Efficiency issues among statistical methods for demonstrating efficacy of caries prevention.

Area of Science:

• Dental research
• Biostatistics
• Epidemiology

Background:

• Longitudinal caries clinical trials collect extensive tooth-surface-specific data.
• Traditional statistical analyses often underutilize these repeated measures, limiting statistical efficiency.
• Emerging analytical methods offer potential improvements in analyzing longitudinal dental data.

Purpose of the Study:

• To compare the statistical efficiency of various analytical methods for longitudinal caries clinical trials.
• To evaluate traditional methods against newer approaches, including time-to-event and clustered data models.
• To determine the impact of incorporating time-at-risk and surface-specific information on treatment comparison efficiency.

Main Methods:

• Comparison of traditional caries onset counting with advanced statistical models.
• Evaluation of time-to-event analysis methods.
• Assessment of Poisson regression for clustered data.
• Inclusion of surface time at risk and surface-specific characteristics in analyses.

Main Results:

• Time-to-event methods offer substantial efficiency gains, primarily by accounting for subjects lost to follow-up.
• Incorporating the number of surfaces at risk and surface time at risk provides modest efficiency improvements.
• Surface-specific characteristics contribute minor gains in analytical efficiency.

Conclusions:

• Time-to-event analysis is highly recommended for longitudinal caries trials due to its efficiency in handling patient attrition.
• While incorporating detailed surface information offers some benefits, the primary driver of efficiency gains lies in time-to-event methodologies.
• Advanced statistical approaches enhance the power and precision of treatment effect estimation in caries research.