Analytical Methods for Observational Data to Generate Hypotheses and Inform Clinical Decisions

Area of Science:

• Oncology
• Biostatistics
• Clinical Epidemiology

Background:

• Randomized controlled trials (RCTs) are the gold standard for clinical decision-making.
• Observational studies are increasingly considered for informing clinical decisions due to rising RCT costs and accrual challenges.
• Traditional use of observational studies was primarily for hypothesis generation.

Purpose of the Study:

• To review commonly used statistical methods for analyzing observational data in oncology.
• To guide researchers in selecting appropriate methods based on study hypotheses and assumptions.
• To highlight strengths, weaknesses, and common pitfalls of various analytical approaches.

Main Methods:

• Description of several prevalent statistical methods for observational data analysis.
• Evaluation of each method's ability to address specific research hypotheses.
• Emphasis on the importance of meeting underlying statistical assumptions for valid results.

Main Results:

• Different statistical methods for observational studies can yield comparable treatment effect estimates when applied correctly.
• Each analytical method possesses unique strengths and limitations.
• Proper application of methods is crucial for reliable findings.

Conclusions:

• Observational studies, when analyzed with appropriate statistical methods, can reliably inform clinical decisions in oncology.
• Researchers must carefully consider method-specific assumptions and potential missteps.
• This review serves as a reference for the oncology community on analyzing observational data.