A competing risk model for bond strength data analysis.

Dent Mater

Department of Cariology and Operative Dentistry, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.

Published: December 2020

Objectives: A competing risk (CR) model distinguishing adhesive, cohesive and mixed failures as competing events was used for the analysis of micro-tensile bond strength (μTBS) data and compared with a conventional failure mode non-distinguishing survival model.

Methods: Fifty human molars were bonded using five universal adhesives (n = 10) and subdivided according to aging conditions (24-h water storage, thermocycling). After μTBS to dentin was tested, a fractographic analysis was performed using scanning electron microscopy. Survival analyses of the μTBS data were performed using both a failure mode distinguishing Weibull CR model, and a conventional failure mode non-distinguishing Weibull model. Weibull shape (m) and scale (σ) parameters were calculated for both models using the maximum likelihood estimation method, and strength at 10 % probability of failure, σ, was estimated. Groups were compared using 95 % confidence intervals.

Results: CR-model estimates of σ and σ for adhesive failures were higher than those of the conventional model, more markedly in groups with lower percentages of adhesive failures. CR-model strength estimates for cohesive failures were similar in all groups regardless of their bond strengths and failure mode distributions.

Significance: Merging all bond-strength data into one dataset irrespective of the failure mode may result in a severe underestimation of bond strength, especially in groups with low incidence of adhesive failures. Bond-strength data analysis using a CR model could provide more accurate estimates of bond strength, and strength estimates for cohesive failures which were apparently independent of bond strength could serve as an internal validity indicator of the CR model.

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Source
http://dx.doi.org/10.1016/j.dental.2020.09.004DOI Listing

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