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Properties of Estimators in Exponential Family Settings with Observation-based Stopping Rules.

Publication ,  Journal Article
Milanzi, E; Molenberghs, G; Alonso, A; Kenward, MG; Verbeke, G; Tsiatis, AA; Davidian, M
Published in: J Biom Biostat
February 2016

Often, sample size is not fixed by design. A key example is a sequential trial with a stopping rule, where stopping is based on what has been observed at an interim look. While such designs are used for time and cost efficiency, and hypothesis testing theory has been well developed, estimation following a sequential trial is a challenging, still controversial problem. Progress has been made in the literature, predominantly for normal outcomes and/or for a deterministic stopping rule. Here, we place these settings in a broader context of outcomes following an exponential family distribution and, with a stochastic stopping rule that includes a deterministic rule and completely random sample size as special cases. It is shown that the estimation problem is usually simpler than often thought. In particular, it is established that the ordinary sample average is a very sensible choice, contrary to commonly encountered statements. We study (1) The so-called incompleteness property of the sufficient statistics, (2) A general class of linear estimators, and (3) Joint and conditional likelihood estimation. Apart from the general exponential family setting, normal and binary outcomes are considered as key examples. While our results hold for a general number of looks, for ease of exposition, we focus on the simple yet generic setting of two possible sample sizes, N=n or N=2n.

Duke Scholars

Published In

J Biom Biostat

DOI

ISSN

2155-6180

Publication Date

February 2016

Volume

7

Issue

1

Location

United States
 

Citation

APA
Chicago
ICMJE
MLA
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Milanzi, E., Molenberghs, G., Alonso, A., Kenward, M. G., Verbeke, G., Tsiatis, A. A., & Davidian, M. (2016). Properties of Estimators in Exponential Family Settings with Observation-based Stopping Rules. J Biom Biostat, 7(1). https://doi.org/10.4172/2155-6180.1000272
Milanzi, Elasma, Geert Molenberghs, Ariel Alonso, Michael G. Kenward, Geert Verbeke, Anastasios A. Tsiatis, and Marie Davidian. “Properties of Estimators in Exponential Family Settings with Observation-based Stopping Rules.J Biom Biostat 7, no. 1 (February 2016). https://doi.org/10.4172/2155-6180.1000272.
Milanzi E, Molenberghs G, Alonso A, Kenward MG, Verbeke G, Tsiatis AA, et al. Properties of Estimators in Exponential Family Settings with Observation-based Stopping Rules. J Biom Biostat. 2016 Feb;7(1).
Milanzi, Elasma, et al. “Properties of Estimators in Exponential Family Settings with Observation-based Stopping Rules.J Biom Biostat, vol. 7, no. 1, Feb. 2016. Pubmed, doi:10.4172/2155-6180.1000272.
Milanzi E, Molenberghs G, Alonso A, Kenward MG, Verbeke G, Tsiatis AA, Davidian M. Properties of Estimators in Exponential Family Settings with Observation-based Stopping Rules. J Biom Biostat. 2016 Feb;7(1).

Published In

J Biom Biostat

DOI

ISSN

2155-6180

Publication Date

February 2016

Volume

7

Issue

1

Location

United States