On the analysis of sequential designs without a specified number of observations

TL;DR

This paper analyzes sequential experimental designs with variable total observations, focusing on their distributional properties and modeling using staged trees in algebraic statistics, supported by theoretical and real data analysis.

Contribution

It introduces a new parameterization and model class for analyzing data from sequential experiments without fixed sample sizes, using staged trees in algebraic statistics.

Findings

Distributional assumptions are clarified for such experiments.

Properties of parameter estimates and test statistics are derived.

Illustrations include hypothetical and real data applications.

Abstract

The paper focuses on sequential experiments for categorical responses in which whether or not a further observation is made depends on the outcome of a previous experiment. Examples include subsequent medical interventions being performed or not depending on the result of a previous intervention, data about offsprings, life tables, and repeated educational retraininig until a certain proficiency level is achieved. Such experiments do not lead to data with a full Cartesian product structure and, despite a prespecified initial sample size, the total number of observations, or interventions, made cannot be determined in advance. The paper investigates the distributional assumptions behind such data and describes a parameterization of the distribution that arises and the respective model class to analyze it. Both the data structure resulting from such an experiment and the model class are special examples of staged trees in algebraic statistics. The properties of the resulting parameter estimates and test statistics are obtained and illustrated using hypothetical and real data.