 Methodology
 Open Access
 Open Peer Review
Statistical design of personalized medicine interventions: The Clarification of Optimal Anticoagulation through Genetics (COAG) trial
 Benjamin French†^{1}Email author,
 Jungnam Joo†^{2},
 Nancy L Geller^{2},
 Stephen E Kimmel^{1},
 Yves Rosenberg^{3},
 Jeffrey L Anderson^{4},
 Brian F Gage^{5},
 Julie A Johnson^{6},
 Jonas H Ellenberg^{1} and
 the COAG (Clarification of Optimal Anticoagulation through Genetics) Investigators
https://doi.org/10.1186/1745621511108
© French et al; licensee BioMed Central Ltd. 2010
 Received: 28 July 2010
 Accepted: 17 November 2010
 Published: 17 November 2010
Abstract
Background
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 genotypeguided 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.
Methods
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: genotypeguided dosing, the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in CYP2C9 and VKORC1; and clinicalguided dosing, the initiation of warfarin therapy based on algorithms using only clinical information.
Results
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 CYP2C9 or VKORC1 and an assumed 15% relative effectiveness of genotypeguided 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%.
Conclusions
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.
Trial Registration
clinicaltrials.gov: NCT00839657
Keywords
 Warfarin
 Warfarin Therapy
 Full Cohort
 Minimum Detectable Difference
 Primary Subgroup
Background
Personalized Medicine Interventions
The recent availability of lowercost genetic testing has motivated medical researchers to determine whether patient care and safety is improved by using a patient's genetic information to initiate and manage drug therapy [1]. To evaluate scientific hypotheses regarding a personalized medicine intervention, a randomized clinical trial can be used to contrast outcomes between subjects randomized to receive genotypeguided drug therapy and those randomized to receive an identical therapy without reference to their genetic characteristics [2]. However, because not all subjects may benefit from the pharmacologic intervention due to their genetic makeup, genotypeguided therapy may not benefit the entire study population. Hence, any putative difference between treatment groups will be attenuated, which may adversely impact key components of the statistical design, such as sample size and statistical power. Therefore, the primary statistical challenge of designing a personalized therapy intervention is to accommodate the potential differential effectiveness of genotypeguided therapy across subpopulations defined by allelic variation.
Although interventions that use a subject's clinical factors, gene expression profile, or perhaps other factors can also be considered as personalized medicine, we restrict our attention to interventions that use genotype. In addition, personalized medicine interventions may be evaluated using several different study designs. For example, in a targeted design, frequently used to evaluate geneticbased therapies for cancer, study eligibility may be restricted to a markerpositive subset of the population anticipated to benefit from therapy based on their genetic characteristics [3]. We focus on untargeted designs, such as those that have been used to evaluate genotypeguided dosing of warfarin, in which all subjects are enrolled regardless of their genetic characteristics.
GenotypeGuided Dosing of Warfarin
Warfarin sodium is the most common oral anticoagulant used for the prevention and treatment of thromboembolism, the formation of a clot in a blood vessel or cardiac chamber that may be carried by the blood stream and obstruct another vessel. Initiation of warfarin therapy is usually based on empiric dosing, which may put patients at an increased risk for either major bleeding complications due to overanticoagulation or thromboembolic events due to underanticoagulation. Therefore, initiation of warfarin therapy at an improper dose may be associated with increased costs and higher morbidity [4].
Many patientspecific clinical factors impact warfarin doseresponse. In addition, two genes influence warfarin dose: the cytochrome P450 family 2 subfamily C polypeptide 9 enzyme (CYP2C9) gene effects pharmacokinetics, i.e., the effects of the body on the drug; and the vitamin K epoxide reductase complex 1 (VKORC1) gene effects pharmacodynamics, i.e., the effects of the drug on the body. Thus, CYP2C9 variants alter Swarfarin metabolism [5]; VKORC1 variants alter warfarin response [6]. Both CYP2C9 and VKORC1 have proven useful in algorithms to predict the ultimate maintenance dose for optimal warfarin therapy [7]. However, they have not yet been proven to be beneficial in choosing the initial warfarin dose or to impact clinical outcomes.
The goal of this manuscript is to provide practical guidance on the statistical design of a personalized medicine intervention that uses each subject's genotype information in an untargeted design. The statistical design of the COAG trial serves as an illustrative example. We briefly summarize the clinical rationale and the general study design for the COAG trial. We use power and sample size calculations to illustrate the primary statistical challenge of designing a personalized therapy intervention: to accommodate the potential differential effectiveness of genotypeguided therapy across subpopulations defined by allelic variation. We provide a sensitivity analysis to quantify the extent to which power and sample size calculations may be sensitive to key assumptions required in the statistical design of a personalized medicine intervention. We conclude with general recommendations for the statistical design of personalized medicine interventions.
Methods
The objective of the COAG trial (clinicaltrials.gov identifier: NCT00839657) is to conduct a multicenter, double blind, randomized clinical trial that compares two approaches to initiation of warfarin therapy:

Genotypeguided dosing, the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in two genes known to influence warfarin response (CYP2C9 and VKORC1); and

Clinicalguided dosing, the initiation of warfarin therapy based on algorithms using only clinical information.
Both approaches will include a baseline doseinitiation algorithm [8] and a doserevision algorithm [9] applied after four or five days of warfarin therapy. Subsequent doses will be determined using a standard dosetitration algorithm, which is identical for both groups. By comparing the efficacy of genotypeguided dosing to that of clinicalguided dosing, the COAG trial will determine whether the incremental use of genetic information improves stability of anticoagulation during the early treatment period. Future studies could then determine whether such an improvement leads to significantly reduced costs and lower morbidity.
Eligible subjects will be recruited from at least 12 clinical sites in the United States. Clinical and genotype data will be collected on all subjects. Subjects will be randomized to initiate warfarin therapy either using genotypeguided or clinicalguided dosing. All subjects will receive their warfarin on a standardofcare schedule. Study investigators, clinicians, and subjects will be blinded to treatment assignment and warfarin dose for the first four weeks of the trial. After four weeks of therapy, subjects will be unblinded to dose and followed for up to an additional five months. The Institutional Review Board of all participating institutions approved the COAG trial. Written informed consent will be obtained from all patients who participate in the trial.
Analysis of the primary outcome will be by intentiontotreat [11]. It will not be possible for subjects to switch from their assigned treatment group, but there might be crossovers due to the unavailability of genetic information at the time that the initial dose is dispensed. Every attempt will be made to determine a subject's genotype prior to administration of the initial dose. Given recent technologies, sameday genotyping for warfarin is now possible in practice. In the COAG trial, clinical sites are using one of two genotyping platforms; each has a rapid turnaround time. Both platforms have been FDA approved, have high call and concordance rates, very low failure rates, and the ability to genotype the SNPs needed for the selected dosing algorithms.
For those subjects assigned to the genotypeguided dosing group whose genetic information is not available prior to the initial dose, the initial dose will be determined using the clinical doseinitiation algorithm. Once genetic information becomes available, the dose for these subjects will be determined using the genetic doseinitiation and doserevision algorithms. The genotypeguided doseinitiation algorithm on day one only uses information on VKORC1 (not CYP2C9) [8]. Therefore, we expect the dose differences on day one to be small relative to the dose differences after the first day. The genotypeguided doseinitiation algorithm on day two, as well as the genotypeguided doserevision algorithm on days four and five [9], uses information from both VKORC1 and CYP2C9, so that the availability of genetic information by day two will allow the full use of the subject's genetic information to determine their dose for days two through five. We fully expect genotype information to be available on almost all subjects within 24 hours, and certainly by the time of the doserevision calculations on days four and five.
Randomization
To provide balance in treatment assignment within sites, random assignment to either the genotypeguided or clinicalguided dosing group will be stratified by clinical site. Randomization will also be stratified by race (African American versus not, including Caucasian and Asian American) because race is associated with differential predictive ability of dosing algorithms, with lesser accuracy in African Americans [8], and the dosing algorithms used in the trial predict dose differently among African Americans [9]. In addition, AfricanAmerican race is associated with the prevalence CYP2C9 and VKORC1 variants and is associated with the prevalence of other genetic variants that influence warfarin doseresponse. Finally, some clinical sites may recruit a small number of African Americans due to the demographic makeup of their surrounding community.
We will use a blockrandomized procedure to assign the treatment groups. Blocking ensures that there will be a balance in the number of patients in each treatment group within each clinical site. Thus, we will use permuted blocks with block sizes of four and six, randomly chosen, which will minimize any imbalances in treatment group assignment. The RANUNI function in SAS 9.2 will be used to generate the randomization numbers within each site for two strata [12].
Sample Size and Statistical Power
A critical element of the statistical design of a randomized clinical trial is to determine a sample size so that a statistical test has adequate power to detect a clinically relevant difference in the primary outcome between treatment groups. The parameters considered in the estimation of sample size include: a minimum detectable difference in the primary outcome between groups; an assumed level of significance for the statistical test of the primary outcome; a measure of variability for the primary outcome in the study population; and the percentage of subjects, if any, expected to drop out of the trial. The sample size parameters for a personalized medicine intervention require additional considerations: the distribution of relevant allelic variants in the study population; and whether the intervention is equally effective across subpopulations defined by allelic variants (e.g., if patients with particular genotypes are not expected to benefit from genotypeguided drug therapy, as we illustrate for warfarin). Due to uncertainly in the distribution of allelic variants and uncertainty in the effectiveness of the intervention across subpopulations, careful attention is required in the design of a personalized medicine to ensure that the study will have adequate power to detect a clinically relevant minimum detectable difference.
where PTTR_{C} and PTTR_{G} denote the PTTR in the clinicalguided and genotypeguided dosing groups, respectively. It is straightforward to generalize this approach to more than two subpopulations of interest. Adding additional terms into the weighted average, given the population prevalence and the anticipated treatment effect in each subpopulation, could accommodate more than two subpopulations. Indeed, this approach is generalizeable to any setting in which treatment effects are expected to differ across any number of subpopulations. Specific assumptions are discussed in the following section.
Minimum Detectable Difference
where:

The proportion in the population who possess a single genetic variant (in either CYP2C9 or VKORC1) and who possess zero or multiple variants is assumed to be 0.4 and 0.6, respectively;

A PTTR of 73% and 61% is assumed for those who possess a single genetic variant and for those who possess zero or multiple variants, respectively;

A 0% relative difference in PTTR is assumed for those with a single genetic variant; and

A 15% relative difference in PTTR for those with zero or multiple variants is assumed to be a clinically relevant difference between the genotypeguided and clinicalguided dosing groups [14].
We assumed that subjects who possess a single genetic variant (in either CYP2C9 or VKORC1) would not benefit from clinicalguided dosing because previous data suggest that the genotypeguided algorithm will predict essentially the same dose as the clinicalguided algorithm. These subjects are expected to attain the same PTTR regardless of their treatment assignment and thus attenuate the mean difference in PTTR between the two groups. To wit, the assumed 15% relative difference in PTTR between the genotypeguided and clinicalguided dosing groups is attenuated to an absolute difference of 5.49% (PTTR_{G}  PTTR_{C}). Therefore, we assumed an overall minimum detectable difference of 5.49% between groups in the full cohort for sample size calculations. If we had ignored the fact that the intervention is not equally effective across subpopulations defined by genetic variants and assumed a minimum detectable difference of 15%, then the trial would have chosen an inadequately small sample size to achieve adequate power.
The assumed proportion of 0.4 who possess a single genetic variant is based on the CoumaGen trial [13] and the International Warfarin Pharmacogenetics Consortium (IWPC) [15]. We considered the sensitivity of sample size calculations to a range a population proportions. The assumption that those who possess a single genetic variant will not benefit from genotypeguided dosing, while suggested by the CoumaGen trial, was not supported in another clinical trial in which all patients benefited from dosing based on CYP2C9[16]. In the latter study, the effect of a pharmacogenetic dosing algorithm was similar regardless of the number of CYP2C9 variants present. Therefore, we believe that our assumptions are conservative.
Level of Significance
To determine the level of significance (α) for the statistical test of PTTR between the genotypeguided and clinicalguided dosing groups, we considered an alphaallocation approach [17–19]. In this approach, a portion (α_{A}) of the overall level of significance is used to test the comparison in the full cohort; the remaining portion (α_{S}) is used to test the comparison in a predefined primary subgroup. The alphaallocation approach facilitates a traditional primary analysis to assess a statistically significant difference between the treatment groups, as well as a predefined primary subgroup analysis that is not relegated to a secondary analysis, as in a traditional analysis.
We defined the primary subgroup based on subjects whose predicted initial dose employing the genetic and clinical doseinitiation algorithms differs by ≥ 1.0 mg, a factor known at the time of randomization and therefore not a postrandomization selection. We posited that the subgroup of participants with a larger difference between the predicted initial doses should have a larger separation in PTTR between the two groups. If the improvement in PTTR is related to the magnitude of difference in dosing between the genotypeguided and clinicalguided dosing groups, then the primary subgroup comparison should reflect a larger absolute difference than the 5.49% assumed for the full cohort analysis. We assumed that a clinically relevant absolute difference to detect in the primary subgroup is 9.15%, from a PTTR of 61% to 70.15% in Equation (4), reflecting a 15% relative difference.
We selected α_{A} = 0.04 for the full cohort analysis and α_{S} = 0.01 for the primary subgroup analysis, for an overall typeI error rate of α = 0.05. However, allocating alpha so that sum of α_{A} and α_{S} is equal to α is a conservative Bonferronitype correction, which may be unnecessarily conservative if there is a positive correlation between the tests in the full cohort and in the primary subgroup [20, 21]. The correlation between the two tests will be obtained under the null hypothesis when the size of the primary subgroup is known. The correlation will then be incorporated to obtain α_{S} > α  α_{A} given that α_{A} is fixed, so that the overall typeI error rate is controlled at α.
Other assumptions in the computation of sample size were the standard deviation of the PTTR in the study population and the percentage of subjects expected to drop out before reaching the primary endpoint. The withinstudy variability of PTTR in the literature varied across study designs and populations under study. However, there was a reasonable consistency of variability for the geneticguided and clinicalguided dosing groups in the studies reviewed. We assumed a standard deviation of 25% based on a study of doserefinement algorithms in which the standard deviation averaged 23% [22]. We also assumed that 10% of subjects would drop out before reaching the primary endpoint and increased the sample size by dividing the calculated sample size by the square of one minus the dropout rate [23].
Primary Analysis
The null hypothesis for the primary outcome is that the percent of time that subjects spend within the therapeutic INR range (PTTR) during the first four weeks of therapy is equal between the genotypeguided and clinicalguided dosing groups. We will estimate the difference in mean PTTR between the genotypeguided and clinicalguided dosing groups using a linear regression model, both for the full cohort and for the primary subgroup whose predicted initial dose employing the genetic and clinical doseinitiation algorithms differs by ≥ 1 mg. Inference will be based on a Wald test with a level of significance of 0.05 allocated between the full cohort analysis and the primary subgroup analysis. Because randomization will be stratified by site and race, these variables will be included in the linear regression model. We will perform additional analyses in subgroups defined a priori by allelic variation (zero versus a single versus multiple CYP2C9 or VKORC1 variants) and by race (African American versus not).
Additional genetic factors may be considered in secondary analyses. Specifically, because CYP2C9 and VKORC1 genotypes may not be the only genetic variants that determine optimal warfarin dosing, it is possible that more variants will be identified during the trial. To adjust for additional genetic factors in secondary analyses, we will include them as covariates in a linear regression model if their prevalence differs between the clinical and genetic groups, and also consider possible interactions with CYP2C9 and/or VKORC1.
Results
Sample size estimates for the full cohort analysis; p denotes the proportion with a single genetic variant and Δ denotes the corresponding minimum detectable difference
Standard Deviation of PTTR  

20%  25%  30%  
Power  Power  Power  
p  Δ  80%  90%  80%  90%  80%  90% 
0.4  5.49%  550  730  860  1140  1238  1642 
0.5  4.58%  792  1050  1238  1642  1782  2364 
0.6  3.67%  1238  1642  1932  2564  2782  3692 
A sample size of 1238 provides sufficient power for the primary subgroup analysis using a twosample ttest with α_{S} = 0.01 (twosided). Recall that the size of the primary subgroup is determined by the percentage of subjects whose predicted initial dose employing the genetic and clinical doseinitiation algorithms differs by ≥ 1 mg. If the relative size of the primary subgroup is 50% and the standard deviation of PTTR is 25%, then there is 93.6% power to detect a 9.15% absolute difference. In addition, if the relative size of the primary subgroup is 60% and the standard deviation is 30%, then there is 87.8% power. In fact, the power will be higher because α_{S} will be increased according to the correlation between the tests in the full cohort and in the primary subgroup.
Sensitivity Analysis
where d is the proportion of subjects with zero or multiple genetic variants in whom there is a clinically meaningful difference between the predicted dose determined by the genotypeguided and clinicalguided dosing algorithms. Hence the expected 15% difference would be diluted by a factor d and it would be more difficult to detect a clinically relevant difference between groups.
To explore the impact of dilution of the treatment effect, we examined the distribution of the differences between the predicted doses among groups defined by allelic variation in the IWPC cohort [15] and calculated the difference between the rounded predicted doses. An absolute dose difference < 1.0 mg per day was defined as the 'same' predicted dose; an absolute dose difference of ≥ 1.0 mg per day was defined as a 'different' predicted dose. The rationale for the 1.0 mg cutpoint is that the average initial dose is 5.0 milligrams; therefore, a 1.0 mg absolute difference represents a clinically relevant 20% difference, on average. Approximately 9% of IWPC participants in the (0, >1) allelic variant group would have received the 'same' initial dose, i.e., d = 0.91. With this dilution of the treatment effect, in order to detect an overall effect size of 5.49% in PTTR, the relative effect size in the (0, > 1) group would need to be 16.5%.
Power estimates for the full cohort analysis in which the treatment effect is diluted; d denotes the proportion of subjects with zero or multiple genetic variants in whom there is a difference between the predicted initial doses
d  Treatment Effect  Power 

0.7  3.84  65% 
0.8  4.39  77% 
0.9  4.94  86% 
1.0 (Undiluted)  5.49  93% 
Discussion
In this manuscript we provided practical guidance on the statistical design of a personalized medicine intervention that uses each subject's genotype information in an untargeted design. We used power and sample size calculations to illustrate the primary statistical challenge of designing this type of personalized therapy intervention: to accommodate the potential differential effectiveness of genotypeguided therapy across subpopulations defined by allelic variation. To determine a minimum detectable difference in PTTR between groups, we assumed that 40% of enrolled subjects would have a single genetic variant and would therefore not benefit from genotypeguided warfarin therapy. Hence, 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. In addition, the sample size for the primary subgroup analysis depends on the proportion of subjects whose predicted initial dose employing the genetic and the clinical doseinitiation algorithms differs by ≥ 1.0 mg. Due to the importance of these parameters for adequate sample size and statistical power to detect a clinically meaningful difference, they will be monitored during the course of the trial.
As shown in Table 1, the sample size is sensitive to the standard deviation of PTTR. The Data Safety and Monitoring Board (DSMB) may suggest an 'internal pilot study' in which an estimate of the standard deviation will be obtained using the first half of the observed data and the sample size calculations will be updated based on the new estimate [24, 25]. The preplanned sample size will be assumed to represent a minimum sample size (i.e., the final sample size based on the 'internal pilot study' will not be less than the preplanned sample size). In this case, the 'internal pilot study' is known as restricted. For restricted designs, the disparity in the typeI error rate in testing the primary hypothesis is negligible [26]. Therefore, it will not be necessary to adjust the typeI error rate of any hypothesis tests regarding the primary outcome. In assessing the need for a sample size increase, data will neither be unblinded nor assessed for the primary outcome. In addition, a sample size adjustment will not impact the overall design of the study. Because the DSMB will not monitor efficacy during the conduct on the COAG trial, there is no conflict between any interim sample size adjustment and interim measures of efficacy.
where d' is the proportion of subjects with a single genetic variant in whom there is a meaningful difference between the predicted doses and d is defined in Equation (5). For example, in the IWPC cohort, approximately 26% of subjects with a single genetic variant would have received a 'different' initial dose, i.e., d' = 0.26. For these subjects, we expect that there would be a difference in PTTR, which would increase the power of the full cohort analysis. Because we were not highly confident in this estimate, we did not examine the increase in power associated with this allelic subgroup. Therefore, our sensitivity analysis is conservative.
An individual's genetic information could be used prior to randomization to identify subjects who are potentially unresponsive to either drug therapy or the pharmacologic intervention, motivating researchers to decide whether to include or exclude those subjects from the trial [27]. For example, in a targeted design, study eligibility may be restricted to subjects who, based on their genetic characteristics, are predicted to be responsive [28]. By excluding potentially unresponsive individuals, a targeted design will require a smaller sample size to detect a statistically significant effect. Conversely, in a traditional (untargeted) design, particularly of an intervention designed to select dose, subjects for whom geneticbased drug therapy is not effective are eligible, because they would still receive drug treatment regardless of their genetic makeup. For example, subjects in the COAG trial would receive warfarin therapy regardless of their CYP2C9 and VKORC1 variants. As we have shown with the statistical design of the COAG trial, by including potentially unresponsive subjects, a larger sample size may be required. Costbenefit considerations regarding the cost of genetic screening for eligibility versus the cost of enrolling potentially unresponsive subjects may be useful to determine which design is more practical in specific applications.
For the COAG trial, we favored including all participants, regardless of their genetic variants. First, the assumption that those who possess a single genetic variant will not benefit from genotypeguided dosing, while suggested by the CoumaGen trial [13], was not supported in another clinical trial in which all patients benefited from dosing based on CYP2C9[16]. Therefore, if we excluded subjects who may not benefit from genotypeguided dosing, we would be unable to evaluate our assumptions. Second, all subjects are genotyped prior to randomization, so that much of the cost is already incurred in screening. Third, including subjects potentially unresponsive to genotypeguided dosing allows the results of the trial to be more generalizable. That is, if the COAG trial indicates that genotypeguided dosing provides increased efficacy compared to clinicalguided dosing, then it motivates consideration of the policy question of whether all patients prescribed warfarin should be genotyped to predict the drug's efficacy.
We recommend that the statistical design of a personalized medicine intervention that uses each subject's genotype information, within the framework of a randomized clinical trial, consider the distribution of relevant allelic variants in the study population and whether the intervention is equally effective across subpopulations defined by allelic variants. In the statistical design of the COAG trial, we considered the distribution of CYP2C9 and VKORC1 variants and whether genotypeguided dosing of warfarin therapy would provide an equal improvement in efficacy across populations defined by genetic variants. We assumed that subjects with a single genetic variant would not benefit from genotypeguided dosing, thus attenuating the postulated 15% relative difference between the two treatment groups to a 5.49% absolute difference. In our sample size calculations, if we ignored the fact that the genotypeguided dosing is not equally effective across subpopulations defined by genetic variants and assumed a minimum detectable difference of 15%, then the COAG trial would likely have chosen an inadequately small sample size to achieve adequate power. We also recommend that key assumptions regarding sample size and statistical power be monitored during the conduct of the trial, to inform any requisite increase in the sample size needed to detect a clinically relevant difference in the primary outcome between treatment groups. Further research is required to determine whether an interim sample size adjustment based on the observed proportion of allelic variants increases the typeI error rate.
Conclusions
In summary, we found that sample size and power calculations may be sensitive to key assumptions required in the design of a personalized medicine intervention: the distribution of relevant allelic variants in the study population; and whether the pharmacogenetic intervention is equally effective across subpopulations defined by allelic variants. Given the novelty of pharmacogenetic research, we recommend that these assumptions be monitored during the conduct of a personalized medicine intervention.
Notes
Declarations
Acknowledgements
We gratefully acknowledge the National Heart, Lung and Blood Institute (N01 HV88210) and the University of Pennsylvania for supporting this research, and two reviewers for comments that greatly improved the manuscript.
COAG Investigators: Sherif AbdelRahman, University of Texas; Robert J Desnick and Jonathan L Halperin, Mount Sinai School of Medicine; Margaret C Fang, University of California, San Francisco; Brian F Gage, Washington University School of Medicine; Richard B Horenstein, University of Maryland School of Medicine; Julie A Johnson, University of Florida; Scott Kaatz, Henry Ford Hospital; Robert D McBane, Mayo Clinic College of Medicine; Emile R Mohler III, Hospital of the University of Pennsylvania; James A S Muldowney III, Vanderbilt University; Scott M. Stevens, Intermountain Medical Center; Steven Yale, Marshfield Clinical Research Foundation.
Authors’ Affiliations
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