The Role of Pairwise Matching in Experimental Design for an Incidence Outcome

09/01/2022
by   Adam Kapelner, et al.
0

We consider the problem of evaluating designs for a two-arm randomized experiment with an incidence (binary) outcome under a nonparametric general response model. Our two main results are that the priori pair matching design of Greevy et al. (2004) is (1) the optimal design as measured by mean squared error among all block designs which includes complete randomization. And (2), this pair-matching design is minimax, i.e. it provides the lowest mean squared error under an adversarial response model. Theoretical results are supported by simulations and clinical trial data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/04/2022

The Optimality of Blocking Designs in Experiments with General Response

We consider the problem of evaluating designs for a two-arm randomized e...
research
07/13/2018

Optimal designs for frequentist model averaging

We consider the problem of designing experiments for the estimation of a...
research
06/26/2020

Properties of restricted randomization with implications for experimental design

Recently, there as been an increasing interest in the use of heavily res...
research
12/06/2020

Better Experimental Design by Hybridizing Binary Matching with Imbalance Optimization

We present a new experimental design procedure that divides a set of exp...
research
08/12/2019

Prediction in regression models with continuous observations

We consider the problem of predicting values of a random process or fiel...
research
05/06/2020

On the Optimality of Randomization in Experimental Design: How to Randomize for Minimax Variance and Design-Based Inference

I study the minimax-optimal design for a two-arm controlled experiment w...
research
03/03/2022

Online Balanced Experimental Design

e consider the experimental design problem in an online environment, an ...

Please sign up or login with your details

Forgot password? Click here to reset