Chapter 2 Scope of the regression analyses for the examples

Regression models can be used for a wide range of purposes, for the purpose of these examples the assumptions on the regression analysis set-up in this paper are listed in Table 1. Thus, IDA tasks will be explained in a well-defined, practically relevant setting. Since a key principle is that IDA does not touch the research question no associations between dependent (outcome) and independent (non-outcome) variables are considered.

Table 1: The scope of the regression analyses considered for IDA tasks

Aspects of the research plan	Assumptions in this paper	Reason for the assumption
Dependent (outcome) variable	One dependent variable that can be continuous or binary; exclude time-to-event or longitudinal outcomes	Explain IDA tasks in a well-defined, practically relevant setting
Regression models	Models with linear predictors	Explain IDA tasks in a well-defined, practically relevant setting
Purpose of regression model	Adjust effect of one variable of interest for confounders; quantify the effects of explanatory variables on the outcome	Explain IDA tasks in a well-defined, practically relevant setting
Independent variables	“explanatory” or “confounder” depending on purpose of model; small to moderate number of mixed types; Not high dimensional; no repeated measurements	To demonstrate IDA approaches for a mix of variables likely to be encountered in practice
Statistical analysis plan	Exists, defines the outcome variable, the type of regression model to be used, and a set of independent variables	IDA does not touch the research question, but may lead to an update or refinement of the analysis plan

References:

Vach W. Regression Models as a Tool in Medical Research. Chapman/Hall CRC 2012

Harrell FE. Regression Modeling Strategies. Springer (2nd ed) 2015

Royston P and Sauerbrei W. Multivariable Model Building. Wiley (2008)

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