Sparse data bias: a problem hiding in plain sight

This study aims to illustrate the problem of (Quasi) Complete Separation in the sparse data pattern occurring medical data. We presented the failure of traditional methods and then provided an overview of popular remedial approaches to reduce bias through vivid examples. Penalized maximum likelihood estimation and Bayesian methods are some remedial tools introduced to reduce bias. Data from the Tehran Thyroid and Pregnancy Study, a two-phase cohort study conducted from September 2013 through February 2016, was applied for illustration. The bias reduction of the estimate showed how sufficient these methods are compared to the traditional method. Extremely large measures of association such as the Risk ratios along with an extraordinarily wide range of confidence interval proved the traditional estimation methods futile in case of sparse data while it is still widely applying and reporting. In this review paper, we introduce some advanced methods such as data augmentation to provide unbiased estimations.

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Sparse-data bias accompanying overly fine stratification in an analysis of beryllium exposure and lung cancer risk

Annals of Epidemiology ◽

10.1016/j.annepidem.2012.11.005 ◽

2013 ◽

Vol 23 (2) ◽

pp. 43-48 ◽

Cited By ~ 4

Author(s):

Kenneth J. Rothman ◽

Paul L. Mosquin

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Lung Cancer Risk ◽

Sparse Data ◽

Sparse Data Bias

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Meta-Analysis and Sparse-Data Bias

American Journal of Epidemiology ◽

10.1093/aje/kwaa205 ◽

2020 ◽

Author(s):

David B Richardson ◽

Stephen R Cole ◽

Rachael K Ross ◽

Charles Poole ◽

Haitao Chu ◽

...

Keyword(s):

Logistic Regression ◽

Meta Analysis ◽

Sparse Data ◽

Maximum Likelihood Estimates ◽

Finite Sample ◽

Logistic Regression Models ◽

Confidence Interval Coverage ◽

The Individual ◽

Meta Analyses ◽

Sparse Data Bias

Abstract Meta-analyses are undertaken to combine information from a set of studies, often in settings where some of the individual study-specific estimates are based on relatively small study samples. Finite sample bias may occur when maximum likelihood estimates of associations are obtained by fitting logistic regression models to sparse data sets. Here we show that combining information from small studies by undertaking a meta-analytical summary of logistic regression estimates can propagate such sparse-data bias. In simulations, we illustrate 2 challenges encountered in meta-analyses of logistic regression results in settings of sparse data: 1) bias in the summary meta-analytical result and 2) confidence interval coverage that can worsen rather than improve, in terms of being less than nominal, as the number of studies in the meta-analysis increases.

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