Use of win time for ordered composite endpoints in clinical trials.

Consider the choice of outcome for overall treatment benefit in a clinical trial which measures the first time to each of several clinical events. We describe several new variants of the win ratio that incorporate the time spent in each clinical state over the common follow-up, where clinical state means the worst clinical event that has occurred by that time. One version allows restriction so that death during follow-up is most important, while time spent in other clinical states is still accounted for. Three other variants are described; one is based on the average pairwise win time, one creates a continuous outcome for each participant based on expected win times against a reference distribution and another that uses the estimated distributions of clinical state to compare the treatment arms. Finally, a combination testing approach is described to give robust power for detecting treatment benefit across a broad range of alternatives. These new methods are designed to be closer to the overall treatment benefit/harm from a patient's perspective, compared to the ordinary win ratio. The new methods are compared to the composite event approach and the ordinary win ratio. Simulations show that when overall treatment benefit on death is substantial, the variants based on either the participants' expected win times (EWTs) against a reference distribution or estimated clinical state distributions have substantially higher power than either the pairwise comparison or composite event methods. The methods are illustrated by re-analysis of the trial heart failure: a controlled trial investigating outcomes of exercise training.

Comparison of Bayesian and frequentist monitoring boundaries motivated by the Multiplatform Randomized Clinical Trial.

BACKGROUND: The coronavirus disease 2019 pandemic highlighted the need to conduct efficient randomized clinical trials with interim monitoring guidelines for efficacy and futility. Several randomized coronavirus disease 2019 trials, including the Multiplatform Randomized Clinical Trial (mpRCT), used Bayesian guidelines with the belief that they would lead to quicker efficacy or futility decisions than traditional "frequentist" guidelines, such as spending functions and conditional power. We explore this belief using an intuitive interpretation of Bayesian methods as translating prior opinion about the treatment effect into imaginary prior data. These imaginary observations are then combined with actual observations from the trial to make conclusions. Using this approach, we show that the Bayesian efficacy boundary used in mpRCT is actually quite similar to the frequentist Pocock boundary. METHODS: The mpRCT's efficacy monitoring guideline considered stopping if, given the observed data, there was greater than 99% probability that the treatment was effective (odds ratio greater than 1). The mpRCT's futility monitoring guideline considered stopping if, given the observed data, there was greater than 95% probability that the treatment was less than 20% effective (odds ratio less than 1.2). The mpRCT used a normal prior distribution that can be thought of as supplementing the actual patients' data with imaginary patients' data. We explore the effects of varying probability thresholds and the prior-to-actual patient ratio in the mpRCT and compare the resulting Bayesian efficacy monitoring guidelines to the well-known frequentist Pocock and O'Brien-Fleming efficacy guidelines. We also contrast Bayesian futility guidelines with a more traditional 20% conditional power futility guideline. RESULTS: A Bayesian efficacy and futility monitoring boundary using a neutral, weakly informative prior distribution and a fixed probability threshold at all interim analyses is more aggressive than the commonly used O'Brien-Fleming efficacy boundary coupled with a 20% conditional power threshold for futility. The trade-off is that more aggressive boundaries tend to stop trials earlier, but incur a loss of power. Interestingly, the Bayesian efficacy boundary with 99% probability threshold is very similar to the classic Pocock efficacy boundary. CONCLUSIONS: In a pandemic where quickly weeding out ineffective treatments and identifying effective treatments is paramount, aggressive monitoring may be preferred to conservative approaches, such as the O'Brien-Fleming boundary. This can be accomplished with either Bayesian or frequentist methods.

Cardiorespiratory Monitoring Data to Predict Respiratory Outcomes in Extremely Preterm Infants.

Rationale: Immature control of breathing is associated with apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants. However, it is not clear if such events independently predict worse respiratory outcome. Objectives: To determine if analysis of cardiorespiratory monitoring data can predict unfavorable respiratory outcomes at 40 weeks postmenstrual age (PMA) and other outcomes, such as bronchopulmonary dysplasia at 36 weeks PMA. Methods: The Prematurity-related Ventilatory Control (Pre-Vent) study was an observational multicenter prospective cohort study including infants born at <29 weeks of gestation with continuous cardiorespiratory monitoring. The primary outcome was either "favorable" (alive and previously discharged or inpatient and off respiratory medications/O2/support at 40 wk PMA) or "unfavorable" (either deceased or inpatient/previously discharged on respiratory medications/O2/support at 40 wk PMA). Measurements and Main Results: A total of 717 infants were evaluated (median birth weight, 850 g; gestation, 26.4 wk), 53.7% of whom had a favorable outcome and 46.3% of whom had an unfavorable outcome. Physiologic data predicted unfavorable outcome, with accuracy improving with advancing age (area under the curve, 0.79 at Day 7, 0.85 at Day 28 and 32 wk PMA). The physiologic variable that contributed most to prediction was intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <90%. Models with clinical data alone or combining physiologic and clinical data also had good accuracy, with areas under the curve of 0.84-0.85 at Days 7 and 14 and 0.86-0.88 at Day 28 and 32 weeks PMA. Intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <80% was the major physiologic predictor of severe bronchopulmonary dysplasia and death or mechanical ventilation at 40 weeks PMA. Conclusions: Physiologic data are independently associated with unfavorable respiratory outcome in extremely preterm infants.

Evaluating treatment effects in group sequential multivariate longitudinal studies with covariate adjustment.

Jeffries et al. (2018) investigated testing for a treatment difference in the setting of a randomized clinical trial with a single outcome measured longitudinally over a series of common follow-up times while adjusting for covariates. That paper examined the null hypothesis of no difference at any follow-up time versus the alternative of a difference for at least one follow-up time. We extend those results here by considering multivariate outcome measurements, where each individual outcome is examined at common follow-up times. We consider the case where there is interest in first testing for a treatment difference in a global function of the outcomes (e.g., weighted average or sum) with subsequent interest in examining the individual outcomes, should the global function show a treatment difference. Testing is conducted for each follow-up time and may be performed in the setting of a group sequential trial. Testing procedures are developed to determine follow-up times for which a global treatment difference exists and which individual combinations of outcome and follow-up time show evidence of a difference while controlling for multiplicity in outcomes, follow-up, and interim analyses. These approaches are examined in a study evaluating the effects of tissue plasminogen activator on longitudinally obtained stroke severity measurements.

Joint testing of overall and simple effects for the two-by-two factorial trial design.

BACKGROUND/AIMS: The two-by-two factorial design randomizes participants to receive treatment A alone, treatment B alone, both treatments A and B(AB), or neither treatment (C). When the combined effect of A and B is less than the sum of the A and B effects, called a subadditive interaction, there can be low power to detect the A effect using an overall test, that is, factorial analysis, which compares the A and AB groups to the C and B groups. Such an interaction may have occurred in the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD BP) which simultaneously randomized participants to receive intensive or standard blood pressure, control and intensive or standard glycemic control. For the primary outcome of major cardiovascular event, the overall test for efficacy of intensive blood pressure control was nonsignificant. In such an instance, simple effect tests of A versus C and B versus C may be useful since they are not affected by a subadditive interaction, but they can have lower power since they use half the participants of the overall trial. We investigate multiple testing procedures which exploit the overall tests' sample size advantage and the simple tests' robustness to a potential interaction. METHODS: In the time-to-event setting, we use the stratified and ordinary logrank statistics' asymptotic means to calculate the power of the overall and simple tests under various scenarios. We consider the A and B research questions to be unrelated and allocate 0.05 significance level to each. For each question, we investigate three multiple testing procedures which allocate the type 1 error in different proportions for the overall and simple effects as well as the AB effect. The Equal Allocation 3 procedure allocates equal type 1 error to each of the three effects, the Proportional Allocation 2 procedure allocates 2/3 of the type 1 error to the overall A (respectively, B) effect and the remaining type 1 error to the AB effect, and the Equal Allocation 2 procedure allocates equal amounts to the simple A (respectively, B) and AB effects. These procedures are applied to ACCORD BP. RESULTS: Across various scenarios, Equal Allocation 3 had robust power for detecting a true effect. For ACCORD BP, all three procedures would have detected a benefit of intensive glycemia control. CONCLUSIONS: When there is no interaction, Equal Allocation 3 has less power than a factorial analysis. However, Equal Allocation 3 often has greater power when there is an interaction. The R package factorial2x2 can be used to explore the power gain or loss for different scenarios.

Detecting treatment differences in group sequential longitudinal studies with covariate adjustment.

In longitudinal studies comparing two treatments over a series of common follow-up measurements, there may be interest in determining if there is a treatment difference at any follow-up period when there may be a non-monotone treatment effect over time. To evaluate this question, Jeffries and Geller (2015) examined a number of clinical trial designs that allowed adaptive choice of the follow-up time exhibiting the greatest evidence of treatment difference in a group sequential testing setting with Gaussian data. The methods are applicable when a few measurements were taken at prespecified follow-up periods. Here, we test the intersection null hypothesis of no difference at any follow-up time versus the alternative that there is a difference for at least one follow-up time. Results of Jeffries and Geller (2015) are extended by considering a broader range of modeled data and the inclusion of covariates using generalized estimating equations. Testing procedures are developed to determine a set of follow-up times that exhibit a treatment difference that accounts for multiplicity in follow-up times and interim analyses.

Dealing with competing risks in clinical trials: How to choose the primary efficacy analysis?

We investigate different primary efficacy analysis approaches for a 2-armed randomized clinical trial when interest is focused on a time to event primary outcome that is subject to a competing risk. We extend the work of Friedlin and Korn (2005) by considering estimation as well as testing and by simulating the primary and competing events' times from both a cause-specific hazards model as well as a joint subdistribution-cause-specific hazards model. We show that the cumulative incidence function can provide useful prognostic information for a particular patient but is not advisable for the primary efficacy analysis. Instead, it is preferable to fit a Cox model for the primary event which treats the competing event as an independent censoring. This is reasonably robust for controlling type I error and treatment effect bias with respect to the true primary and competing events' cause-specific hazards model, even when there is a shared, moderately prognostic, unobserved baseline frailty for the primary and competing events in that model. However, when it is plausible that a strongly prognostic frailty exists, combining the primary and competing events into a composite event should be considered. Finally, when there is an a priori interest in having both the primary and competing events in the primary analysis, we compare a bivariate approach for establishing overall treatment efficacy to the composite event approach. The ideas are illustrated by analyzing the Women's Health Initiative clinical trials sponsored by the National Heart, Lung, and Blood Institute.

Evaluation of proton-coupled folate transporter (SLC46A1) polymorphisms as risk factors for neural tube defects and oral clefts.

Many folate-related genes have been investigated for possible causal roles in neural tube defects (NTDs) and oral clefts. However, no previous reports have examined the major gene responsible for folate uptake, the proton-coupled folate transporter (SLC46A1). We tested for association between these birth defects and single nucleotide polymorphisms in the SLC46A1 gene. The NTD study population included 549 complete and incomplete case-family triads, and 999 controls from Ireland. The oral clefts study population comprised a sample from Utah (495 complete and incomplete case-family triads and 551 controls) and 221 Filipino multiplex cleft families. There was suggestive evidence of increased NTD case risk with the rs17719944 minor allele (odds ratio (OR): 1.29; 95% confidence intervals (CI): [1.00-1.67]), and decreased maternal risk of an NTD pregnancy with the rs4795436 minor allele (OR: 0.62; [0.39-0.99]). In the Utah sample, the rs739439 minor allele was associated with decreased case risk for cleft lip with cleft palate (genotype relative risk (GRR): 0.56 [0.32-0.98]). Additionally, the rs2239907 minor allele was associated with decreased case risk for cleft lip with cleft palate in several models, and with cleft palate only in a recessive model (OR: 0.41; [0.20-0.85]). These associations did not remain statistically significant after correcting for multiple hypothesis testing. Nominal associations between SLC46A1 polymorphisms and both Irish NTDs and oral clefts in the Utah population suggest some role in the etiology of these birth defects, but further investigation in other populations is needed.

Optimizing the order of hypotheses in serial testing of multiple endpoints in clinical trials.

Clinical trials usually collect information on a large number of variables or endpoints, including one or more primary endpoints as well as a number of secondary endpoints representing different aspects of treatment effectiveness and safety. In this article, we focus on serial testing procedures that test multiple endpoints in a pre-specified order, and consider how to optimize the order of endpoints subject to any clinical constraints, with respect to the expected number of successes (i.e., endpoints that reach statistical significance) or the expected gain (if endpoints are associated with numerical utilities). We consider some common approaches to this problem and propose two new approaches: a greedy algorithm based on conditional power and a simulated annealing algorithm that attempts to improve a given sequence in a random and iterative fashion. Simulation results indicate that the proposed algorithms are useful for finding a high-performing sequence, and that optimized fixed sequence procedures can be competitive against traditional multiple testing procedures such as Holm's. The methods and findings are illustrated with two examples concerning migraine and asthma.

Replication and exploratory analysis of 24 candidate risk polymorphisms for neural tube defects.

BACKGROUND: Neural tube defects (NTDs), which are among the most common congenital malformations, are influenced by environmental and genetic factors. Low maternal folate is the strongest known contributing factor, making variants in genes in the folate metabolic pathway attractive candidates for NTD risk. Multiple studies have identified nominally significant allelic associations with NTDs. We tested whether associations detected in a large Irish cohort could be replicated in an independent population. METHODS: Replication tests of 24 nominally significant NTD associations were performed in racially/ethnically matched populations. Family-based tests of fifteen nominally significant single nucleotide polymorphisms (SNPs) were repeated in a cohort of NTD trios (530 cases and their parents) from the United Kingdom, and case-control tests of nine nominally significant SNPs were repeated in a cohort (190 cases, 941 controls) from New York State (NYS). Secondary hypotheses involved evaluating the latter set of nine SNPs for NTD association using alternate case-control models and NTD groupings in white, African American and Hispanic cohorts from NYS. RESULTS: Of the 24 SNPs tested for replication, ADA rs452159 and MTR rs10925260 were significantly associated with isolated NTDs. Of the secondary tests performed, ARID1A rs11247593 was associated with NTDs in whites, and ALDH1A2 rs7169289 was associated with isolated NTDs in African Americans. CONCLUSIONS: We report a number of associations between SNP genotypes and neural tube defects. These associations were nominally significant before correction for multiple hypothesis testing. These corrections are highly conservative for association studies of untested hypotheses, and may be too conservative for replication studies. We therefore believe the true effect of these four nominally significant SNPs on NTD risk will be more definitively determined by further study in other populations, and eventual meta-analysis.

Association analysis of complex diseases using triads, parent-child dyads and singleton monads.

BACKGROUND: Triad families are routinely used to test association between genetic variants and complex diseases. Triad studies are important and popular since they are robust in terms of being less prone to false positives due to population structure. In practice, one may collect not only complete triads, but also incomplete families such as dyads (affected child with one parent) and singleton monads (affected child without parents). Since there is a lack of convenient algorithms and software to analyze the incomplete data, dyads and monads are usually discarded. This may lead to loss of power and insufficient utilization of genetic information in a study. RESULTS: We develop likelihood-based statistical models and likelihood ratio tests to test for association between complex diseases and genetic markers by using combinations of full triads, parent-child dyads, and affected singleton monads for a unified analysis. A likelihood is calculated directly to facilitate the data analysis without imputation and to avoid computational complexity. This makes it easy to implement the models and to explain the results. CONCLUSION: By simulation studies, we show that the proposed models and tests are very robust in terms of accurately controlling type I error evaluations, and are powerful by empirical power evaluations. The methods are applied to test for association between transforming growth factor alpha (TGFA) gene and cleft palate in an Irish study.

A prospective natural history study of post acute sequalae of COVID-19 using digital wearables: Study protocol.

Background: Post-acute sequelae of COVID-19 (PASC) is characterized by having 1 + persistent, recurrent, or emergent symptoms post the infection's acute phase. The duration and symptom manifestation of PASC remain understudied in nonhospitalized patients. Literature on PASC is primarily based on data from hospitalized patients where clinical indicators such as respiratory rate, heart rate, and oxygen saturation have been predictive of disease trajectories. Digital wearables allow for a continuous collection of such physiological parameters. This protocol outlines the design, aim, and procedures of a natural history study of PASC using digital wearables. Methods: This is a single-arm, prospective, natural history study of a cohort of 550 patients, ages 18 to 65 years old, males or females who own a smartphone and/or a tablet that meets pre-determined Bluetooth version and operating system requirements, speak English, and provide documentation of a positive COVID-19 test issued by a healthcare professional or organization within 5 days before enrollment. The study aims to identify wearables collected physiological parameters that are associated with PASC in patients with a positive diagnosis. The primary endpoint is long COVID-19, defined as ≥ 1 symptom at 3 weeks beyond first symptom onset or positive diagnosis, whichever comes first. The secondary endpoint is chronic COVID-19, defined as ≥ 1 symptom at 12 weeks beyond first symptom onset or positive diagnosis. We hypothesize that physiological parameters collected via wearables are associated with self-reported PASC. Participants must be willing and able to consent to participate in the study and adhere to study procedures for six months. Discussion: This is a fully decentralized study investigating PASC using wearable devices to collect physiological parameters and patient-reported outcomes. Given evidence on key demographics and risk profiles associated with PASC, the study will shed light on the duration and symptom manifestation of PASC in nonhospitalized patient subgroups and is an exemplar of use of wearables as population-level monitoring health tools for communicable diseases. Trial registration: ClinicalTrials.gov NCT04927442.

Lack of association between folate-receptor autoantibodies and neural-tube defects.

BACKGROUND: A previous report described the presence of autoantibodies against folate receptors in 75% of serum samples from women with a history of pregnancy complicated by a neural-tube defect, as compared with 10% of controls. We sought to confirm this finding in an Irish population, which traditionally has had a high prevalence of neural-tube defects. METHODS: We performed two studies. Study 1 consisted of analysis of stored frozen blood samples collected from 1993 through 1994 from 103 mothers with a history of pregnancy complicated by a neural-tube defect (case mothers), 103 mothers with a history of pregnancy but no complication by a neural-tube defect (matched with regard to number of pregnancies and sampling dates), 58 women who had never been pregnant, and 36 men. Study 2, conducted to confirm that the storage of samples did not influence the folate-receptor autoantibodies, included fresh samples from 37 case mothers, 22 control mothers, 10 women who had never been pregnant, and 9 men. All samples were assayed for blocking and binding autoantibodies against folate receptors. RESULTS: In Study 1, blocking autoantibodies were found in 17% of case mothers, as compared with 13% of control mothers (odds ratio, 1.54; 95% confidence interval [CI], 0.70 to 3.39), and binding autoantibodies in 29%, as compared with 32%, respectively (odds ratio, 0.82; 95% CI, 0.44 to 1.50). Study 2 showed similar results, indicating that sample degradation was unlikely. CONCLUSIONS: The presence and titer of maternal folate-receptor autoantibodies were not significantly associated with a neural-tube defect-affected pregnancy in this Irish population.

Evaluation of common genetic variants in 82 candidate genes as risk factors for neural tube defects.

BACKGROUND: Neural tube defects (NTDs) are common birth defects (~1 in 1000 pregnancies in the US and Europe) that have complex origins, including environmental and genetic factors. A low level of maternal folate is one well-established risk factor, with maternal periconceptional folic acid supplementation reducing the occurrence of NTD pregnancies by 50-70%. Gene variants in the folate metabolic pathway (e.g., MTHFR rs1801133 (677 C > T) and MTHFD1 rs2236225 (R653Q)) have been found to increase NTD risk. We hypothesized that variants in additional folate/B12 pathway genes contribute to NTD risk. METHODS: A tagSNP approach was used to screen common variation in 82 candidate genes selected from the folate/B12 pathway and NTD mouse models. We initially genotyped polymorphisms in 320 Irish triads (NTD cases and their parents), including 301 cases and 341 Irish controls to perform case-control and family based association tests. Significantly associated polymorphisms were genotyped in a secondary set of 250 families that included 229 cases and 658 controls. The combined results for 1441 SNPs were used in a joint analysis to test for case and maternal effects. RESULTS: Nearly 70 SNPs in 30 genes were found to be associated with NTDs at the p < 0.01 level. The ten strongest association signals (p-value range: 0.0003-0.0023) were found in nine genes (MFTC, CDKN2A, ADA, PEMT, CUBN, GART, DNMT3A, MTHFD1 and T (Brachyury)) and included the known NTD risk factor MTHFD1 R653Q (rs2236225). The single strongest signal was observed in a new candidate, MFTC rs17803441 (OR = 1.61 [1.23-2.08], p = 0.0003 for the minor allele). Though nominally significant, these associations did not remain significant after correction for multiple hypothesis testing. CONCLUSIONS: To our knowledge, with respect to sample size and scope of evaluation of candidate polymorphisms, this is the largest NTD genetic association study reported to date. The scale of the study and the stringency of correction are likely to have contributed to real associations failing to survive correction. We have produced a ranked list of variants with the strongest association signals. Variants in the highest rank of associations are likely to include true associations and should be high priority candidates for further study of NTD risk.

Causal inference on quantiles with an obstetric application.

The current statistical literature on causal inference is primarily concerned with population means of potential outcomes, while the current statistical practice also involves other meaningful quantities such as quantiles. Motivated by the Consortium on Safe Labor (CSL), a large observational study of obstetric labor progression, we propose and compare methods for estimating marginal quantiles of potential outcomes as well as quantiles among the treated. By adapting existing methods and techniques, we derive estimators based on outcome regression (OR), inverse probability weighting, and stratification, as well as a doubly robust (DR) estimator. By incorporating stratification into the DR estimator, we further develop a hybrid estimator with enhanced numerical stability at the expense of a slight bias under misspecification of the OR model. The proposed methods are illustrated with the CSL data and evaluated in simulation experiments mimicking the CSL.

Methods of adjusted comparison of medians.

Consider the problem of making an adjusted comparison of the medians of two populations on an interval type outcome variable. A common method of doing this is through the use of a linear model requiring the residuals to be normally distributed. We describe here two methods based on a linear model after Box-Cox transformation of the outcome variable. The methods require a reference population, which could be either of the populations under study or their aggregate. We compare the new procedures with the comparison of normal means procedure and other procedures proposed for this problem by simulation. It is found that the procedure based on comparison of the predicted values obtained from the observed covariates of the reference population has higher power for testing and smaller mean square error of estimation than the other methods, while maintaining reasonable control of the type I error rate. We illustrate the methods by analyzing the duration of the second stage of labor for women in two large observation studies (Collaborative Perinatal Project and Consortium on Safe Labor) separated by 50 years. We recommend the method based on comparison of the predicted values of the transformed outcomes, with careful attention to how close the resulting residual distribution is to normal.

Correction for multiplicity in genetic association studies of triads: the permutational TDT.

New technology for large-scale genotyping has created new challenges for statistical analysis. Correcting for multiple comparison without discarding true positive results and extending methods to triad studies are among the important problems facing statisticians. We present a one-sample permutation test for testing transmission disequilibrium hypotheses in triad studies, and show how this test can be used for multiple single nucleotide polymorphism (SNP) testing. The resulting multiple comparison procedure is shown in the case of the transmission disequilibrium test to control the familywise error. Furthermore, this procedure can handle multiple possible modes of risk inheritance per SNP. The resulting permutational procedure is shown through simulation of SNP data to be more powerful than the Bonferroni procedure when the SNPs are in linkage disequilibrium. Moreover, permutations implicitly avoid any multiple comparison correction penalties when the SNP has a rare allele. The method is illustrated by analyzing a large candidate gene study of neural tube defects and an independent study of oral clefts, where the smallest adjusted p-values using the permutation procedure are approximately half those of the Bonferroni procedure. We conclude that permutation tests are more powerful for identifying disease-associated SNPs in candidate gene studies and are useful for analysis of triad studies.

Evaluation of 64 candidate single nucleotide polymorphisms as risk factors for neural tube defects in a large Irish study population.

Individual studies of the genetics of neural tube defects (NTDs) contain results on a small number of genes in each report. To identify genetic risk factors for NTDs, we evaluated potentially functional single nucleotide polymorphisms (SNPs) that are biologically plausible risk factors for NTDs but that have never been investigated for an association with NTDs, examined SNPs that previously showed no association with NTDs in published studies, and tried to confirm previously reported associations in folate-related and non-folate-related genes. We investigated 64 SNPs in 34 genes for association with spina bifida in up to 558 case families (520 cases, 507 mothers, 457 fathers) and 994 controls in Ireland. Case-control and mother-control comparisons of genotype frequencies, tests of transmission disequilibrium, and log-linear regression models were used to calculate effect estimates. Spina bifida was associated with over-transmission of the LEPR (leptin receptor) rs1805134 minor C allele [genotype relative risk (GRR): 1.5; 95% confidence interval (CI): 1.0-2.1; P = 0.0264] and the COMT (catechol-O-methyltransferase) rs737865 major T allele (GRR: 1.4; 95% CI: 1.1-2.0; P = 0.0206). After correcting for multiple comparisons, these individual test P-values exceeded 0.05. Consistent with previous reports, spina bifida was associated with MTHFR 677C>T, T (Brachyury) rs3127334, LEPR K109R, and PDGFRA promoter haplotype combinations. The associations between LEPR SNPs and spina bifida suggest a possible mechanism for the finding that obesity is a NTD risk factor. The association between a variant in COMT and spina bifida implicates methylation and epigenetics in NTDs.

Permutational Multiple Testing Adjustments With Multivariate Multiple Group Data.

We consider the multiple comparison problem where multiple outcomes are each compared among several different collections of groups in a multiple group setting. In this case there are several different types of hypotheses, with each specifying equality of the distributions of a single outcome over a different collection of groups. Each type of hypothesis requires a different permutational approach. We show that under a certain multivariate condition it is possible to use closure over all hypotheses, although intersection hypotheses are tested using Boole's inequality in conjunction with permutation distributions in some cases. Shortcut tests are then found so that the resulting testing procedure is easily performed. The error rate and power of the new method is compared to existing competitors through simulation of correlated data. An example is analyzed, consisting of multiple adverse events in a clinical trial.

Geospatial Analysis of Neighborhood Environmental Stress in Relation to Biological Markers of Cardiovascular Health and Health Behaviors in Women: Protocol for a Pilot Study.

BACKGROUND: Innovative analyses of cardiovascular (CV) risk markers and health behaviors linked to neighborhood stressors are essential to further elucidate the mechanisms by which adverse neighborhood social conditions lead to poor CV outcomes. We propose to objectively measure physical activity (PA), sedentary behavior, and neighborhood stress using accelerometers, GPS, and real-time perceived ecological momentary assessment via smartphone apps and to link these to biological measures in a sample of White and African American women in Washington, DC, neighborhoods. OBJECTIVE: The primary aim of this study is to test the hypothesis that living in adverse neighborhood social conditions is associated with higher stress-related neural activity among 60 healthy women living in high or low socioeconomic status neighborhoods in Washington, DC. Sub-aim 1 of this study is to test the hypothesis that the association is moderated by objectively measured PA using an accelerometer. A secondary objective is to test the hypothesis that residing in adverse neighborhood social environment conditions is related to differences in vascular function. Sub-aim 2 of this study is to test the hypothesis that the association is moderated by objectively measured PA. The third aim of this study is to test the hypothesis that adverse neighborhood social environment conditions are related to differences in immune system activation. METHODS: The proposed study will be cross-sectional, with a sample of at least 60 women (30 healthy White women and 30 healthy Black women) from Wards 3 and 5 in Washington, DC. A sample of the women (n=30) will be recruited from high-income areas in Ward 3 from census tracts within a 15% of Ward 3's range for median household income. The other participants (n=30) will be recruited from low-income areas in Wards 5 from census tracts within a 15% of Ward 5's range for median household income. Finally, participants from Wards 3 and 5 will be matched based on age, race, and BMI. Participants will wear a GPS unit and accelerometer and report their stress and mood in real time using a smartphone. We will then examine the associations between GPS-derived neighborhood variables, stress-related neural activity measures, and adverse biological markers. RESULTS: The National Institutes of Health Institutional Review Board has approved this study. Recruitment will begin in the summer of 2021. CONCLUSIONS: Findings from this research could inform the development of multilevel behavioral interventions and policies to better manage environmental factors that promote immune system activation or psychosocial stress while concurrently working to increase PA, thereby influencing CV health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/29191.