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Related Experiment Video

Updated: Dec 26, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Exact conditional maximized sequential probability ratio test adjusted for covariates.

Ivair R Silva¹, Lingling Li², Martin Kulldorff^2,3

¹Department of Statistics, Federal University of Ouro Preto, Ouro Preto, MG, Brazil.

Sequential Analysis

|March 11, 2020

Summary

This summary is machine-generated.

This study provides exact critical values and power calculations for the conditional maximized sequential probability ratio test (CMaxSPRT). This enhances drug and vaccine safety surveillance by improving the detection of rare adverse events.

Keywords:

MaxSPRT Poisson distribution Type I error spending

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Published on: August 9, 2024

Area of Science:

Pharmacovigilance
Biostatistics
Clinical Trial Methodology

Background:

Sequential analysis is crucial for post-market drug and vaccine safety surveillance.
Poisson stochastic processes are standard for monitoring rare adverse events.
Uncertainty in expected counts under the null hypothesis necessitates advanced methods like CMaxSPRT.

Purpose of the Study:

To derive exact critical values for the conditional maximized sequential probability ratio test (CMaxSPRT).
To provide statistical power and expected time to signal calculations for CMaxSPRT.
To demonstrate covariate adjustment within sequential designs and illustrate the method with real-world vaccine safety data.

Main Methods:

Derivation of exact critical values for CMaxSPRT in continuous and group sequential analyses.
Calculation of statistical power and expected time to signal for various rejection boundaries.
Development of methods for covariate adjustment in sequential monitoring designs.

Main Results:

Exact critical values for CMaxSPRT are provided for selected parameters and rejection boundaries.
New functions in the R Sequential package facilitate further calculations.
The methodology is successfully illustrated using adverse event data from Pediarix vaccination.

Conclusions:

The derived exact critical values and power calculations enhance the reliability of CMaxSPRT for safety surveillance.
The study offers practical tools and guidance for implementing covariate-adjusted sequential monitoring.
This work improves the ability to detect rare adverse events efficiently in post-market settings.