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Related Concept Videos

Hypoglycemia01:26

Hypoglycemia

Hypoglycemia is a blood glucose level below 70 mg/dL. It commonly occurs in individuals using insulin or insulin-secreting drugs, but may also arise in non-diabetic conditions. People with type 1 diabetes are at the highest risk because they depend on exogenous insulin. People with type 2 diabetes are also at risk, especially when treated with insulin or medications such as sulfonylureas, which increase insulin release regardless of blood glucose levels. It develops when insulin levels exceed...
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Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...
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Hyperglycemia01:29

Hyperglycemia

Hyperglycemia is an abnormally high blood glucose level. It is diagnosed by fasting glucose ≥126 mg/dL, 2-hour oral glucose tolerance test (or OGTT) ≥200 mg/dL, random glucose ≥200 mg/dL with symptoms, or HbA1c ≥6.5%. However, HbA1c results may be unreliable in certain conditions, such as anemia or hemoglobinopathies, and the diagnosis should be confirmed unless classic symptoms are present. Postprandial hyperglycemia is typically considered significant when glucose levels exceed 180 mg/dL two...
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
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The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
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The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Analysis of hypoglycemic events using negative binomial models.

Junxiang Luo1, Yongming Qu

  • 1Department of Global Statistical Science, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, Indiana 46285, USA. luoju@lilly.com

Pharmaceutical Statistics
|June 19, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a robust Negative Binomial-Sandwich-Pearson (NBSP) regression model for analyzing diabetes hypoglycemia events. NBSP improves estimation efficiency and is resilient to model misspecification, outperforming standard methods.

Keywords:
hypoglycemianegative binomialoverdispersion

Related Experiment Videos

Last Updated: May 10, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Diabetes Research

Background:

  • Negative binomial regression is standard for analyzing hypoglycemic events in diabetes trials.
  • Adjusting for covariates can improve efficiency but risks model misspecification.
  • Existing methods for standard error correction have limitations.

Purpose of the Study:

  • To propose and evaluate a robust Negative Binomial-Sandwich-Pearson (NBSP) regression model.
  • To assess the robustness and efficiency of NBSP compared to standard negative binomial models.
  • To determine if adjusting for baseline hypoglycemia improves estimation.

Main Methods:

  • Developed the Negative Binomial-Sandwich-Pearson (NBSP) estimation method.
  • Calculated the covariance matrix using Sandwich estimation with Pearson overdispersion correction.
  • Compared NBSP with common negative binomial models via simulations and real data analysis.

Main Results:

  • NBSP demonstrated superior robustness against model misspecification.
  • The proposed NBSP method showed improved estimation efficiency.
  • Adjusting for baseline hypoglycemia enhanced the estimation efficiency.

Conclusions:

  • NBSP is a robust and efficient alternative for analyzing hypoglycemic events in diabetes clinical trials.
  • The method effectively handles potential model misspecification issues.
  • Incorporating baseline covariates is beneficial for improving estimation accuracy.