An analysis of research biopsy core variability from over 5000 prospectively collected core samples

AbstractFactors correlated with biopsy tissue adequacy and the prevalence of within-biopsy variability were evaluated. Totally, 1149 research biopsies were performed on 686 patients from which 5090 cores were assessed. Biopsy cores were reviewed for malignant percentage (estimated percentage of cells in the core that were malignant) and malignant area (estimated area occupied by malignant cells). Linear mixed models and generalized linear mixed models were used for the analysis. A total of 641 (55.8%) biopsies contained a core with <10% malignant percentage (inadequate core). The chance of an inadequate core was not influenced by core order, though the malignant area decreased with each consecutive core (p < 0.001). Younger age, bone biopsy location, appendiceal tumor pathology, and responding/stable disease prior to biopsy increased the odds of a biopsy containing zero adequate cores. Within-biopsy variability in core adequacy is prevalent and suggests the need for histological tumor quality assessment of each core in order to optimize translational analyses.

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l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

Statistical Methods in Medical Research ◽

10.1177/09622802211017583 ◽

2021 ◽

pp. 096228022110175

Author(s):

Jan P Burgard ◽

Joscha Krause ◽

Ralf Münnich ◽

Domingo Morales

Keyword(s):

Parameter Estimation ◽

Medical Treatment ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Linear Mixed Models ◽

Obesity Prevalence ◽

Model Parameter ◽

Model Parameter Estimation ◽

Public Health Reporting ◽

Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

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