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PubMed1999

The future of physician profiling.

Although enthusiasm for physician profiling dates back to the origins of scientific medicine, the track record of profiling as an intervention is not strong. Profiling appears to be most valuable for simple interventions and processes, but its acceptance, usefulness, and impact will be influenced by future trends. Health care organizations will continue to be complex, and multiple overlapping profiles will be the norm. Technology will advance, but concerns over privacy and confidentiality will influence the social, political, and regulatory approaches to profiling. Public sector purchasers and consumers will increasingly demand profiling, but data will continue to lack the ability to interpret complex profiling data. Medical practice will increasingly accept profiling as part of ongoing quality improvement efforts, but profiling may become focused on key processes, not outcomes. Statistical, cognitive, and epistemological challenges to profiling will remain, and profiling may simultaneously become more complex and more simplified with the rise of information brokers. Greater attention to the human side of profiling will enhance its effectiveness.

Data Collection↗
PubMed2001

[DNA chip data mining].

DNA chip data routinely contain gene expression levels of thousands of genes and the analysis should be supported by various computational tools. To be brief, the analysis procedure consists of four steps including image scanning, image processing, mathematical interpretation and biological interpretation. In image processing step, we should detect the spots and measure the signals of the spots and the background. In mathematical interpretation step, first of all we should massage the measured signals to make them appropriate for further mathematical analysis. The massaged data could be analyzed by various computational methods especially when the data were generated for multiple samples comparisons. The clustering techniques including hierarchical clustering, k-means clustering, SOTA, SOM are the most popular methods in this step. Various other multivariate statistics and related machine learning techniques are being introduced and applied to DNA chip data analysis recently. And finally the most important step we should tackle is the biological interpretation task. Although the depth of the domain knowledge about the biological situation under which the data were generated is the most important factor to elucidate the biological context, it could be supported by various bioinformatics tools including MEDLINE abstract processing by NLP techniques or genetic network models constructed by Boolean networks algorithms.

Algorithms↗
PubMed1989

Meta-analysis and evidence.

Meta-analysis is the science of combining evidence from different studies, but traditional statistical techniques contain neither a formal definition nor a measure of evidence. It is argued in this paper that the log-likelihood ratio, as a measure of the "weight of evidence," can be a very useful tool in the meta-analysis. The mathematics and the philosophy behind the use of this index are introduced. The construction and interpretation of "support curves" in fixed and random-effects models are presented. The application of evidential techniques is illustrated on six trials of aspirin therapy previously presented by Canner. The possible dangers of focusing on statistical error rates instead of evidence are discussed.

Aspirin↗
PubMed2001

Is there a meaningful definition of the value of a statistical life?

A definition of the value of a statistical life is derived. This definition has a meaningful interpretation in terms of the monetary value of expected present value utility if consumption is age-independent. In all other cases, empirical estimates of the value of a statistical life are biased estimators of the monetary counterpart to expected present value utility.

Data Interpretation, Statistical↗
PubMedDate not supplied

Use of the score test as a goodness-of-fit measure of the covariance structure in genetic analysis of longitudinal data.

Model selection is an essential issue in longitudinal data analysis since many different models have been proposed to fit the covariance structure. The likelihood criterion is commonly used and allows to compare the fit of alternative models. Its value does not reflect, however, the potential improvement that can still be reached in fitting the data unless a reference model with the actual covariance structure is available. The score test approach does not require the knowledge of a reference model, and the score statistic has a meaningful interpretation in itself as a goodness-of-fit measure. The aim of this paper was to show how the score statistic may be separated into the genetic and environmental parts, which is difficult with the likelihood criterion, and how it can be used to check parametric assumptions made on variance and correlation parameters. Selection of models for genetic analysis was applied to a dairy cattle example for milk production.

Animals↗
PubMed2002

Statistics review 3: hypothesis testing and P values.

The present review introduces the general philosophy behind hypothesis (significance) testing and calculation of P values. Guidelines for the interpretation of P values are also provided in the context of a published example, along with some of the common pitfalls. Examples of specific statistical tests will be covered in future reviews.

Clinical Trials as Topic↗