Using survival methodologies in demonstrating caries efficacy.

Abstract

Exploiting recent advances in statistical methods, particularly for correlated intra-subject data, could increase the efficiency of caries clinical trials. Methods of analysis using the tooth surface as the unit should be investigated. Whole-mouth measures such as the DMFS increment ignore the variation in the number of surfaces at risk between subjects and within a subject over time. The use of "survival time" for each surface as the outcome measure--i.e., the time from the start of the trial to when a surface is recorded as decayed or filled--is proposed. Data from caries clinical trials could be described as clustered survival data, where clustering of tooth surfaces exists such that survival times within the same cluster or subject are correlated. Advances in the analysis of clustered survival data, such as the use of marginal models with robust variance estimators, have recently been exploited in the analysis of caries clinical trials. The analysis produced results similar to those achieved by conventional DMFS-based analysis. The results using survival analysis are easily interpreted-for example, the median survival time of tooth surfaces in female subjects using a toothpaste with a higher level of fluoride (1500 ppm F) is 1.07 times the median survival time of surfaces in female subjects using toothpaste with less fluoride (1000 ppm F). Further research is required to investigate if survival analysis is a more sensitive method of analysis, i.e., whether causative factors can be identified with fewer subjects than with the conventional method of analysis.