## KEY CONCEPTS

KEY CONCEPTS

• The choice of statistical methods depends on the research question, the scales on which the variables are measured, and the number of variables to be analyzed.

• Many of the advanced statistical procedures can be interpreted as an extension or modification of multiple regression analysis.

• Many of the statistical methods used for questions with one independent variable have direct analogies with methods for multiple independent variables.

• The term “multivariate” is used when more than one independent variable is analyzed.

• Multiple regression is a simple and ideal method to control for confounding variables.

• Multiple regression coefficients indicate whether the relationship between the independent and dependent variables is positive or negative.

• Dummy, or indicator, coding is used when nominal variables are used in multiple regression.

• Regression coefficients indicate the amount the change in the dependent variable for each one-unit change in the X variable, holding other independent variables constant.

• Multiple regression measures a linear relationship only.

• The Multiple R statistic is the best indicator of how well the model fits the data—how much variance is accounted for by the model.

• Several methods can be used to select variables in a multivariate regression.

• Polynomial regression can be used when the relationship is curvilinear.

• Cross-validation tell us how applicable the model will be if we used it in another sample of subjects.

• A good rule of thumb is to have ten times as many subjects as variables.

• Analysis of covariance controls for confounding variables; it can be used as part of analysis of variance or in multiple regression.

• Logistic regression predicts a nominal outcome; it is the most widely used regression method in medicine.

• The regression coefficients in logistic regression can be transformed to give odds ratios.

• The Cox model is the multivariate analogue of the Kaplan–Meier curve; it predicts time-dependent outcomes when there are censored observations.

• The Cox model is also called the proportional hazard model; it is one of the most important statistical methods in medicine.

• Meta-analysis provides a way to combine the results from several studies in a quantitative way and is especially useful when studies have come to opposite conclusions or are based on small samples.

• An effect size is a measure of the magnitude of differences between two groups; it is a useful concept in estimating sample sizes.

• The Cochrane Collection is a set of very well-designed meta-analyses and is available at libraries and online.

• Several methods are available when the goal is to classify subjects into groups.

• Multivariate analysis of variance, or MANOVA, is analogous to using ANOVA when there are several dependent variables.

## PRESENTING PROBLEMS

### Presenting Problem 1

In Chapter 8, we examined the study by Neidert and colleagues (2016), studying the relationship between body composition measurements: plasma DPP-IV activity, gynoid fat, BMI, and lean mass.