MDA and SOD levels were recorded in 8 normal rats, 8 rats treated with traditional laminectomy and 8 rats treated with LAD laminectomy. Compared with traditional laminectomy, in our LAD, the surgical incision was smaller (approximately 2.2 and 1.3 cm, respectively), the success rate was higher (88.89% and 100%, respectively) and the duration shorter (14.644±1.617 and 4.821±0.668 minutes, respectively). Compared with normal rats, those treated with either laminectomy using LAD or the traditional method showed slower body weight gain and temporarily increased oxidative stress levels. However, there were no significant differences between these two groups. Our results show that laminectomy using this LAD provides three main advantages in rats: a high success rate, time savings, small incisions and reduced trauma. We believe this LAD can be used as an effective assistant tool for rodent laminectomy.

Background Although researchers have shown that prodromal symptoms can predict acute coronary events, the ability of patients with acute coronary syndrome (ACS) to identify these symptoms in a timely manner is limited. Objectives We aimed to assess prodromal symptoms in Chinese patients with ACS and their responses to symptoms by sex. Design This cross-sectional, multicenter study involved 5 teaching hospitals in China and included 806 patients admitted for ACS between June 2013 and February 2014. The McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey (Chinese version) was used to gather data. Results Among 806 patients (including 483 women), 688 (85.4%) experienced at least 1 prodromal symptom before ACS onset. Using adjusted logistic regression models, we determined that women were significantly more likely than men to report back pain, between- or under-shoulder blade pain/discomfort, sleep disturbances, anxiousness, or heart racing. The prevalence of generalized chest pain and loss of appetite was higher among men than women. Only 41% of patients attributed their prodromal symptoms to the heart, and women were more likely than men to attribute prodromal symptoms to a heart attack. Conclusions More than two-thirds of patients with ACS reported at least 1 prodromal symptom, with some significant sex differences. Most patients do not attribute their symptoms to an impending ACS event.

Purpose The purpose of this paper is to examine how signals of uncertainty in the media affect retail investor decisions and initial public offering (IPO) underpricing through theoretical and empirical methods. Design/methodology/approach The authors construct a theoretical model of the influence of media signals on IPO pricing, which describes the micro process in which uncertain signals in media influence retail investors’ decisions and IPO underpricing. Besides, the authors take 516 small and medium-size enterprises (SMEs) listed in A-share from July 2009 to December 2012 as samples for empirical tests and establish an in-depth learning model for text analysis with Java programming to measure Chinese media tone. Finally, the results of the model analysis are verified by empirical results. Findings The results show that authoritative media with high credibility can reduce the uncertainty of information sources attract more investors’ attention and improve the valuation and demand of retail investors. The higher the media credibility is the higher the IPO underpricing rate is. The uncertain tone of the media will increase the decision-making cost of investors reduce the valuation expectation and demand of the secondary market and lead to a lower IPO underpricing rate. Originality/value The authors study the influence of the uncertainty of media source and media content on the degree of IPO underpricing of SMEs. This is a useful supplement to the Chinese media tone research system that is still in the exploration stage. The research has reference value for government regulation and investor decision-making.

In biomedical research, missing data are a common problem. The statistical literature to solve this problem is well developed but overly technical and complicated for health science researchers who...

Covariates associated with treatment-effect heterogeneity can potentially be used to make personalized treatment recommendations towards best clinical outcomes. Methods for treatment-selection rule development that directly maximize treatment-selection benefits have attracted much interest in recent years, due to the robustness of these methods to outcome modeling. In practice, the task of treatment-selection rule development can be further complicated by missingness in data. Here we consider the identification of optimal treatment-selection rules for a binary disease outcome when measurements of an important covariate from study participants are partly missing. Under the missing at random assumption, we develop a robust estimator of treatment-selection rules under the direct-optimization paradigm. This estimator targets the maximum selection benefits to the population under correct specification of at least one mechanism from each of the two sets — missing data or conditional covariate distribution, and treatment assignment or disease outcome model. We evaluate and compare performance of the proposed estimator with alternative direct-optimization estimators through extensive simulation studies. We demonstrate the application of the proposed method through a real data example from an Alzheimer’s disease study for developing covariate combinations to guide the treatment of Alzheimer’s disease.

In the era of big data and cloud computing, analysts need statistical models to go beyond predicting outcomes to forecasting how outcomes change when decision-makers intervene to change one or more...

Summary Alzheimer’s disease (AD) is a chronic neurodegenerative disease that changes the functional connectivity of the brain. The alteration of the strong connections between different brain regions is of particular interest to researchers. In this article, we use partial correlations to model the brain connectivity network and propose a data-driven procedure to recover a $c$-level partial correlation graph based on PET data, which is the graph of the absolute partial correlations larger than a pre-specified constant $c$. The proposed procedure is adaptive to the “large p, small n” scenario commonly seen in whole brain studies, and it incorporates the variation of the estimated partial correlations, which results in higher power compared to the existing methods. A case study on the FDG-PET images from AD and normal control (NC) subjects discovers new brain regions, Sup Frontal and Mid Frontal in the frontal lobe, which have different brain functional connectivity between AD and NC.

Subject noncompliance is a common problem in the analysis of randomized clinical trials (RCTs). With cognitive behavioral interventions, the addition of provider noncompliance further complicates making causal inference. As a motivating example, we consider an RCT of a motivational interviewing (MI)-based behavioral intervention for treating problem drug use. Treatment receipt depends on compliance of both a therapist (provider) and a patient (subject), where MI is received when the therapist adheres to the MI protocol and the patient actively participates in the intervention. However, therapists cannot be forced to follow protocol and patients cannot be forced to cooperate in an intervention. In this article, we (1) define a causal estimand of interest based on a principal stratification framework, the average causal effect of treatment among provider-subject pairs that comply with assignment or ACE(cc); (2) explore possible assumptions that identify ACE(cc); (3) develop novel estimators of ACE(cc); (4) evaluate estimators' statistical properties via simulation; and (5) apply our proposed methods for estimating ACE(cc) to data from our motivating example.

In this article, we propose a novel statistical method for estimating the accuracy of chest computed tomography (CT) and reverse transcription polymerase chain reaction (RT-PCR) tests in the diagnosis of coronavirus disease 2019 (COVID-19), with a correction for imperfect gold standard and verification bias simultaneously. These two types of bias are often involved in estimating the diagnostic accuracy of COVID-19 tests. Imperfect gold standard bias arises when estimating accuracy measures of chest CT while using the RT-PCR test as a gold standard, despite its tendency to produce false negative results. Meanwhile, verification bias occurs in some studies where the results from chest CT are verified by RT-PCR test in a subsample of suspected cases that is not representative of the original population. Consequently, the accuracy estimates of chest CT and RT-PCR tests could be seriously biased and lead to invalid inference. Our proposed method is able to correct these two types of bias in providing unbiased and more accurate estimates of sensitivity and specificity of the two tests. Our results suggest that chest CT has higher sensitivity and lower specificity than RT-PCR, and the accuracy estimates can serve as an important reference for assessing and comparing the performance of these two tests in the diagnosis of COVID-19, and could guide policy recommendations for the implementation of these tests.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has been going on for over a year and has reemerged in several regions Therefore, understanding the covertness of COVID-19 is critical to more precisely estimating the pandemic size, especially the population of hidden carriers (those with very mild or no symptoms) Methods: A stochastic dynamic model was proposed to capture the transmission mechanism of COVID-19 and to depict the covertness of COVID-19 The proposed model captured unique features of COVID-19, changes in the diagnosis criteria, and escalating containment measures Results: The model estimated that, for the epidemic in Wuhan, 79 8% (76 7%–82 7%) of the spread was caused by hidden carriers The overall lab-confirmation rate in Wuhan up until March 8, 2020 was 0 17 (0 15–0 19) The diagnostic rate among patients with significant symptoms went up to 0 82 on March 8, 2020 from 0 43 on January 1, 2020 with escalating containment measures and nationwide medical supports The probability of resurgence could be as high as 0 72 if containment measures were lifted after zero new reported (lab-confirmed or clinically confirmed) cases in a consecutive period of 14 days This probability went down to 0 18 and 0 01 for measures lifted after 30 and 60 days, respectively Discussion: Consistent with the cases detected in Wuhan in mid-May, 2020, this study suggests that much of the COVID-19 pandemic is underreported and highly covert, which suggests that strict measures must be enforced continuously to contain the spread of the pandemic

Highlights • At the beginning of the epidemic, measures to lockdown the city could reduce imported spread. • Household transmission is not yet controlled, particularly for the infection of imported cases to elderly women. • Patients who contacted with a confirmed case of family members were admitted to the hospital earlier. • Surveillance and education of immediate admission/isolation should be emphasized, after travel restrictions were taken., Background As COVID-19 ravages continuously around the world, more information on the epidemiological characteristics and factors associated with time interval between critical events is needed to contain the pandemic and to assess the effectiveness of interventions. Methods Individual information on confirmed cases from January 21 to March 2 was collected from provincial or municipal health commissions. We identified the difference between imported and local cases in the epidemiological characteristics. Two models were established to estimate the factors associated with time interval from symptom onset to hospitalization (TOH) and length of hospital stay (LOS) respectively. Results Among 7,042 cases, 3392 (48.17%) were local cases and 3304 (46.92%) were imported cases. Since the first intervention was adopted in Hubei on January 23, the daily reported imported cases reached a peak on January 28 and gradually decreased since then. Imported cases were on average younger (41 vs. 48), and had more male (58.66% vs. 47.53%) compared to local cases. Furthermore, imported cases had more contacts with other confirmed cases (2.80 ± 2.33 vs. 2.17 ± 2.10), which were mainly within family members (2.26 ± 2.18 vs. 1.57 ± 2.06). The TOH and LOS were 2.67 ± 3.69 and 18.96 ± 7.63 days respectively, and a longer TOH was observed in elderly living in the provincial capital cities that were higher migration intensity with Hubei. Conclusions Measures to restrict traffic can effectively reduce imported spread. However, household transmission is still not controlled, particularly for the infection of imported cases to elderly women. It is still essential to surveil and educate patients about the early admission or isolation.

Computer aided detection and diagnosis (CADe and CADx) products are an emerging branch of medical device industry. However, limited technical standard has been developed for product verification and validation. It will be helpful to investigate the current practice of preclinical and clinical evaluation of approved products and provide insights for future standardization.Document review was conducted on 56 products approved by the United States Food and Drug Administration, including Summary of Safety and Effectiveness Data, 510(k) decision andPreclinical studies were widely adopted in the verification of CADe/CADx products. Standalone performance testing was a common procedure, but the selection of testing dataset and performance metrics showed significant variability and flexibility among manufacturers. Clinical studies were reported by all class III products and some class II products, and Multi-Reader Multi-Case design was commonly used. However, statistical analysis and presentation/interpretation of results was oftentimes incomplete. To resolve above issues, systematic development of standards of CADe/CADx is encouraged, which can be implemented at different aspects through the product lifecycle.

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a pandemic causing global health problem. We provide estimates of the daily trend in the size of the epidemic in Wuhan based on detailed information of 10 940 confirmed cases outside Hubei province. Methods In this modelling study, we first estimate the epidemic size in Wuhan from 10 January to 5 April 2020 with a newly proposed model, based on the confirmed cases outside Hubei province that left Wuhan by 23 January 2020 retrieved from official websites of provincial and municipal health commissions. Since some confirmed cases have no information on whether they visited Wuhan before, we adjust for these missing values. We then calculate the reporting rate in Wuhan from 20 January to 5 April 2020. Finally, we estimate the date when the first infected case occurred in Wuhan. Results We estimate the number of cases that should be reported in Wuhan by 10 January 2020, as 3229 (95% confidence interval [CI]: 3139–3321) and 51 273 (95% CI: 49 844–52 734) by 5 April 2020. The reporting rate has grown rapidly from 1.5% (95% CI: 1.5–1.6%) on 20 January 2020, to 39.1% (95% CI: 38.0–40.2%) on 11 February 2020, and increased to 71.4% (95% CI: 69.4–73.4%) on 13 February 2020, and reaches 97.6% (95% CI: 94.8–100.3%) on 5 April 2020. The date of first infection is estimated as 30 November 2019. Conclusions In the early stage of COVID-19 outbreak, the testing capacity of Wuhan was insufficient. Clinical diagnosis could be a good complement to the method of confirmation at that time. The reporting rate is very close to 100% now and there are very few cases since 17 March 2020, which might suggest that Wuhan is able to accommodate all patients and the epidemic has been controlled.

COVID-19 caused rapid mass infection worldwide. Understanding its transmission characteristics, including heterogeneity and the emergence of super spreading events (SSEs) where certain individuals infect large numbers of secondary cases, is of vital importance for prediction and intervention of future epidemics. Here, we collected information of all infected cases (135 cases) between 21 January and 26 February 2020 from official public sources in Tianjin, a metropolis of China, and grouped them into 43 transmission chains with the largest chain of 45 cases and the longest chain of four generations. Utilizing a heterogeneous transmission model based on branching process along with a negative binomial offspring distribution, we estimated the reproductive number R and the dispersion parameter k (lower value indicating higher heterogeneity) to be 0.67 (95% CI: 0.54&ndash, 0.84) and 0.25 (95% CI: 0.13&ndash, 0.88), respectively. A super-spreader causing six infections was identified in Tianjin. In addition, our simulation allowing for heterogeneity showed that the outbreak in Tianjin would have caused 165 infections and sustained for 7.56 generations on average if no control measures had been taken by local government since 28 January. Our results highlighted more efforts are needed to verify the transmission heterogeneity of COVID-19 in other populations and its contributing factors.

This study estimated the incubation distribution of COVID-19 using the theory of renewal process., We have proposed a novel, accurate low-cost method to estimate the incubation-period distribution of COVID-19 by conducting a cross-sectional and forward follow-up study. We identified those presymptomatic individuals at their time of departure from Wuhan and followed them until the development of symptoms. The renewal process was adopted by considering the incubation period as a renewal and the duration between departure and symptoms onset as a forward time. Such a method enhances the accuracy of estimation by reducing recall bias and using the readily available data. The estimated median incubation period was 7.76 days [95% confidence interval (CI): 7.02 to 8.53], and the 90th percentile was 14.28 days (95% CI: 13.64 to 14.90). By including the possibility that a small portion of patients may contract the disease on their way out of Wuhan, the estimated probability that the incubation period is longer than 14 days was between 5 and 10%.

IntroductionPrevious studies have provided equivocal evidence of antidepressant use on subsequent cognitive impairment; this could be due to inconsistent modeling approaches. Our goals are methodological and clinical. We evaluate the impact of statistical modeling approaches on the associations between antidepressant use and risk of Mild Cognitive Impairment (MCI) in older adults with depression.Methods716 participants were enrolled. Our primary analysis employed a time-dependent Cox proportional hazards model. We also implemented two fixed-covariate proportional hazards models-one based on having ever used antidepressants during follow-up, and the other restricted to baseline use only.ResultsTreating antidepressant use as a time-varying covariate, we found no significant association with incident MCI (HR = 0.92, 95% CI: 0.70, 1.20). In contrast, when antidepressant use was treated as a fixed covariate, we observed a significant association between having ever used antidepressants and lower risk of MCI (HR = 0.40, 95% CI: 0.28, 0.56). However, in the baseline-use only model, the association was non-significant (HR = 0.84, 95% CI: 0.60, 1.17).DiscussionOur results were dependent upon statistical models and suggest that antidepressant use should be modeled as a time-varying covariate. Using a robust time-dependent analysis, antidepressant use was not significantly associated with incident MCI among cognitively normal persons with depression.

Statistical agencies are releasing statistical data to other agencies for research purposes or to inform public policy. Prior to data release, these agencies have a legal and ethical obligation to protect the confidentiality of individuals in the data. Agencies often release altered versions of the data, but there usually remains risks of disclosure. Many well-studied risk measures are available to assess risk; however, many agencies today continue to use subjective judgement, past experience, and ad hoc rules or checklists to assess disclosure risk. More recently, there has been a recognized demand for quantitative risk measures that provide a more objective criteria for data release. This tutorial provides an overview of the statistical disclosure control framework for microdata. We focus on the risk analysis stage within this framework by defining existing disclosure risk measures and how to estimate them with available software.

Approximately 233,898 individuals in the Veterans Affairs healthcare network are hepatitis C virus (HCV)-infected, making the Veterans Affairs the single largest provider of HCV care in the USA. Direct-acting antiviral treatment regimens for HCV offer high cure rates. However, these medications pose an enormous financial burden, and whether HCV cure is associated with decreased healthcare costs is poorly defined. To measure downstream healthcare costs in a national population of HCV-infected patients up to 9 years post-HCV antiviral treatment, to compare downstream healthcare costs between cured and uncured patients, and to assess impact of cirrhosis status on cost differences. This is a retrospective cohort study (2004–2014) of hepatitis C-infected patients who initiated antiviral treatment within the United States Veterans Affairs healthcare system October 2004–September 2013. We measured inpatient, outpatient, and pharmacy costs after HCV treatment. For the entire cohort, cure was associated with mean cumulative cost savings in post-treatment years three–six, but no cost savings by post-treatment year nine. By post-treatment year nine, cure in cirrhosis patients was associated with a mean cumulative cost savings of $9474 (− 32,666 to 51,614) per patient, while cure in non-cirrhotic patients was associated with a mean cumulative cost excess of $2526 (− 12,211 to 7159) per patient. Among patients with cirrhosis at baseline, cure is associated with absolute cost savings up to 9 years post-treatment compared to those without cure. Among patients without cirrhosis, early post-treatment cost savings are counterbalanced by higher costs in later years.

It remains unclear what clinical features inform the accuracy of a clinical diagnosis of Alzheimer disease (AD). Data were obtained from the National Alzheimer’s Coordinating Center to compare clinical and neuropathologic features among participants who did or did not have Alzheimer disease neuropathologic changes (ADNC) at autopsy. Participants (1854) had a clinical Alzheimer dementia diagnosis and ADNC at autopsy (Confirmed-AD), 204 participants had an AD diagnosis and no ADNC (AD-Mimics), and 253 participants had no AD diagnosis and ADNC (Unidentified-AD). Compared to Confirmed-AD participants, AD-Mimics had less severe cognitive impairment, while Unidentified-AD participants displayed more parkinsonian signs, depression, and behavioral problems. This study highlights the importance of developing a complete panel of biomarkers as a tool to inform clinical diagnoses, as clinical phenotypes that are typically associated with diseases other than AD may result in inaccurate diagnoses.

Clinical Named Entity Recognition (CNER) is a critical task which aims to identify and classify clinical terms in electronic medical records. In recent years, deep neural networks have achieved significant success in CNER. However, these methods require high-quality and large-scale labeled clinical data, which is challenging and expensive to obtain, especially data on Chinese clinical records. To tackle the Chinese CNER task, we pre-train BERT model on the unlabeled Chinese clinical records, which can leverage the unlabeled domain-specific knowledge. Different layers such as Long Short-Term Memory (LSTM) and Conditional Random Field (CRF) are used to extract the text features and decode the predicted tags respectively. In addition, we propose a new strategy to incorporate dictionary features into the model. Radical features of Chinese characters are used to improve the model performance as well. To the best of our knowledge, our ensemble model outperforms the state of the art models which achieves 89.56% strict F1 score on the CCKS-2018 dataset and 91.60% F1 score on CCKS-2017 dataset.

Vascular risk factors (VRFs) have been associated with clinically diagnosed Alzheimer disease (AD), but few studies have examined the association between VRF and AD neuropathology (ADNP) in cognitively normal individuals. We used longitudinal data from the National Alzheimer's Disease Center's Uniform Data Set and Neuropathology Data Set to examine the association between VRF and ADNP (moderate to frequent neuritic plaques; Braak stage III-VI) in those with normal cognition. Our sample included 53 participants with ADNP and 140 without ADNP. Body mass index (BMI), resting heart rate (HR), and pulse pressure (PP) were measured at each visit; values were averaged across participant visits and examined annual change in BMI, PP, and HR. Hypertension, diabetes, and hypercholesterolemia were self-reported. In the multivariable logistic regression analyses, average BMI and HR were associated with lower odds of ADNP, and annual increases in HR and BMI were associated with higher odds of ADNP. A previously experienced decline in BMI or HR in late-life (therefore, currently low BMI and low HR) as well as a late-life increase in BMI and HR may indicate underlying AD pathology. Additional clinicopathological research is needed to elucidate the role of changes in late-life VRF and AD pathogenesis.

The objective of diagnostic studies or prognostic studies is to evaluate and compare predictive capacities of biomarkers. Suppose we are interested in evaluation and comparison of predictive capacities of continuous biomarkers for a binary outcome based on research synthesis. In analysis of each study, subjects are often classified into two groups of the high-expression and low-expression groups according to a cut-off value, and statistical analysis is based on a 2 × 2 table defined by the response and the high expression or low expression of the biomarker. Because the cut-off is study specific, it is difficult to interpret a combined summary measure such as an odds ratio based on the standard meta-analysis techniques. The summary receiver operating characteristic curve is a useful method for meta-analysis of diagnostic studies in the presence of heterogeneity of cut-off values to examine discriminative capacities of biomarkers. We develop a method to estimate positive or negative predictive curves, which are alternative to the receiver operating characteristic curve based on information reported in published papers of each study. These predictive curves provide a useful graphical presentation of pairs of positive and negative predictive values and allow us to compare predictive capacities of biomarkers of different scales in the presence of heterogeneity in cut-off values among studies. Copyright © 2016 John Wiley & Sons, Ltd.

We investigated the epidemiological and clinical characteristics deaths from road traffic injury (RTI) in Beijing, and provided evidence useful for the prevention of fatal traffic trauma and for the treatment of traffic-related injuries. We retrospectively reviewed death cases provided by the Beijing Red Cross Emergency Center on road traffic injury deaths from 2008 to 2017. We analyzed population characteristics, time distribution, distribution of transportation modes, intervals to death, locations and injured body parts. From 2008 to 2017, there were 3327 deaths from RTI recorded by the Beijing Red Cross Emergency Center, with mainly males among these deaths. The average age at death was 46.19 ± 17.43 years old (46.19, 0.43–100.24). In accidents with more detail recorded, pedestrians and people using nonmotorized transportation modes suffered the most fatalities (664/968, 68.60%). The most commonly injured body parts were the head (2569/3327, 77.22%), followed by the chest (180/3327, 5.41%), abdomen (130/3327, 3.91%), lower extremities (68/3327, 2.04%), pelvis (67/3327, 2.01%), spinal cord (31/3327, 0.93%), and upper extremities (26/3327, 0.78%). Burns accounted for 0.96% (32/3327), and unknown body parts were affected in 11.28% (365/3327). The average time interval from injury to death was 36.90 ± 89.57 h (36.90, 0–720); 46.7% (1554/3327) died within 10 minutes after injury; 9.02% (300/3327) died between 10 min and 1 hour; 30.33% (1009/3327) died between 1 hour and 3 days; 13.95% (464/3327) died between 3 and 30 days. In Beijing, RTI is a significant cause of preventable death, particularly among pedestrians and users of non-motorized vehicles. Head trauma was the most lethal cause of RTI deaths. Our findings suggested that interventions to prevent collisions and reduce injuries, and improved trauma treatment process and trauma rescue system could address a certain proportion of avoidable RTI deaths.

The continued efforts to evaluate biomarkers’ predictive abilities and identify optimal biomarker combinations are often challenged by the absence of a gold standard, that is, the true disease status. Current methods that address this issue are mostly developed for binary or ordinal diagnostic tests, which do not fully utilize information provided by continuous biomarkers, or require strong parametric assumptions. Moreover, limited methods exist to allow for the inclusion of covariates—despite their crucial role in facilitating the accurate evaluation of biomarkers. In this paper, we proposed a latent profile approach to evaluating diagnostic accuracy of biomarkers without a gold standard. The method allows for flexible biomarker distributions and incorporation of previous knowledge about risk factors while simultaneously permitting researchers to model paticipants’ characteristics that putatively affect biomarker levels, and therefore provides information needed to develop more personalized diagnostic procedures. Additionally, the proposed method presents a potential strategy for biomarker combination when gold standard information is unavailable, as it derives a composite risk score for the underlying disease status. The method is applied to evaluate different cerebral spinal fluid (CSF) biomarkers for Alzheimer’s disease (AD) detection. The results show that CSF biomarkers hold significant potential for facilitating early AD detection and for continuous disease monitoring. Furthermore, they call attention to biomarker variability in subgroups and reexamination of CSF biomarker distributions. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.

Background: Lung resections carry a significant risk of complications necessitating the characterization of peri-operative risk factors. Unhealthy alcohol use represents one potentially modifiable factor. In this retrospective cohort study, the largest to date of lung resections in the Veterans Health Administration (VHA), we examined the association between unhealthy alcohol use and postoperative complications and mortality. Methods: Veterans Affairs Surgical Quality Improvement Program data recorded at 86 medical centers between 2007 and 2011 were used to identify 4,715 patients that underwent lung resection. Logistic regression models, adjusted for demographics and comorbidities, were fit to assess the association between unhealthy alcohol use (report of >2 drinks per day in the 2 weeks preceding surgery) and 30-day outcomes. Results: Among 4,715 patients that underwent pulmonary resection, 630 (13.4%) reported unhealthy alcohol use (>2 drinks/day). Overall, postoperative complications occurred in 896 (19.0%) patients, including pneumonia in 524 (11.1%). The rate of mortality was 2.6%. In adjusted analyses, complications were significantly more common among patients with unhealthy alcohol use [odds ratio (OR), 1.42; 95% confidence interval (CI), 1.15–1.74] including, specifically, pneumonia (OR, 1.69; 95% CI, 1.32–2.15). No statistically significant association was identified between unhealthy alcohol use and mortality (OR, 1.27; 95% CI, 0.75–2.02). In secondary analyses that stratified by smoking status at the time of surgery, drinking more than 2 drinks per day was associated with post-operative complications in patients reporting current smoking (OR, 1.51; 95% CI, 1.18–1.91) and was not identified in those reporting no current smoking at the time of surgery (OR, 1.23; 95% CI, 0.79–1.85). Conclusions: In this large VHA study, 13% of patients undergoing lung resection reported drinking more than 2 drinks per day in the preoperative period, which was associated with increased risk of postoperative complications. Unhealthy alcohol use may be an important target for perioperative risk-mitigation interventions, particularly in patients who report current smoking.

Recent technological advances continue to provide noninvasive and more accurate biomarkers for evaluating disease status. One standard tool for assessing the accuracy of diagnostic tests is the receiver operating characteristic (ROC) curve. Few statistical methods exist to accommodate multiple continuous-scale biomarkers in the framework of ROC analysis. In this paper, we propose a method to integrate continuous-scale biomarkers to optimize classification accuracy. Specifically, we develop semiparametric transformation models for multiple biomarkers. We assume that unknown and marker-specific transformations of biomarkers follow a multivariate normal distribution. Our models accommodate biomarkers subject to limits of detection and account for the dependence among biomarkers by including a subject-specific random effect. We also propose a diagnostic measure using an optimal linear combination of the transformed biomarkers. Our diagnostic rule does not depend on any monotone transformation of biomarkers and is not sensitive to extreme biomarker values. Nonparametric maximum likelihood estimation (NPMLE) is used for inference. We show that the parameter estimators are asymptotically normal and efficient. We illustrate our semiparametric approach using data from the Endometriosis, Natural History, Diagnosis, and Outcomes (ENDO) study.

Hypertensive disorders of pregnancy are closely related to maternal mortality and morbidity. Calcium supplementation during pregnancy seems to reduce the risk of hypertensive disorders. No systematic review on multicentre RCTs of calcium supplementation during pregnancy has been published. The purpose of this study was to report a quantitative systematic review of the effectiveness of calcium supplementation during pregnancy on reducing the risk of hypertensive disorders of pregnancy and related problems. Publications over the years of 1991-2012 were searched through PubMed, Science Direct, EMBASE, CINAHL and Web of Science. The literatures were selected of the multicentre RCTs on calcium supplementation during pregnancy in prevention of hypertensive disorders and related problems. Reference lists from the studies were also examined for additional references. Studies were critically appraised by three independent reviewers, and the Cochrane Handbook was used to assess the quality of those included trials. Four studies were included in this systematic review. All included studies were high quality, with low risk of bias. There was an observed risk reduction in hypertension in calcium group. However, there was no reduction in the risk of severe gestational hypertension, pre-eclampsia, severe pre-eclampsia, preterm birth and low birthweight. Calcium supplementation appears to reduce the risk of hypertension in pregnancy.

Purpose Optimizing evidence-based care to improve quality is a critical priority in the United States. We sought to examine adherence to imaging guideline recommendations for staging in patients with locally advanced lung cancer in a national cohort. Methods We identified 3,808 patients with stage IIB, IIIA, or IIIB lung cancer by using the national Department of Veterans Affairs (VA) Central Cancer Registry (2004-2007) and linked these patients to VA and Medicare databases to examine receipt of guideline-recommended imaging based on National Comprehensive Cancer Network and American College of Radiology Appropriateness Criteria. Our primary outcomes were receipt of guideline-recommended brain imaging and positron emission tomography (PET) imaging. We also examined rates of overuse defined as combined use of bone scintigraphy (BS) and PET, which current guidelines recommend against. All imaging was assessed during the period 180 days before and 180 days after diagnosis. Results Nearly 75% of patients received recommended brain imaging, and 60% received recommended PET imaging. Overuse of BS and PET occurred in 25% of patients. More advanced clinical stage and later year of diagnosis were the only clinical or demographic factors associated with higher rates of guideline-recommended imaging after adjusting for covariates. We observed considerable regional variation in recommended PET imaging and overuse of combined BS and PET. Conclusion Receipt of guideline-recommended imaging is not universal. PET appears to be underused overall, whereas BS demonstrates continued overuse. Wide regional variation suggests that these findings could be the result of local practice patterns, which may be amenable to provider education efforts such as Choosing Wisely.

High-grade serous ovarian cancer (HGSC) is an aggressive cancer with a worse clinical outcome. Therefore, studies about the prognosis of HGSC may provide therapeutic avenues to improve patient outcomes. Since genome alteration are manifested at the protein level, we integrated protein and mRNA data of ovarian cancer from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) and utilized the sparse overlapping group lasso (SOGL) method, a new mechanism-driven variable selection method, to select dysregulated pathways and crucial proteins related to the survival of HGSC. We found that biosynthesis of amino acids was the main biological pathway with the best predictive performance (AUC = 0.900). A panel of three proteins, namely EIF2B1, PRPS1L1 and MAPK13 were selected as potential predictive proteins and the risk score consisting of these three proteins has predictive performance for overall survival (OS) and progression free survival (PFS), with AUC of 0.976 and 0.932, respectively. Our study provides additional information for further mechanism and therapeutic avenues to improve patient outcomes in clinical practice.

In this paper, by analyzing the relationship between rotor position and phase back electromotive forces (PB-EMFs) of brushless DC motor (BLDCM), a novel rotor position detection method based on morphological filter algorithm (MFA) for BLDCM is proposed. MFA is applied to identify the signal turning points (STPs) of the input stator PB-EMFs, which provides technical support for the current commutations of PWM control system. In addition, accurate current commutation point detection of BLDCM promises rotor field to keep perpendicularity with armature magnetic field, to achieve accurate detection of rotor position and position sensorless control of BLDCM. Results of simulation studies proved that, the proposed detection method can realize the position sensorless control for BLDCM by obtaining the STPs of PB-EMFs in BLDCM, which greatly improves the static and dynamic performance of speed regulation system of BLDCM, and obtaining a outstanding control effect.

Prognostic biomarker studies, which examine the association between biomarkers and patients’ prognoses, have played important roles in clinical decision making. Since prognostic studies are often conducted with small sample sizes in a limited number of centers, meta-analysis is expected to be a powerful tool to obtain sound evidence on prognostic biomarkers. However, the application of meta-analysis of prognostic studies has been limited partly due to the lack of sound statistical methods. In this chapter, we introduce some recently developed methods useful for the evaluation of diagnostic or predictive capacities of biomarkers for binary or time-to-event outcomes. In addition, we newly present a novel method to estimate the time-dependent positive and negative predictive value curves based on meta-analysis.

Autism spectrum disorders are a member of the pervasive developmental disorders (PDDs) that have been increasing dramatically since described by Leo Kanner in 1943. In the past decade, the number of epidemiological publications addressing air pollution exposures and autism has grown correspondingly, but the association is still unclear. Whether air pollutants play a causal role and which substances are related with autism requires further study. We systematically reviewed the literature from 2005 to 2016 in MEDLINE (National Library of Medicine), Web of Science, and PubMed and summarized the association between different air pollutants and autism. Furthermore, we further discussed the exposure time window and potential confounders that should be considered in the association analysis studies. Our objective is to summarize the association between different air pollutants and autism with literature, which has been published since 2005, and explore whether the exposure time window and potential confounders have influence on this association. These results could provide more comprehensive information about the association between air pollutants and autism and be helpful towards further validation study. Graphical abstract ᅟ.

Rational and Objectives Receiver operating characteristic (ROC) analysis is often used to find the optimal combination of biomarkers. When the subject level covariates affect the magnitude and/or accuracy of the biomarkers, the combination rule should take into account of the covariate adjustment. The authors propose two new biomarker combination methods that make use of the covariate information. Materials and Methods The first method is to maximize the area under the covariate-adjusted ROC curve (AAUC). To overcome the limitations of the AAUC measure, the authors further proposed the area under covariate-standardized ROC curve (SAUC), which is an extension of the covariate-specific ROC curve. With a series of simulation studies, the proposed optimal AAUC and SAUC methods are compared with the optimal AUC method that ignores the covariates. The biomarker combination methods are illustrated by an example from Alzheimer's disease research. Results The simulation results indicate that the optimal AAUC combination performs well in the current study population. The optimal SAUC method is flexible to choose any reference populations, and allows the results to be generalized to different populations. Conclusions The proposed optimal AAUC and SAUC approaches successfully address the covariate adjustment problem in estimating the optimal marker combination. The optimal SAUC method is preferred for practical use, because the biomarker combination rule can be easily evaluated for different population of interest.

Based on AutoCAD 2010, Visual C++. NET 2008 and ObjectARX 2010 the hydraulic design software of pump suction chambers, called PXSS, was successfully developed. The PXSS could design all kinds of pump suction chambers. Key technologies in the development process of software PXSS were introduced. A pipeline pump suction chamber and a spiral-shaped suction chamber were designed with the PXSS software. The two examples show that the PXSS can realize the parametric drawing of the pump suction chamber and improved the efficiency of the design staffs. So, it could be applied to engineering practice.

Personalized medicine is an area of growing attention in medical research and practice. A market-ready companion diagnostic test (CDx) is used in personalized medicine for identifying the best treatment for an individual patient. Unfortunately, development of CDx may lag behind the development of the drug, and consequently we use a clinical trial assay (CTA) to enroll patients into the drug pivotal clinical trial instead. Thus, when CDx becomes available, a bridging study will be required to assess the drug efficacy in the CDx intended use (CDx IU) population. Due to missingness of the CDx results that could be associated with randomization, one challenge we face in a bridging study is covariate imbalance between treatment arms for the subpopulation with both positive CDx and CTA. In this paper, we evaluate the performance of two methods in bridging studies under a causal inference framework. Particularly, we aim to use the propensity score method with doubly robust estimation and optimal matching to address the challenge. We extend under a current framework on drug efficacy estimation in the CDx IU population, using data from both the bridging study and the CTA drug pivotal clinical trial. Both approaches are discussed in the context of a randomized bridging study, and a targeted design clinical trial with simulations, followed by analyzing simulated data that mimic a real ongoing clinic trial.

// Junnan Li 1, * , Hongyu Xie 1, * , Ang Li 1 , Jinlong Cheng 2 , Kai Yang 1 , Jingtao Wang 1 , Wenjie Wang 1 , Fan Zhang 1 , Zhenzi Li 1 , Harman S. Dhillon 3 , Margarita S. Openkova 3 , Xiaohua Zhou 4 , Kang Li 1 and Yan Hou 1, 5 1 Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, P.R. China 2 Department of Gynecology Oncology, The Affiliated Tumor Hospital of Harbin Medical University, Harbin 150086, P.R. China 3 Harbin Medical University, Harbin 150086, P.R. China 4 Department of Biostatistics, University of Washington, Seattle 96596, WA, U.S.A 5 Key Laboratory of Cardiovascular Medicine Research (Harbin Medical University), Ministry of Education, Harbin 150086, P.R. China * These authors contributed equally to this work Correspondence to: Yan Hou, email: houyan@ems.hrbmu.edu.cn Kang Li, email: likang@ems.hrbmu.edu.cn Keywords: epithelial ovarian cancer, lipidomics, recurrence, early recurrence Received: June 03, 2016 Accepted: July 29, 2016 Published: August 25, 2016 ABSTRACT Epithelial ovarian cancer (EOC) is the most deadly gynecologic malignancy worldwide due to its high recurrence rate after surgery and chemotherapy. There is a critical need for discovery of novel biomarkers for EOC recurrence providing higher prediction power than that of the present ones. Lipids have been reported to associate with development and progression of cancer. In the current study, we aim to identify and validate the lipids which were relevant to the ovarian cancer recurrence based on plasma lipidomics performed by ultra-performance liquid chromatography coupled with mass spectrometry. In order to fulfill this objective, plasma from 70 EOC patients with follow up information was obtained. The results revealed that patients with and without recurrence could be clearly distinguished based on their lipid profiles. Thirty-one lipid metabolites were identified as potential biomarkers for EOC recurrence. The AUC value of these metabolite combinations for predicting EOC recurrence was 0.897. In terms of clinical applicability, LysoPG(20:5) arose as a potential EOC recurrence predictive biomarker to increase the predictive power of clinical predictors from AUC value 0.739 to 0.875. Additionally, we still found that individuals with early relapses (< 6 months) had a distinctive metabolomic pattern compared with late EOC and non-EOC recurrence subjects. Interestingly, decreased levels of triglycerides (TGs) were found to be a specific metabolic feature foreshadowing an early relapse. In conclusion, plasma lipidomics study could be used for predicting EOC recurrences, as well as early and late recurrent cases. The lipid biomarker research improves the predictive power of clinical predictors and the identified biomarkers are of great prognostic and therapeutic potential.

Study planning is particularly complex for survival trials because it usually involves an accrual period and a continued observation period after accrual closure. The three-arm clinical trial design, which includes a test treatment, an active reference, and a placebo control, is the gold standard design for the assessment of non-inferiority. The existing statistical methods of calculating minimal sample size for non-inferiority trials with three-arm design and survival-type endpoints cannot take into consideration the accrual rate of patients to the trial, the length of accrual period, the length of continued observation period after accrual closure, and unbalanced allocation of the total sample size. The purpose of this paper is to develop a statistical method, which allows for all these sources of variability for planning non-inferiority trials with the gold standard design for censored, exponentially distributed time-to-event data. The proposed method is based on the assumption of exponentially distributed failure times and a non-inferiority test formulated in terms of the retention of effect hypotheses. It can be used to calculate the duration of accrual required to assure a desired power for non-inferiority trials with active and placebo control. We illustrate the use of the method by considering a randomized, active- and placebo-controlled trial in depression associated with Parkinson’s disease. We then explore the validity of the proposed method by simulation studies. An R-language program for the implementation of the proposed algorithm is provided as supplementary material.

Traditional decoupling control strategy of doubly-fed induction generator (DFIG) wind turbine makes little contribution to system inertia and do not participate in the system frequency control, the synchronization of large-scale wind power requires wind turbine have the ability to participate in the regulation of power system frequency. This paper adds a frequency control segment to traditional DFIG wind turbine and considers the doubly-fed wind turbine operating on the state of the super-synchronous speed, by analysis the effect of inertia and proportional control strategies, a fuzzy control strategy which combines the advantages of the former two control strategies is proposed, simulation results show that this control strategy can more effectively improve the system frequency response.

In this paper, the conventional method to acquire Thevenin equivalent parameters is described. The method is based on features of power systems and utilizes the base-case system information. Considering the impact of loads on Thevenin equivalent potential and impedance, this paper makes improvements and brings it closer to the actual situation. The necessary condition about limit power transmission is derived, and the impedance modulus margin can be used to determine voltage stability of power system. Finally, the results of IEEE-14 bus system calculations show effectiveness of the method.

Two common problems in assessing the accuracy of traditional Chinese medicine (TCM) doctors in detecting a particular symptom are the unknown true symptom status and the ordinal-scale of the symptom status. Wang et al. (Biostatistics 2011; DOI: 10.1093/biostatistics/kxq075) proposed a nonparametric maximum likelihood method for estimating the accuracy of different TCM doctors without a gold standard when the true symptom status is measured on an ordinal-scale. A key assumption of their work is that the diagnosis results are independent conditional on the gold standard. This assumption can be violated in many practical situations.In this paper, we propose a random effects modeling approach that extends their method to incorporate dependence structure among different tests or doctors. The proposed method is illustrated on a real data set from TCM, which contains the diagnostic results from five doctors for the same patients regarding symptoms related to Chills disease. The same data set was analyzed by Wang et al. under the conditional independence assumption. In addition, we also discuss an ad hoc test for the model fitting and a likelihood ratio test on the random effects.

For many medical conditions, several treatment options may be available for treating patients. We consider evaluating markers based on a simple treatment selection policy that incorporates information on the patient's marker value. For example, colon cancer patients may be treated by surgery alone or surgery plus chemotherapy. The c-myc gene expression level may be used as a biomarker for treatment selection. Although traditional regression methods may assess the effect of the marker and treatment on outcomes, it is more appealing to quantify directly the potential impact on the population of using the marker to select treatment. A useful tool is the selection impact (SI) curve proposed by Song and Pepe for binary outcomes (Biometrics 2004; 60:874–883). However, the current SI method does not deal with continuous outcomes, nor does it allow to adjust for other covariates that are important for treatment selection. In this paper, we extend the SI curve for general outcomes, with a specific focus on survival time. We further propose the covariate-specific SI curve to incorporate covariate information in treatment selection. Nonparametric and semiparametric estimators are developed accordingly. We show that the proposed estimators are consistent and asymptotically normal. The performance is assessed by simulation studies and illustrated through an application to data from a cancer clinical trial. Copyright © 2011 John Wiley & Sons, Ltd.

Based on the SCAD penalty and the area under the ROC curve (AUC), we propose a new method for selecting and combining biomarkers for disease classification and prediction. The proposed estimator for the combination of the biomarkers has an oracle property; that is, the estimated combination of the biomarkers performs as well as it would have been if the biomarkers significantly associated with the outcome had been known in advance, in terms of discriminative power. The proposed estimator is computationally feasible, n1/2-consistent and asymptotically normal. Simulation studies show that the proposed method performs better than existing methods. We illustrate the proposed methodology in the acoustic startle response study. The Canadian Journal of Statistics 39: 324–343; 2011 © 2011 Statistical Society of Canada

To investigate major methods of design and statistical analysis in controlled clinical acupuncture trials published in the West during the past six years (2003-2009) and, based on this analysis, to provide recommendations that address methodological issues and challenges in clinical acupuncture research.PubMed was searched for acupuncture RCTs published in Western journals in English between 2003 and 2009. The keyword used was acupuncture.One hundred and eight qualified reports of acupuncture trials that included more than 30 symptoms/conditions were identified, analyzed, and grouped into efficacy (explanatory), effectiveness (pragmatically beneficial), and other (unspecified) studies. All were randomized controlled clinical trials (RCTs). In spite of significant improvement in the quality of acupuncture RCTs in the last 30 years, these reports show that some methodological issues and shortcomings in design and analysis remain. Moreover, the quality of the efficacy studies was not superior to that of the other types of studies. Research design and reporting problems include unclear patient criteria and inadequate practitioner eligibility, inadequate randomization, and blinding, deficiencies in the selection of controls, and improper outcome measurements. The problems in statistical analysis included insufficient sample sizes and power calculations, inadequate handling of missing data and multiple comparisons, and inefficient methods for dealing with repeated measure and cluster data, baseline value adjustment, and confounding issues.Despite recent advancements in acupuncture research, acupuncture RCTs can be improved, and more rigorous research methods should be carefully considered.

In biomedical studies, there are multiple sources of information available of which only a small number of them are associated with the diseases. It is of importance to select and combine these factors that are associated with the disease in order to predict the disease status of a new subject. The receiving operating characteristic (ROC) technique has been widely used in disease classification, and the classification accuracy can be measured with area under the ROC curve (AUC). In this article, we combine recent variable selection methods with AUC methods to optimize diagnostic accuracy of multiple risk factors. We first describe one new and some recent AUC-based methods for effectively combining multiple risk factors for disease classification. We then apply them to analyze the data from a new clinical study, investigating whether a combination of traditional Chinese medicine symptoms and standard Western medicine risk factors can increase discriminative accuracy in diagnosing osteoporosis (OP). Based on the results, we conclude that we can make a better diagnosis of primary OP by combining traditional Chinese medicine symptoms with Western medicine risk factors.

Our research is motivated by 2 methodological problems in assessing diagnostic accuracy of traditional Chinese medicine (TCM) doctors in detecting a particular symptom whose true status has an ordinal scale and is unknown—imperfect gold standard bias and ordinal scale symptom status. In this paper, we proposed a nonparametric maximum likelihood method for estimating and comparing the accuracy of different doctors in detecting a particular symptom without a gold standard when the true symptom status had an ordered multiple class. In addition, we extended the concept of the area under the receiver operating characteristic curve to a hyper-dimensional overall accuracy for diagnostic accuracy and alternative graphs for displaying a visual result. The simulation studies showed that the proposed method had good performance in terms of bias and mean squared error. Finally, we applied our method to our motivating example on assessing the diagnostic abilities of 5 TCM doctors in detecting symptoms related to Chills disease.