Clinical Characteristics of Breast Cancers in African‐American Women with Benign Breast Disease: A Comparison to the Surveillance, Epidemiology, and End Results Program

Benign breast disease ( BBD ) is a very common condition, diagnosed in approximately half of all A merican women throughout their lifecourse. White women with BBD are known to be at substantially increased risk of subsequent breast cancer; however, nothing is known about breast cancer characteristics that develop after a BBD diagnosis in A frican‐ A merican women. Here, we compared 109 breast cancers that developed in a population of A frican‐ A merican women with a history of BBD to 10,601 breast cancers that developed in a general population of A frican‐ A merican women whose cancers were recorded by the M etropolitan D etroit C ancer S urveillance S ystem ( MDCSS population). Demographic and clinical characteristics of the BBD population were compared to the MDCSS population, using chi‐squared tests, F isher's exact tests, t ‐tests, and W ilcoxon tests where appropriate. K aplan– M eier curves and Cox regression models were used to examine survival. Women in the BBD population were diagnosed with lower grade (p = 0.02), earlier stage cancers (p = 0.003) that were more likely to be hormone receptor‐positive (p = 0.03) compared to the general metropolitan Detroit A frican‐ A merican population. In situ cancers were more common among women in the BBD cohort (36.7%) compared to the MDCSS population (22.1%, p < 0.001). Overall, women in the BBD population were less likely to die from breast cancer after 10 years of follow‐up (p = 0.05), but this association was not seen when analyses were limited to invasive breast cancers. These results suggest that breast cancers occurring after a BBD diagnosis may have more favorable clinical parameters, but the majority of cancers are still invasive, with survival rates similar to the general A frican‐ A merican population.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109284/1/tbj12331.pd

Statistical design of personalized medicine interventions: The Clarification of Optimal Anticoagulation through Genetics (COAG) trial

<p>Abstract</p> <p>Background</p> <p>There is currently much interest in pharmacogenetics: determining variation in genes that regulate drug effects, with a particular emphasis on improving drug safety and efficacy. The ability to determine such variation motivates the application of personalized drug therapies that utilize a patient's genetic makeup to determine a safe and effective drug at the correct dose. To ascertain whether a genotype-guided drug therapy improves patient care, a personalized medicine intervention may be evaluated within the framework of a randomized controlled trial. The statistical design of this type of personalized medicine intervention requires special considerations: the distribution of relevant allelic variants in the study population; and whether the pharmacogenetic intervention is equally effective across subpopulations defined by allelic variants.</p> <p>Methods</p> <p>The statistical design of the Clarification of Optimal Anticoagulation through Genetics (COAG) trial serves as an illustrative example of a personalized medicine intervention that uses each subject's genotype information. The COAG trial is a multicenter, double blind, randomized clinical trial that will compare two approaches to initiation of warfarin therapy: genotype-guided dosing, the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in <it>CYP2C9 </it>and <it>VKORC1</it>; and clinical-guided dosing, the initiation of warfarin therapy based on algorithms using only clinical information.</p> <p>Results</p> <p>We determine an absolute minimum detectable difference of 5.49% based on an assumed 60% population prevalence of zero or multiple genetic variants in either <it>CYP2C9 </it>or <it>VKORC1 </it>and an assumed 15% relative effectiveness of genotype-guided warfarin initiation for those with zero or multiple genetic variants. Thus we calculate a sample size of 1238 to achieve a power level of 80% for the primary outcome. We show that reasonable departures from these assumptions may decrease statistical power to 65%.</p> <p>Conclusions</p> <p>In a personalized medicine intervention, the minimum detectable difference used in sample size calculations is not a known quantity, but rather an unknown quantity that depends on the genetic makeup of the subjects enrolled. Given the possible sensitivity of sample size and power calculations to these key assumptions, we recommend that they be monitored during the conduct of a personalized medicine intervention.</p> <p>Trial Registration</p> <p>clinicaltrials.gov: NCT00839657</p

A P-value model for theoretical power analysis and its applications in multiple testing procedures

Background: Power analysis is a critical aspect of the design of experiments to detect an effect of a given size. When multiple hypotheses are tested simultaneously, multiplicity adjustments to p-values should be taken into account in power analysis. There are a limited number of studies on power analysis in multiple testing procedures. For some methods, the theoretical analysis is difficult and extensive numerical simulations are often needed, while other methods oversimplify the information under the alternative hypothesis. To this end, this paper aims to develop a new statistical model for power analysis in multiple testing procedures. Methods: We propose a step-function-based p-value model under the alternative hypothesis, which is simple enough to perform power analysis without simulations, but not too simple to lose the information from the alternative hypothesis. The first step is to transform distributions of different test statistics (e.g., t, chi-square or F) to distributions of corresponding p-values. We then use a step function to approximate each of the p-value’s distributions by matching the mean and variance. Lastly, the step-function-based p-value model can be used for theoretical power analysis. Results: The proposed model is applied to problems in multiple testing procedures. We first show how the most powerful critical constants can be chosen using the step-function-based p-value model. Our model is then applied to the field of multiple testing procedures to explain the assumption of monotonicity of the critical constants. Lastly, we apply our model to a behavioral weight loss and maintenance study to select the optimal critical constants. Conclusions: The proposed model is easy to implement and preserves the information from the alternative hypothesis