SSEv: A New Small Samples Evaluator Based on Modified Survival Curves.

Area of Science:

• Biostatistics
• Rare Disease Research
• Translational Medicine

Background:

• Rare diseases present significant research challenges due to limited patient populations and small sample sizes in experimental studies.
• Financial constraints and complex methodologies further restrict sample sizes in in vitro and animal model studies.
• Pilot and feasibility studies, crucial before large clinical trials, also contend with small sample limitations.

Purpose of the Study:

• To introduce the Small Samples Evaluator (SSEv), a novel tool designed to address the analytical challenges posed by small sample sizes in research.
• To present a web-based application of the SSEv, making this statistical technique readily accessible to the scientific community.
• To provide a versatile tool applicable to diverse study designs, including repeated measures, case-control, and cross-sectional studies.

Main Methods:

• The Small Samples Evaluator (SSEv) is based on a modified survival curve analysis technique.
• The methodology is adaptable for analyzing data from various research designs, accommodating different statistical needs.
• A user-friendly, web-based application has been developed for the SSEv, facilitating its practical implementation.

Main Results:

• The SSEv provides a robust method for analyzing data from studies with limited sample sizes.
• The web application offers a practical and accessible platform for researchers to apply the SSEv technique.
• The tool's flexibility allows for its application across a spectrum of biomedical research scenarios.

Conclusions:

• The Small Samples Evaluator (SSEv) offers a valuable statistical approach to enhance the credibility of research involving small sample sizes.
• The freely accessible web application democratizes the use of advanced statistical methods for rare disease and other small-sample research.
• SSEv empowers researchers to conduct more reliable analyses, ultimately advancing scientific understanding in data-limited fields.