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Item Comparison of Electrosurgical and Formocresol Pulpotomy Procedures(1997) Fulkerson, Bradley Todd; Dean, Jeffrey A.; Avery, David R.; Sanders, Brian J.; Zunt, Susan L.; Legan, Joseph E.Formocresol is the most commonly used pharmacologic pulpotomy agent. Concerns over its safety have led investigators to search for new pulpotomy medicaments. This study compared the electrosurgical pulpotomy with the formocresol pulpotomy in teeth requiring pulp therapy after carious involvement. There were 25 pulpotomies performed in each group. The teeth were evaluated for clinical and radiographic success after at least six months. In the electrosurgical group, the clinical and radiographic success rates were 96 percent and 84 percent, respectively. The age range at the time of treatment was 26 to 97 months, with a mean treatment age of 63.6 months. The postoperative observation time range was six to 31 months, with the mean being 10.9 months. In the formocresol group, the clinical and radiographic success rates were 100 percent and 92 percent, respectively. The age range at the time of treatment was 32 to 126 months, with a mean treatment age of 68.2 months. The postoperative observation time ranged from five to 25 months, with the mean being 11.5 months. The electrosurgical and formocresol groups were compared for differences in the percentage of successes by using a Fisher's Exact test. There were no statistical differences between the two groups at the p < 0.05 level. Therefore, this study failed to demonstrate a statistically significant difference in the success rate between the electrosurgical and formocresol pulpotomy techniques and supports the use of the electrosurgical pulpotomy as a viable and safe alternative to formocresol.Item A comparison of multiple testing adjustment methods with block-correlation positively-dependent tests(PLOS, 2017-04-28) Stevens, John R.; Al Masud, Abdullah; Suyundikov, Anvar; Biostatistics, School of Public HealthIn high dimensional data analysis (such as gene expression, spatial epidemiology, or brain imaging studies), we often test thousands or more hypotheses simultaneously. As the number of tests increases, the chance of observing some statistically significant tests is very high even when all null hypotheses are true. Consequently, we could reach incorrect conclusions regarding the hypotheses. Researchers frequently use multiplicity adjustment methods to control type I error rates-primarily the family-wise error rate (FWER) or the false discovery rate (FDR)-while still desiring high statistical power. In practice, such studies may have dependent test statistics (or p-values) as tests can be dependent on each other. However, some commonly-used multiplicity adjustment methods assume independent tests. We perform a simulation study comparing several of the most common adjustment methods involved in multiple hypothesis testing, under varying degrees of block-correlation positive dependence among tests.