Dataset Information

Partial summary measures of the predictiveness curve.

ABSTRACT: In the evaluation of a biomarker for risk prediction, one can assess the performance of the biomarker in the population of interest by displaying the predictiveness curve. In conjunction with an assessment of the classification accuracy of a biomarker, the predictiveness curve is an important tool for assessing the usefulness of a risk prediction model. Inference for a single biomarker or for multiple biomarkers can be performed using summary measures of the predictiveness curve. We propose two partial summary measures, the partial total gain and the partial proportion of explained variation, that summarize the predictiveness curve over a restricted range of risk. The methods we describe can be used to compare two biomarkers when there are existing thresholds for risk stratification. We describe inferential tools for one and two samples that are shown to have adequate power in a simulation study. The methods are illustrated by assessing the accuracy of a risk score for predicting the onset of Alzheimer's disease.

SUBMITTER: Sachs MC

PROVIDER: S-EPMC3806283 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Partial summary measures of the predictiveness curve.

Sachs Michael C MC Zhou Xiao-Hua XH

Biometrical journal. Biometrische Zeitschrift 20130318 4

In the evaluation of a biomarker for risk prediction, one can assess the performance of the biomarker in the population of interest by displaying the predictiveness curve. In conjunction with an assessment of the classification accuracy of a biomarker, the predictiveness curve is an important tool for assessing the usefulness of a risk prediction model. Inference for a single biomarker or for multiple biomarkers can be performed using summary measures of the predictiveness curve. We propose two ...[more]

PMID: 23508801

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Project description:BACKGROUND: Summary measures of health that combine mortality and morbidity into a single indicator are being estimated in the Canadian context for approximately 200 diseases and conditions. To manage the large amount of data and calculations for this many diseases, we have developed a structured workbook system with easy to use tools. We expect this system will be attractive to researchers from other countries or regions of Canada who are interested in estimating the health-adjusted life years (HALYs) lost to premature mortality and year-equivalents lost to reduced functioning, as well as population attributable fractions (PAFs) associated with risk factors. This paper describes the workbook system using cancers as an example, and includes the entire system as a free, downloadable package. METHODS: The workbook system was developed in Excel and runs on a personal computer. It is a database system that stores data on population structure, mortality, incidence, distributions of cases entering a multitude of health states, durations of time spent in health states, preference scores that weight for severity, life table estimates of life expectancies, and risk factor prevalence and relative risks. The tools are Excel files with embedded macro programs. The main tool generates workbooks that estimate HALY, one per disease, by copying data from the database into a pre-defined template. Other tools summarize the HALY results across diseases for easy analysis. RESULTS: The downloadable zip file contains the database files initialized with Canadian data for cancers, the tools, templates and workbooks that estimate PAF and a user guide. The workbooks that estimate HALY are generated from the system at a rate of approximately one minute per disease. The resulting workbooks are self-contained and can be used directly to explore the details of a particular disease. Results can be discounted at different rates through simple parameter modification. CONCLUSION: The structured workbook approach offers researchers an efficient, easy to use, and easy to understand set of tools for estimating HALY and PAF summary measures for their country or region of interest.

Project description:BackgroundUnicompartmental knee arthroplasty (UKA) procedures are considered to be more technically demanding than conventional total knee arthroplasty (TKA), requiring a longer learning curve and more expert surgical skills. Despite some clear advantages of UKA over TKA (such as lesser blood loss, greater bone stock, greater knee performances, etc.), UKA evidenced a greater rate of revision.ObjectThis study investigated the learning curve of Persona Partial Knee (PPK) arthroplasty for primary medial UKA performed by a single, non-designer surgeon. PPK is a fixed-bearing, compartment-specific implant. The primary outcome of interest for this study was to evaluate the learning curve of the surgical duration. The secondary outcome of interest was to evaluate the learning curve of radiological implant positioning.MethodsPatients who underwent primary medial UKA using PPK (Zimmer-Biomet, Warsaw IN, USA) were prospectively enrolled for the study. All surgeries were performed by a single, non-designer surgeon experienced in knee and hip arthroplasty. The primary outcome of interest was to evaluate the surgical duration. The secondary outcome of interest was to evaluate the implant positioning. The learning curve was estimated using an appropriate nonlinear polynomial regression model with a lower Akaike Information Criterion (AIC).ResultsOne hundred twenty five patients were enrolled in the study. 59% of them (74 of 125 patients) were women. The patients' mean age at the time of surgery was 70.1 ± 9.5 years and their mean body mass index (BMI) was 27.8 ± 4.2 kg/m2. Curve stabilisation of the surgical time was at the 94th patient, of the tibial angle at the 47th patient, of the tibial slope at the 54th patient, of the anterior protrusion at the 29th patient, and of the posterior protrusion at the 51st patient.ConclusionsThe learning curve for component positioning was achieved in approximately 50 cases. The curve of the surgical time achieved a plateau at 94 Persona Partial Knee. Additionally, the factors directly correlated with earlier stabilization of the learning curve in terms of component positioning were: male gender, younger age, right side, and larger components.

Dataset Information

Partial summary measures of the predictiveness curve.

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Partial summary measures of the predictiveness curve.

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