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

Obesity increases operating room times in patients undergoing primary hip arthroplasty: a retrospective cohort analysis

Background. Obesity impacts utilization of healthcare resources. The goal of this study was to measure the relationship between increasing body mass index (BMI) in patients undergoing total hip arthroplasty (THA) with different components of operating room (OR) time.Methods. The Stanford Translational Research Integrated Database Environment (STRIDE) was utilized to identify all ASA PS 2 or 3 patients who underwent primary THA at Stanford Medical Center from February 1, 2008 through January 1, 2013. Patients were divided into five groups based on the BMI weight classification. Regression analysis was used to quantify relationships between BMI and the different components of total OR time.Results. 1,332 patients were included in the study. There were no statistically significant differences in age, gender, height, and ASA PS classification between the BMI groups. Normal-weight patients had a total OR time of 138.9 min compared 167.9 min (P < 0.001) for morbidly obese patients. At a BMI > 35 kg/m2 each incremental BMI unit increase was associated with greater incremental total OR time increases.Conclusion. Morbidly obese patients required significantly more total OR time than normal-weight patients undergoing a THA procedure. This increase in time is relevant when scheduling obese patients for surgery and has an important impact on health resource utilization

KIF1A-Associated Neurological Disorder: An Overview of a Rare Mutational Disease

KIF1A-associated neurological diseases (KANDs) are a group of inherited conditions caused by changes in the microtubule (MT) motor protein KIF1A as a result of KIF1A gene mutations. Anterograde transport of membrane organelles is facilitated by the kinesin family protein encoded by the MT-based motor gene KIF1A. Variations in the KIF1A gene, which primarily affect the motor domain, disrupt its ability to transport synaptic vesicles containing synaptophysin and synaptotagmin leading to various neurological pathologies such as hereditary sensory neuropathy, autosomal dominant and recessive forms of spastic paraplegia, and different neurological conditions. These mutations are frequently misdiagnosed because they result from spontaneous, non-inherited genomic alterations. Whole-exome sequencing (WES), a cutting-edge method, assists neurologists in diagnosing the illness and in planning and choosing the best course of action. These conditions are simple to be identified in pediatric and have a life expectancy of 5&ndash;7 years. There is presently no permanent treatment for these illnesses, and researchers have not yet discovered a medicine to treat them. Scientists have more hope in gene therapy since it can be used to cure diseases brought on by mutations. In this review article, we discussed some of the experimental gene therapy methods, including gene replacement, gene knockdown, symptomatic gene therapy, and cell suicide gene therapy. It also covered its clinical symptoms, pathogenesis, current diagnostics, therapy, and research advances currently occurring in the field of KAND-related disorders. This review also explained the impact that gene therapy can be designed in this direction and afford the remarkable benefits to the patients and society