Assessment of response/error relationship and imprecision profile in immunoassay using computer simulation procedures

Area of Science:

• Biomedical analysis
• Immunoassay validation
• Statistical modeling in laboratory medicine

Background:

• Accurate assessment of imprecision profiles (IP) and response/error relationships (RER) is crucial for immunoassay validation.
• Understanding the impact of dose-response (DR) curve fitting error on overall assay variability is essential for reliable results.

Purpose of the Study:

• To evaluate the effectiveness of simulation procedures in defining reference functions for RER and IP in immunoassays.
• To determine optimal run sizes for accurately describing within-assay IPs.
• To assess the contribution of DR fitting error to total variability and identify methods for controlling response error.

Main Methods:

• Simulation procedures were used to analyze RER and IP for T4 radioimmunoassay (RIA) and TSH immunofluorometric assay (IFMA) using duplicate samples.
• Mean data from 10 experiments were employed to define reference functions for DR, RER, and IP.
• Statistical analysis focused on run size, DR fitting error, and RER parameter influence on variability.

Main Results:

• Run sizes of approximately 100 duplicates adequately describe within-assay IPs, regardless of data distribution.
• DR fitting error's contribution to total variability is negligible for small sample series but significant for larger ones.
• Variability components linked to response error can be effectively managed using RER-based criteria.

Conclusions:

• Approximately 100 duplicate samples are sufficient for reliable within-assay imprecision profiling in immunoassays.
• The impact of dose-response curve fitting error necessitates careful consideration, especially with larger datasets.
• Utilizing response/error relationship parameters provides an efficient method for controlling assay variability.