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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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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...
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Censoring Survival Data01:09

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple

Sören Budig1, Klaus Jung2, Mario Hasler3

  • 1Department of Biostatistics, Institute of Cell Biology and Biophysics, Leibniz University Hannover, Hannover, Germany.

Biometrical Journal. Biometrische Zeitschrift
|July 2, 2024
PubMed
Summary
This summary is machine-generated.

This study compares methods for analyzing multiple binary outcomes in biomedical research. A resampling approach offers better statistical power while controlling errors, outperforming multiple marginal models and Bonferroni correction.

Keywords:
bootstrapfamily‐wise error rategeneralized linear modelsmultiple comparisonspower

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Area of Science:

  • Biostatistics
  • Biomedical Research Methodology

Background:

  • Simultaneous inference for multiple binary endpoints is crucial in biomedical research.
  • Controlling the family-wise error rate (FWER) is essential to avoid incorrect test decisions.

Purpose of the Study:

  • To investigate and compare single-step p-value adjustment methods that account for endpoint correlation.
  • To evaluate the performance of multiple marginal models and a vector-based resampling approach against the Bonferroni method.

Main Methods:

  • Single-step p-value adjustment methods were investigated.
  • Multiple marginal models based on stacked parameter estimates and their joint asymptotic distribution were used.
  • A nonparametric vector-based resampling approach was employed.
  • Comparison involved assessing family-wise error rate and power under various parameter settings, including low proportions and small sample sizes.

Main Results:

  • The resampling-based approach demonstrated superior power while maintaining family-wise error rate control.
  • The multiple marginal models approach exhibited more conservative behavior but offered greater flexibility.
  • Both methods were compared to the traditional Bonferroni method.

Conclusions:

  • The resampling approach is recommended for its balance of power and error control in analyzing multiple binary endpoints.
  • Multiple marginal models provide a versatile alternative, particularly for complex analyses and simultaneous confidence intervals.
  • Both novel methods were validated using a toxicological dataset from the National Toxicology Program.