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Multiple censored data in dentistry: A new statistical model for analyzing lesion size in randomized controlled
Marvin N Wright1, Andreas Ziegler
1Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
Biometrical Journal. Biometrische Zeitschrift
|April 1, 2015
Summary
Caries infiltration uses a novel diffusion barrier to slow tooth decay. A new statistical model, the Multiple Ordered Tobit (MOT) model, offers greater power for analyzing clinical trial data on this dental treatment.
Area of Science:
- Dental science
- Biostatistics
- Clinical trial methodology
Background:
- Proximal caries lesions require effective treatment strategies.
- Caries infiltration is a novel approach to impede lesion progression.
- Clinical trials generate censored ordinal data, posing analytical challenges.
Purpose of the Study:
- To introduce the Multiple Ordered Tobit (MOT) model for analyzing censored ordinal data in caries infiltration trials.
- To compare the statistical power and robustness of the MOT model against standard methods.
- To evaluate the impact of using metric versus scaled data on sample size requirements.
Main Methods:
- Development and implementation of the Multiple Ordered Tobit (MOT) model in R.
- Conducting simulation studies to assess model performance across various scenarios.
- Comparative analysis with standard statistical approaches, including dichotomous and ordinal models.
Main Results:
- The MOT model demonstrated superior statistical power across all tested sample sizes and scenarios.
- The MOT model exhibited robustness against heteroscedasticity.
- Utilizing metric data for lesion size significantly reduced the required sample size compared to scaled models.
Conclusions:
- The Multiple Ordered Tobit (MOT) model is a powerful and robust statistical tool for analyzing caries infiltration clinical trial data.
- Employing metric lesion size data can optimize trial efficiency by reducing sample size needs.
- The MOT model advances the statistical analysis of novel dental treatments with censored ordinal outcomes.
