Theme: Data analysis (June 22, 2007). These categories cover a variety of methods for analyzing data from simple statistics to complex models.
Category: Adverse events in clinical trials. These pages discuss methods for monitoring the frequency of adverse events in a clinical trial or safety study.
Category: Analysis of means. Analysis of means (ANOM) is an approach used in quality control circles to compare averages, proportions, or counts across several groups.
Category: Analysis of variance. Analysis of variance (ANOVA) is an approach that allows you to compare a continuous outcome variable across a factor representing three or more groups and to examine interactions among factors.
Category: Bayesian statistics. In Bayesian statistics, the researcher specifies a probability distribution prior to the start of the experiment that represents his/her degree of belief about the possible values of a process being studied. After data is collected, the Bayesian analysis produces a posterior distribution that combines information from data with information from the prior distribution.
Category: Confidence intervals. A confidence interval provides a range of plausible values for an estimate that accounts for sampling error.
Category: Control charts. A control chart is a graphical tool used in many industrial settings that monitors a work process on a continual and on-going basis.
Category: Covariate adjustment. Covariate adjustment is the use of statistical methods (most notably analysis of covariance or ANCOVA) to correct for an imbalance in an important prognostic variable between a treatment/exposure group and a control group.
Category: Data mining. Data mining is a broad class of statistical tools that are designed for massive data sets. Many of the links in this category refer to methods for genetic data sets, especially microarray studies.
Category: Descriptive statistics. [incomplete] Descriptive statistics are statistics that are not used to test a formal research hypothesis, but rather to describe general features of a data set. I also use this category to represent very simple and fundamental issues in data analysis.
Category: Hypothesis testing. Hypothesis testing is a set of formal methods to select between two competing research hypotheses. These pages discuss some of the philosophical underpinnings for hypothesis testing as well as some pragmatic concerns.
Category: Information theory. These pages describe information theory, a branch of mathematics developed by Claude Shannon in the 1940's to model signals going through telephone lines. Information theory has found a diverse range of applications in areas like file compression and genetics.
Category: Linear regression. The linear regression model provides a framework for quantitative predictions of an outcome variable that is continuous, using one or more predictor variables.
Category: Logistic regression. The logistic regression model provides a framework for quantitative predictions of an outcome variable that is categorical, using one or more predictor variables.
Category: Measuring agreement. There are several ways to calculate the degree of agreement between two variables that are purporting to measure the same thing. In addition to describing these measures, this category includes discussion of assessment of reliability and validity, which is typically done by establishing a strong degree of agreement.
Category: Measuring benefit and risk. There are many measures of risk or benefit. I describe some of these (the odds ratio, the relative risk, the number needed to treat) and explain the advantages and disadvantages of particular measures.
Category: Mixed models. Mixed linear regression models, also known as random coefficient models extend the simple linear regression model to cases where you have to characterize variation between patients and within patients.
Category: Modeling issues. These pages discuss issues about statistical models which are relevant across a broad class of models. These pages may mention a specific model like logistic regression to provide context, but the ideas generalize easily to other models.
Category: Nonlinear regression. These pages describe regression models where you specify a nonlinear functional relationship.
Category: Poisson regression. Poisson regression is quite simply a regression model that assumes that the outcome variable follows a Poisson distribution. These regression models are commonly used to predict count or rate variables. These pages describe how Poisson regression works and some of the issues associated with these models.
Category: Probability concepts. These pages discuss some of the practical and theoretical considerations concerning probability.
Category: P-values. A p-value is a measure of evidence commonly used in hypothesis testing. These pages describe some of the controversies associated with the use of p-values.
Category: Qualitative data. These pages discuss some of the conceptual and logistical issues associated with the analysis of interviews, focus group data, and other sources of non-quantitative data.
Category: Quality control. These pages discuss some of the organizational and pragmatic issues associated with developing a quality control program.
Category: R software. These pages discuss how to program using R software, an open source package for statistical analysis.
Category: SPSS software. These pages describe how to use SPSS, a commercial statistical software program, to manage data and perform data analyses.
Category: Statistical computing. These pages describe the computational aspects of statistics.
Category: Statistical theory. These pages describe some of the more mathematical and/or technical aspects of Statistics.
Category: Survival analysis. Survival data represents data that indicates with information about the time to a certain event (often death, but it can represent other events as well). A common feature for most survival data is the process of censoring. These pages discuss the various ways you can analysis survival data.
Category: Unusual data. These pages describe data analysis that does not fit easily into the more traditional categories of data analysis. If I get a sufficient number of pages on the same general topic, I will create a new category.
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This webpage was written and was last modified on 07/08/2008.