TY - JOUR
T1 - Recurrent individual treatment assignment
T2 - a treatment policy approach to account for heterogeneous treatment effects
AU - Cornelisz, Ilja
AU - van Klaveren, Chris
N1 - data source: Simulated Data (Available on Request)
PY - 2022/2/4
Y1 - 2022/2/4
N2 - Longitudinal randomized controlled trials generally assign individuals randomly to interventions at baseline and then evaluate how differential average treatment effects evolve over time. This study shows that longitudinal settings could benefit from Recurrent Individual Treatment Assignment (RITA) instead, particularly in the face of (dynamic) heterogeneous treatment effects. Focusing on the optimization of treatment assignment, rather than on estimating treatment effects, acknowledges the presence of unobserved heterogeneous treatment effects and improves overall intervention response when compared to intervention policies in longitudinal settings based on Randomized Controlled Trials (RCTs)-derived average treatment effects. This study develops a RITA-algorithm and evaluates its performance in a multi-period simulation setting, considering two alternative interventions and varying the extent of unobserved heterogeneity in individual treatment response. The results show that RITA learns quickly, and adapts individual assignments effectively. If treatment heterogeneity exists, the inherent focus on both exploit and explore enables RITA to outperform a conventional assignment strategy that relies on RCT-derived average treatment effects.
AB - Longitudinal randomized controlled trials generally assign individuals randomly to interventions at baseline and then evaluate how differential average treatment effects evolve over time. This study shows that longitudinal settings could benefit from Recurrent Individual Treatment Assignment (RITA) instead, particularly in the face of (dynamic) heterogeneous treatment effects. Focusing on the optimization of treatment assignment, rather than on estimating treatment effects, acknowledges the presence of unobserved heterogeneous treatment effects and improves overall intervention response when compared to intervention policies in longitudinal settings based on Randomized Controlled Trials (RCTs)-derived average treatment effects. This study develops a RITA-algorithm and evaluates its performance in a multi-period simulation setting, considering two alternative interventions and varying the extent of unobserved heterogeneity in individual treatment response. The results show that RITA learns quickly, and adapts individual assignments effectively. If treatment heterogeneity exists, the inherent focus on both exploit and explore enables RITA to outperform a conventional assignment strategy that relies on RCT-derived average treatment effects.
U2 - 10.1038/s41539-021-00117-4
DO - 10.1038/s41539-021-00117-4
M3 - Article
C2 - 35121772
SN - 2056-7936
VL - 7
JO - npj Science of Learning
JF - npj Science of Learning
IS - 1
M1 - 3
ER -