A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. Meta-analytic results are considered the most trustworthy source of evidence by the evidence-based medicine literature.
Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem, a model is usually given a dataset of known data on which training is run, and a dataset of unknown data against which the model is tested. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset.
Quantitative structure–activity relationship models are regression or classification models used in the chemical and biological sciences and engineering. Like other regression models, QSAR regression models relate a set of "predictor" variables (X) to the potency of the response variable (Y), while classification QSAR models relate the predictor variables to a categorical value of the response variable.
Kazem Sadegh-Zadeh is a German analytic philosopher of medicine of Iranian descent. He was the first ever professor of philosophy of medicine at a German university and has made significant contributions to the philosophy, methodology, and logic of medicine since 1970.
In statistics, unit-weighted regression is a simplified and robust version of multiple regression analysis where only the intercept term is estimated. That is, it fits a model
In statistics, resampling is the creation of new samples based on one observed sample. Resampling methods are:
- Permutation tests
- Bootstrapping
- Cross validation
MedCalc is a statistical software package designed for the biomedical sciences. It has an integrated spreadsheet for data input and can import files in several formats.
Recursive partitioning is a statistical method for multivariable analysis. Recursive partitioning creates a decision tree that strives to correctly classify members of the population by splitting it into sub-populations based on several dichotomous independent variables. The process is termed recursive because each sub-population may in turn be split an indefinite number of times until the splitting process terminates after a particular stopping criterion is reached.
A clinical prediction rule or clinical probability assessment specifies how to use medical signs, symptoms, and other findings to estimate the probability of a specific disease or clinical outcome.
Robyn Mason Dawes was an American psychologist who specialized in the field of human judgment. His research interests included human irrationality, human cooperation, intuitive expertise, and the United States AIDS policy. He applied linear models to human decision making, including models with equal weights, a method known as unit-weighted regression. He co-wrote an early textbook on mathematical psychology.
In statistics, regression validation is the process of deciding whether the numerical results quantifying hypothesized relationships between variables, obtained from regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression, analyzing whether the regression residuals are random, and checking whether the model's predictive performance deteriorates substantially when applied to data that were not used in model estimation.
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
David M. Eddy is an American physician, mathematician, and healthcare analyst who has done seminal work in mathematical modeling of diseases, clinical practice guidelines, and evidence-based medicine. Four highlights of his career have been summarized by the Institute of Medicine of the National Academy of Sciences: "more than 25 years ago, Eddy wrote the seminal paper on the role of guidelines in medical decision-making, the first Markov model applied to clinical problems, and the original criteria for coverage decisions; he was the first to use and publish the term 'evidence-based'."
In silico medicine is the application of in silico research to problems involving health and medicine. It is the direct use of computer simulation in the diagnosis, treatment, or prevention of a disease. More specifically, in silico medicine is characterized by modeling, simulation, and visualization of biological and medical processes in computers with the goal of simulating real biological processes in a virtual environment.
Jacqueline Meulman is a Dutch statistician and professor emerita of Applied Statistics at the Mathematical Institute of Leiden University.
In statistics, the one in ten rule is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis while keeping the risk of overfitting low. The rule states that one predictive variable can be studied for every ten events. For logistic regression the number of events is given by the size of the smallest of the outcome categories, and for survival analysis it is given by the number of uncensored events.
The Center for Advanced Life Cycle Engineering (CALCE) is a university research facility focused on risk assessment, management, and mitigation for electronic products and systems. CALCE is the largest electronic products and systems research center focused on electronics reliability and is dedicated to providing a knowledge and resource base to support the development of competitive electronic components, products, and systems. CALCE is located at the University of Maryland in College Park, Maryland, and was founded by Professor Michael Pecht.
Thomas G. McGinn is an American physician and researcher in clinical decision support. McGinn is the David J. Greene Professor of Medicine at Hofstra Northwell School of Medicine and currently Chair of Department of Medicine.
Decision curve analysis evaluates a predictor for an event as a probability threshold is varied, typically by showing a graphical plot of net benefit against threshold probability. By convention, the default strategies of assuming that all or no observations are positive are also plotted.
Laure Wynants is a Belgian epidemiologist who is a professor at Maastricht University. She studies prediction models in medicine and hospital acquired infections.
