Layman's abstract for paper on doubly robust estimation and causal inference for recurrent event data


  • Author: Chien‐Lin Su, Russell Steele and Ian Shrier
  • Date: 25 May 2020

Each week, we will be publishing layman's abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.

The article featured today is from Statistics in Medicine, with the full article now available to read in early view here.

thumbnail image: Layman's abstract for paper on doubly robust estimation and causal inference for recurrent event data

Su, C‐L, Steele, R, Shrier, I. Doubly robust estimation and causal inference for recurrent event data. Statistics in Medicine. 2020; 1– 15.

Recurrent event data commonly arise in studies from industry, public health and medicine where each individual may experience multiple episodes of the same type of event during the observed follow-up time. In a recurrent injury study for artists, for example, it is of interest to understand whether acrobat and non-acrobat groups have the same rates of recurrent injuries over time if all artists were in the acrobat group and in the non-acrobat group, respectively. However, it is not valid to compare these two groups when artists are not randomly assigned to either the acrobat or non-acrobat group, but instead the assignment depends on the artist-specific covariates such as age and gender.

This article proposes a novel statistical method for reducing confounding effects induced by covariates when analyzing recurrent event data. Using our approach, researchers can achieve three goals: 1) explore risk factors for the recurrent event of interest, 2) properly investigate the impact of risk factors for the group assignment, 3) estimate and compare the cumulative number of events for each group such as treated and untreated group at a given time point. The new methodology is illustrated with an application to a database of circus artist injuries.

Related Topics

Related Publications

Related Content

Site Footer


This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.