Two‐stage randomized trial design for testing treatment, preference, and self‐selection effects for count outcomes

2020 ◽  
Vol 39 (25) ◽  
pp. 3653-3683
Author(s):  
Yu Shi ◽  
Briana Cameron ◽  
Xian Gu ◽  
Michael Kane ◽  
Peter Peduzzi ◽  
...  
2020 ◽  
Vol 102 (5) ◽  
pp. 839-851
Author(s):  
Hyuncheol Bryant Kim ◽  
Seonghoon Kim ◽  
Thomas T. Kim

We study how career and wage incentives affect labor productivity through self-selection and incentive effect channels using a two-stage field experiment in Malawi. First, recent secondary school graduates were hired with either career or wage incentives. After employment, half of the workers with career incentives randomly received wage incentives, and half of the workers with wage incentives randomly received career incentives. Career incentives attract higher-performing workers than wage incentives do, but they do not increase productivity conditional on selection. Wage incentives increase productivity for those recruited through career incentives. Observable characteristics are limited in explaining selection effects of entry-level workers.


2016 ◽  
Vol 27 (7) ◽  
pp. 2168-2184 ◽  
Author(s):  
Briana Cameron ◽  
Denise A Esserman

The two-stage (or doubly) randomized preference trial design is an important tool for researchers seeking to disentangle the role of patient treatment preference on treatment response through estimation of selection and preference effects. Up until now, these designs have been limited by their assumption of equal preference rates and effect sizes across the entire study population. We propose a stratified two-stage randomized trial design that addresses this limitation. We begin by deriving stratified test statistics for the treatment, preference, and selection effects. Next, we develop a sample size formula for the number of patients required to detect each effect. The properties of the model and the efficiency of the design are established using a series of simulation studies. We demonstrate the applicability of the design using a study of Hepatitis C treatment modality, specialty clinic versus mobile medical clinic. In this example, a stratified preference design (stratified by alcohol/drug use) may more closely capture the true distribution of patient preferences and allow for a more efficient design than a design which ignores these differences (unstratified version).


2019 ◽  
Vol 208 ◽  
pp. 11-20 ◽  
Author(s):  
Kristina Lambrakis ◽  
John K. French ◽  
Ian A. Scott ◽  
Tom Briffa ◽  
David Brieger ◽  
...  

2010 ◽  
Vol 63 (11) ◽  
pp. 1271-1275 ◽  
Author(s):  
Daniel L. Riddle ◽  
Robert E. Johnson ◽  
Mark P. Jensen ◽  
Francis J. Keefe ◽  
Kurt Kroenke ◽  
...  

2020 ◽  
Vol 72 (2) ◽  
pp. 771-772
Author(s):  
Graham R. McClure ◽  
William F. McIntyre ◽  
Richard P. Whitlock ◽  
Emilie P. Belley-Cote

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