Workshop on sample selection modelling for missingness not at random using GJRM

Summary of workshop:

The workshop provides an introduction to flexible regression models available in the R package GJRM with a particular focus on sample selection modelling. GJRM will be illustrated on several examples, affected by sample selection bias, in the fields of epidemiology, health economics and marketing.

Provisional timetable:

09:00-09:20 - Registration and coffee
09:20-09:30 - Welcome and introduction
09:30-10:20 - Overview on GJRM
10:20-11:10 - Sample selection models using GJRM
11:10-11:30 - Coffee
11:30-12:20 - Examples of sample selection bias in epidemiology and health economics
12:20-13:10 - Case study on marketing research
13:10-13:20 - Conclusions
13:20-14:00 - Lunch

Who will benefit and how:

This workshop provides a practical introduction to a general class of bivariate flexible regression models including sample selection models that can be useful to analysts and quantitative researchers whose analyses are affected by sample selection bias (or bias due to missing not at random) which can occur in several contexts (e.g., marketing, epidemiology, health economics). The workshop will also be relevant for practitioners conducting surveys on topics which are likely to be affected by missing information.

Prerequisites:

Participants are expected to have some familiarity with R, as well as relevant statistical concepts such as linear regression.

Presenters:

Rosalba Radice (Bayes Business School, Faculty of Actuarial Science and Insurance)
Giampiero Marra (University College London, Department of Statistical Science)
Nadine Schröder (Vienna University of Economics and Business, Institute for Marketing and Customer Analytics)