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Comparing paired biomarkers in predicting quantitative health outcome subject to random censoring.

ABSTRACT: This paper uses a non-parametric test, based on consistently estimated discrimination accuracy defined as concordance probability between quantitative predictor and outcome, to compare paired biomarkers in predicting a health outcome, possibly subject to random censoring. Comparing with the Wilcoxon test for paired predictors based on Harrell's C-index, we found that the proposed test is better in presence of random censoring, although the two unbiased tests are equivalent for outcome either uncensored or censored by a constant. A simulation study also demonstrates that the bias in estimated difference in concordance probability, due to ignoring random censoring, results in overestimated power, especially when random censoring is heavy. The method was applied in two studies, where the biomarkers measured from the same study subjects are correlated. The first study on 299 school children in Bangladesh found the associations that higher blood arsenic and manganese were related to lower intellectual test scores, while the differences between the biomarkers in predicting the intellectual test scores were not statistically significant. The second study on 418 patients with primary biliary cirrhosis found that the baseline serum bilirubin had greater discrimination accuracy than the baseline serum albumin in predicting survival time.

SUBMITTER: Liu X

PROVIDER: S-EPMC4390496 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Comparing paired biomarkers in predicting quantitative health outcome subject to random censoring.

Liu Xinhua X Jin Zhezhen Z Graziano Joseph H JH

Statistical methods in medical research 20121014 1

This paper uses a non-parametric test, based on consistently estimated discrimination accuracy defined as concordance probability between quantitative predictor and outcome, to compare paired biomarkers in predicting a health outcome, possibly subject to random censoring. Comparing with the Wilcoxon test for paired predictors based on Harrell's C-index, we found that the proposed test is better in presence of random censoring, although the two unbiased tests are equivalent for outcome either unc ...[more]

PMID: 23070589

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Project description:BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients.MethodsThe clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients' clinical prognoses.ResultsSeven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42-16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13-11.25]).ConclusionWe applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk factors for the prognoses of COVID-19 patients in the early stage of the disease.

Project description:BackgroundThis scoping review reports on studies that collect survey data using quantitative research to measure self-reported oral health status outcome measures. The objective of this review is to categorize measures used to evaluate self-reported oral health status and oral health quality of life used in surveys of general populations.MethodsThe review is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) with the search on four online bibliographic databases. The criteria include (1) peer-reviewed articles, (2) papers published between 2011 and 2021, (3) only studies using quantitative methods, and (4) containing outcome measures of self-assessed oral health status, and/or oral health-related quality of life. All survey data collection methods are assessed and papers whose methods employ newer technological approaches are also identified.ResultsOf the 2981 unduplicated papers, 239 meet the eligibility criteria. Half of the papers use impact scores such as the OHIP-14; 10% use functional measures, such as the GOHAI, and 26% use two or more measures while 8% use rating scales of oral health status. The review identifies four data collection methods: in-person, mail-in, Internet-based, and telephone surveys. Most (86%) employ in-person surveys, and 39% are conducted in Asia-Pacific and Middle East countries with 8% in North America. Sixty-six percent of the studies recruit participants directly from clinics and schools, where the surveys were carried out. The top three sampling methods are convenience sampling (52%), simple random sampling (12%), and stratified sampling (12%). Among the four data collection methods, in-person surveys have the highest response rate (91%), while the lowest response rate occurs in Internet-based surveys (37%). Telephone surveys are used to cover a wider population compared to other data collection methods. There are two noteworthy approaches: 1) sample selection where researchers employ different platforms to access subjects, and 2) mode of interaction with subjects, with the use of computers to collect self-reported data.ConclusionThe study provides an assessment of oral health outcome measures, including subject-reported oral health status and notes newly emerging computer technological approaches recently used in surveys conducted on general populations. These newer applications, though rarely used, hold promise for both researchers and the various populations that use or need oral health care.

Project description:About 20-40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value?<?0.0001) and overall survival (p?=?0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.

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Comparing paired biomarkers in predicting quantitative health outcome subject to random censoring.

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Comparing paired biomarkers in predicting quantitative health outcome subject to random censoring.

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