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)
[…]