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Updated: Jun 19, 2026

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

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

Update on statistical power and sample size assessments for cost-effectiveness studies.

Joseph C Gardiner¹, Corina M Sirbu, M Hossein Rahbar

¹Division of Biostatistics, Department of Epidemiology, Michigan State University, B629 West Fee Hall, East Lansing, MI 48824, USA. gardine3@msu.edu

Expert Review of Pharmacoeconomics & Outcomes Research

|October 8, 2009

Summary

Planning healthcare studies requires calculating sample size and statistical power to compare treatment effectiveness and cost-effectiveness. This review covers methodologies for designing studies that assess both dimensions for better resource allocation.

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Last Updated: Jun 19, 2026

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

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:

Health economics
Clinical trial design
Biostatistics

Background:

Healthcare interventions are traditionally compared on efficacy or effectiveness using clinical trials.
Rising healthcare costs necessitate economic evaluations to compare interventions based on cost alongside benefits.
Assessing both effectiveness and cost-effectiveness is crucial for informed healthcare decisions.

Purpose of the Study:

To review statistical methodologies for designing studies that assess both treatment effectiveness and cost-effectiveness.
To guide researchers in calculating sample size and statistical power for comparative effectiveness and cost-effectiveness studies.
To inform the planning of studies that evaluate healthcare interventions within budget constraints.

Main Methods:

Review of existing statistical methodologies for sample size and power calculations.
Examination of approaches for designing studies that simultaneously assess effectiveness and cost-effectiveness.
Discussion of methods applicable when one treatment's effectiveness is known versus when both effectiveness and cost-effectiveness are evaluated concurrently.

Main Results:

The design of cost-effectiveness studies requires careful consideration of statistical power and sample size for both effectiveness and economic endpoints.
Methodologies exist for studies where effectiveness is pre-determined and for those assessing both effectiveness and cost-effectiveness simultaneously.
Accurate sample size and power calculations are essential for robust study design in health economic evaluations.

Conclusions:

Effective planning of healthcare intervention studies necessitates integrating statistical power and sample size considerations for both clinical effectiveness and cost-effectiveness.
The reviewed methodologies provide a framework for designing studies that support evidence-based healthcare resource allocation.
Future research should continue to refine statistical approaches for comprehensive health economic evaluations.