Using Power Analysis to Choose the Unit of Randomization, Outcome, and Approach for Subgroup Analysis for a Multilevel Randomized Controlled Clinical Trial to Reduce Disparities in Cardiovascular Health.

We give examples of three features in the design of randomized controlled clinical trials which can increase power and thus decrease sample size and costs. We consider an example multilevel trial with several levels of clustering. For a fixed number of independent sampling units, we show that power can vary widely with the choice of the level of randomization. We demonstrate that power and interpretability can improve by testing a multivariate outcome rather than an unweighted composite outcome. Finally, we show that using a pooled analytic approach, which analyzes data for all subgroups in a single model, improves power for testing the intervention effect compared to a stratified analysis, which analyzes data for each subgroup in a separate model. The power results are computed for a proposed prevention research study. The trial plans to randomize adults to either telehealth (intervention) or in-person treatment (control) to reduce cardiovascular risk factors. The trial outcomes will be measures of the Essential Eight, a set of scores for cardiovascular health developed by the American Heart Association which can be combined into a single composite score. The proposed trial is a multilevel study, with outcomes measured on participants, participants treated by the same provider, providers nested within clinics, and clinics nested within hospitals. Investigators suspect that the intervention effect will be greater in rural participants, who live farther from clinics than urban participants. The results use published, exact analytic methods for power calculations with continuous outcomes. We provide example code for power analyses using validated software.

Stratified split sampling of electronic health records.

Although superficially similar to data from clinical research, data extracted from electronic health records may require fundamentally different approaches for model building and analysis. Because electronic health record data is designed for clinical, rather than scientific use, researchers must first provide clear definitions of outcome and predictor variables. Yet an iterative process of defining outcomes and predictors, assessing association, and then repeating the process may increase Type I error rates, and thus decrease the chance of replicability, defined by the National Academy of Sciences as the chance of "obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data."[1] In addition, failure to account for subgroups may mask heterogeneous associations between predictor and outcome by subgroups, and decrease the generalizability of the findings. To increase chances of replicability and generalizability, we recommend using a stratified split sample approach for studies using electronic health records. A split sample approach divides the data randomly into an exploratory set for iterative variable definition, iterative analyses of association, and consideration of subgroups. The confirmatory set is used only to replicate results found in the first set. The addition of the word 'stratified' indicates that rare subgroups are oversampled randomly by including them in the exploratory sample at higher rates than appear in the population. The stratified sampling provides a sufficient sample size for assessing heterogeneity of association by testing for effect modification by group membership. An electronic health record study of the associations between socio-demographic factors and uptake of hepatic cancer screening, and potential heterogeneity of association in subgroups defined by gender, self-identified race and ethnicity, census-tract level poverty and insurance type illustrates the recommended approach.

Power and sample size analysis for longitudinal mixed models of health in populations exposed to environmental contaminants: a tutorial.

BACKGROUND: When evaluating the impact of environmental exposures on human health, study designs often include a series of repeated measurements. The goal is to determine whether populations have different trajectories of the environmental exposure over time. Power analyses for longitudinal mixed models require multiple inputs, including clinically significant differences, standard deviations, and correlations of measurements. Further, methods for power analyses of longitudinal mixed models are complex and often challenging for the non-statistician. We discuss methods for extracting clinically relevant inputs from literature, and explain how to conduct a power analysis that appropriately accounts for longitudinal repeated measures. Finally, we provide careful recommendations for describing complex power analyses in a concise and clear manner. METHODS: For longitudinal studies of health outcomes from environmental exposures, we show how to [1] conduct a power analysis that aligns with the planned mixed model data analysis, [2] gather the inputs required for the power analysis, and [3] conduct repeated measures power analysis with a highly-cited, validated, free, point-and-click, web-based, open source software platform which was developed specifically for scientists. RESULTS: As an example, we describe the power analysis for a proposed study of repeated measures of per- and polyfluoroalkyl substances (PFAS) in human blood. We show how to align data analysis and power analysis plan to account for within-participant correlation across repeated measures. We illustrate how to perform a literature review to find inputs for the power analysis. We emphasize the need to examine the sensitivity of the power values by considering standard deviations and differences in means that are smaller and larger than the speculated, literature-based values. Finally, we provide an example power calculation and a summary checklist for describing power and sample size analysis. CONCLUSIONS: This paper provides a detailed roadmap for conducting and describing power analyses for longitudinal studies of environmental exposures. It provides a template and checklist for those seeking to write power analyses for grant applications.

Internal pilot design for balanced repeated measures.

Repeated measures are common in clinical trials and epidemiological studies. Designing studies with repeated measures requires reasonably accurate specifications of the variances and correlations to select an appropriate sample size. Underspecifying the variances leads to a sample size that is inadequate to detect a meaningful scientific difference, while overspecifying the variances results in an unnecessarily large sample size. Both lead to wasting resources and placing study participants in unwarranted risk. An internal pilot design allows sample size recalculation based on estimates of the nuisance parameters in the covariance matrix. We provide the theoretical results that account for the stochastic nature of the final sample size in a common class of linear mixed models. The results are useful for designing studies with repeated measures and balanced design. Simulations examine the impact of misspecification of the covariance matrix and demonstrate the accuracy of the approximations in controlling the type I error rate and achieving the target power. The proposed methods are applied to a longitudinal study assessing early antiretroviral therapy for youth living with HIV.

Multivariate test power approximations for balanced linear mixed models in studies with missing data.

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd.

Quality of Care for Chronic Conditions Among Disabled Medicaid Enrollees: An Evaluation of a 1915 (b) and (c) Waiver Program.

IMPORTANCE: Examining the impact of Medicaid-managed care home-based and community-based service (HCBS) alternatives to institutional care is critical given the recent rapid expansion of these models nationally. OBJECTIVE: We analyzed the effects of STAR+PLUS, a Texas Medicaid-managed care HCBS waiver program for adults with disabilities on the quality of chronic disease care. DESIGN, SETTING, AND PARTICIPANTS: We compared quality before and after a mandatory transition of disabled Medicaid enrollees older than 21 years from fee-for-service (FFS) or primary care case management (PCCM) to STAR+PLUS in 28 counties, relative to enrollees in counties remaining in the FFS or PCCM models. MEASURES AND ANALYSIS: Person-level claims and encounter data for 2006-2010 were used to compute adherence to 6 quality measures. With county as the independent sampling unit, we employed a longitudinal linear mixed-model analysis accounting for administrative clustering and geographic and individual factors. RESULTS: Although quality was similar among programs at baseline, STAR+PLUS enrollees experienced large and sustained improvements in use of ß-blockers after discharge for heart attack (49% vs. 81% adherence posttransition; P<0.01) and appropriate use of systemic corticosteroids and bronchodilators after a chronic obstructive pulmonary disease event (39% vs. 68% adherence posttransition; P<0.0001) compared with FFS/PCCM enrollees. No statistically significant effects were identified for quality measures for asthma, diabetes, or cardiovascular disease. CONCLUSION: In 1 large Medicaid-managed care HCBS program, the quality of chronic disease care linked to acute events improved while that provided during routine encounters appeared unaffected.

Determinants of First-Time Cancer Examinations in a Rural Community: A Mechanism for Behavior Change.

OBJECTIVES: After conducting a media campaign focusing on the importance of oral and pharyngeal cancer (OPC) examinations, we assessed mechanisms of behavior change among individuals receiving an OPC examination for the first time. METHODS: We used data from 2 waves of telephone surveys of individuals residing in 36 rural census tracts in northern Florida (n = 806). The second survey occurred after our media intervention. We developed media messages and modes of message delivery with community members via focus groups and intercept interviews. We performed a mediation analysis to examine behavior change mechanisms. RESULTS: Greater exposure to media messages corresponded with heightened concern about OPC. Heightened concern, in turn, predicted receipt of a first-time OPC examination, but only among men. CONCLUSIONS: We extended earlier studies by measuring an outcome behavior (receipt of an OPC examination) and demonstrating that the putative mechanism of action (concern about the disease) explained the link between a media intervention and engaging in the target behavior. Improving the quality of media campaigns by engaging community stakeholders in selecting messages and delivery methods is an effective strategy in building public health interventions aimed at changing behaviors.

Recommendations for choosing an analysis method that controls Type I error for unbalanced cluster sample designs with Gaussian outcomes.

We used theoretical and simulation-based approaches to study Type I error rates for one-stage and two-stage analytic methods for cluster-randomized designs. The one-stage approach uses the observed data as outcomes and accounts for within-cluster correlation using a general linear mixed model. The two-stage model uses the cluster specific means as the outcomes in a general linear univariate model. We demonstrate analytically that both one-stage and two-stage models achieve exact Type I error rates when cluster sizes are equal. With unbalanced data, an exact size α test does not exist, and Type I error inflation may occur. Via simulation, we compare the Type I error rates for four one-stage and six two-stage hypothesis testing approaches for unbalanced data. With unbalanced data, the two-stage model, weighted by the inverse of the estimated theoretical variance of the cluster means, and with variance constrained to be positive, provided the best Type I error control for studies having at least six clusters per arm. The one-stage model with Kenward-Roger degrees of freedom and unconstrained variance performed well for studies having at least 14 clusters per arm. The popular analytic method of using a one-stage model with denominator degrees of freedom appropriate for balanced data performed poorly for small sample sizes and low intracluster correlation. Because small sample sizes and low intracluster correlation are common features of cluster-randomized trials, the Kenward-Roger method is the preferred one-stage approach.

Health literacy: a pathway to better oral health.

OBJECTIVES: We examined whether health literacy was associated with self-rated oral health status and whether the relationship was mediated by patient-dentist communication and dental care patterns. METHODS: We tested a path model with data collected from 2 waves of telephone surveys (baseline, 2009-2010; follow-up, 2011) of individuals residing in 36 rural census tracts in northern Florida (final sample size n = 1799). RESULTS: Higher levels of health literacy were associated with better self-rated oral health status (B = 0.091; P < .001). In addition, higher levels of health literacy were associated with better patient-dentist communication, which in turn corresponded with patterns of regular dental care and better self-rated oral health (B = 0.003; P = .01). CONCLUSIONS: Our study showed that, beyond the often-reported effects of gender, race, education, financial status, and access to dental care, it is also important to consider the influence of health literacy and quality of patient-dentist communication on oral health status. Improved patient-dentist communication is needed as an initial step in improving the population's oral health.

Drug-eluting stents may not reduce target lesion revascularization in cardiac allograft vasculopathy.

OBJECTIVE: Although drug-eluting stent (DES) compared with bare metal stent (BMS) use reduces in-stent restenosis (ISR) in traditional coronary artery disease, its efficacy in cardiac allograft vasculopathy (CAV) has not been clearly established. BACKGROUND: CAV is a leading cause of mortality after the first year following cardiac transplantation. CAV treatment options are limited, and DES use has increased significantly in this population. METHODS: In a retrospective study of heart transplant patients at our institution who underwent percutaneous coronary intervention with a BMS or DES for CAV, we compared baseline characteristics, clinical outcomes, ISR, and target lesion revascularization (TLR). The primary end-point was angiographic ISR assessed by quantitative coronary angiography analyzed as both a binary (≤50% vs. >50%) and continuous variable (follow-up minimal luminal area [MLA]/baseline MLA). Secondary outcomes included TLR and a composite of death, myocardial infarction, heart failure, and retransplantation. RESULTS: In 45 patients with DES, BMS, or both, ISR assessed as a continuous variable was statistically different between the 2 stent groups (follow-up MLA/baseline MLA = 0.796 DES vs. 0.481 BMS; P = 0.0037). There was also a significant difference in ISR (10.8% for DES versus 30.7% for BMS) when assessed as a binary variable. There was no statistically significant difference in TLR or composite cardiovascular outcomes between groups when adjusted for traditional cardiovascular risk factors. CONCLUSIONS: ISR assessed as a continuous variable was significantly different between stent groups. However, this did not lead to a difference in TLR or cardiovascular outcomes. This hypothesis-generating finding suggests that patients with CAV may not necessarily need treatment with DES, which can be more costly and carries more potential risk than BMS.

Power calculation for overall hypothesis testing with high-dimensional commensurate outcomes.

The complexity of system biology means that any metabolic, genetic, or proteomic pathway typically includes so many components (e.g., molecules) that statistical methods specialized for overall testing of high-dimensional and commensurate outcomes are required. While many overall tests have been proposed, very few have power and sample size methods. We develop accurate power and sample size methods and software to facilitate study planning for high-dimensional pathway analysis. With an account of any complex correlation structure between high-dimensional outcomes, the new methods allow power calculation even when the sample size is less than the number of variables. We derive the exact (finite-sample) and approximate non-null distributions of the 'univariate' approach to repeated measures test statistic, as well as power-equivalent scenarios useful to generalize our numerical evaluations. Extensive simulations of group comparisons support the accuracy of the approximations even when the ratio of number of variables to sample size is large. We derive a minimum set of constants and parameters sufficient and practical for power calculation. Using the new methods and specifying the minimum set to determine power for a study of metabolic consequences of vitamin B6 deficiency helps illustrate the practical value of the new results. Free software implementing the power and sample size methods applies to a wide range of designs, including one group pre-intervention and post-intervention comparisons, multiple parallel group comparisons with one-way or factorial designs, and the adjustment and evaluation of covariate effects.

Analytic methods for individually randomized group treatment trials and group-randomized trials when subjects belong to multiple groups.

Participants in trials may be randomized either individually or in groups and may receive their treatment either entirely individually, entirely in groups, or partially individually and partially in groups. This paper concerns cases in which participants receive their treatment either entirely or partially in groups, regardless of how they were randomized. Participants in group-randomized trials are randomized in groups, and participants in individually randomized group treatment trials are individually randomized, but participants in both types of trials receive part or all of their treatment in groups or through common change agents. Participants who receive part or all of their treatment in a group are expected to have positively correlated outcome measurements. This paper addresses a situation that occurs in group-randomized trials and individually randomized group treatment trials-participants receive treatment through more than one group. As motivation, we consider trials in The Childhood Obesity Prevention and Treatment Research Consortium, in which each child participant receives treatment in at least two groups. In simulation studies, we considered several possible analytic approaches over a variety of possible group structures. A mixed model with random effects for both groups provided the only consistent protection against inflated type I error rates and did so at the cost of only moderate loss of power when intraclass correlations were not large. We recommend constraining variance estimates to be positive and using the Kenward-Roger adjustment for degrees of freedom; this combination provided additional power but maintained type I error rates at the nominal level.

Reducing decision errors in the paired comparison of the diagnostic accuracy of screening tests with Gaussian outcomes.

BACKGROUND: Scientists often use a paired comparison of the areas under the receiver operating characteristic curves to decide which continuous cancer screening test has the best diagnostic accuracy. In the paired design, all participants are screened with both tests. Participants with suspicious results or signs and symptoms of disease receive the reference standard test. The remaining participants are classified as non-cases, even though some may have occult disease. The standard analysis includes all study participants, which can create bias in the estimates of diagnostic accuracy since not all participants receive disease status verification. We propose a weighted maximum likelihood bias correction method to reduce decision errors. METHODS: Using Monte Carlo simulations, we assessed the method's ability to reduce decision errors across a range of disease prevalences, correlations between screening test scores, rates of interval cases and proportions of participants who received the reference standard test. RESULTS: The performance of the method depends on characteristics of the screening tests and the disease and on the percentage of participants who receive the reference standard test. In studies with a large amount of bias in the difference in the full areas under the curves, the bias correction method reduces the Type I error rate and improves power for the correct decision. We demonstrate the method with an application to a hypothetical oral cancer screening study. CONCLUSION: The bias correction method reduces decision errors for some paired screening trials. In order to determine if bias correction is needed for a specific screening trial, we recommend the investigator conduct a simulation study using our software.

Metabolite profile analysis reveals functional effects of 28-day vitamin B-6 restriction on one-carbon metabolism and tryptophan catabolic pathways in healthy men and women.

Suboptimal vitamin B-6 status, as reflected by low plasma pyridoxal 5'-phosphate (PLP) concentration, is associated with increased risk of vascular disease. PLP plays many roles, including in one-carbon metabolism for the acquisition and transfer of carbon units and in the transsulfuration pathway. PLP also serves as a coenzyme in the catabolism of tryptophan. We hypothesize that the pattern of these metabolites can provide information reflecting the functional impact of marginal vitamin B-6 deficiency. We report here the concentration of major constituents of one-carbon metabolic processes and the tryptophan catabolic pathway in plasma from 23 healthy men and women before and after a 28-d controlled dietary vitamin B-6 restriction (<0.35 mg/d). liquid chromatography-tandem mass spectrometry analysis of the compounds relevant to one-carbon metabolism showed that vitamin B-6 restriction yielded increased cystathionine (53% pre- and 76% postprandial; P < 0.0001) and serine (12% preprandial; P < 0.05), and lower creatine (40% pre- and postprandial; P < 0.0001), creatinine (9% postprandial; P < 0.05), and dimethylglycine (16% postprandial; P < 0.05) relative to the vitamin B-6-adequate state. In the tryptophan pathway, vitamin B-6 restriction yielded lower kynurenic acid (22% pre- and 20% postprandial; P < 0.01) and higher 3-hydroxykynurenine (39% pre- and 34% postprandial; P < 0.01). Multivariate ANOVA analysis showed a significant global effect of vitamin B-6 restriction and multilevel partial least squares-discriminant analysis supported this conclusion. Thus, plasma concentrations of creatine, cystathionine, kynurenic acid, and 3-hydroxykynurenine jointly reveal effects of vitamin B-6 restriction on the profiles of one-carbon and tryptophan metabolites and serve as biomarkers of functional effects of marginal vitamin B-6 deficiency.

Increasing screening intentions for oral and pharyngeal cancer.

BACKGROUND: Oral and pharyngeal cancer is a serious health threat that goes unnoticed by most people. Increasing screenings for oral and pharyngeal cancer is essential to achieving early detection when the disease is most treatable. PURPOSE: We tested the effectiveness of a media campaign designed to increase intentions to seek an oral and pharyngeal cancer screening. We further examined whether concern and knowledge of oral and pharyngeal cancer mediated screening intentions. METHODS: Participants in the intervention condition received messages on posters, handheld fans, pamphlets, and magnets displayed on the sides of cars or trucks. Participants in the intervention and comparison conditions (N = 1,790) were surveyed prior to and after the intervention. RESULTS: Intervention participants reported greater intentions to seek free oral and pharyngeal cancer screenings. Concern about oral and pharyngeal cancer partially mediated the effect whereas knowledge did not. CONCLUSIONS: Our media campaign successfully increased screening intentions by heightening concerns.

Confidence regions for repeated measures ANOVA power curves based on estimated covariance.

BACKGROUND: Using covariance or mean estimates from previous data introduces randomness into each power value in a power curve. Creating confidence intervals about the power estimates improves study planning by allowing scientists to account for the uncertainty in the power estimates. Driving examples arise in many imaging applications. METHODS: We use both analytical and Monte Carlo simulation methods. Our analytical derivations apply to power for tests with the univariate approach to repeated measures (UNIREP). Approximate confidence intervals and regions for power based on an estimated covariance matrix and fixed means are described. Extensive simulations are used to examine the properties of the approximations. RESULTS: Closed-form expressions are given for approximate power and confidence intervals and regions. Monte Carlo simulations support the accuracy of the approximations for practical ranges of sample size, rank of the design matrix, error degrees of freedom, and the amount of deviation from sphericity. The new methods provide accurate coverage probabilities for all four UNIREP tests, even for small sample sizes. Accuracy is higher for higher power values than for lower power values, making the methods especially useful in practical research conditions. The new techniques allow the plotting of power confidence regions around an estimated power curve, an approach that has been well received by researchers. Free software makes the new methods readily available. CONCLUSIONS: The new techniques allow a convenient way to account for the uncertainty of using an estimated covariance matrix in choosing a sample size for a repeated measures ANOVA design. Medical imaging and many other types of healthcare research often use repeated measures ANOVA.

Selecting a sample size for studies with repeated measures.

Many researchers favor repeated measures designs because they allow the detection of within-person change over time and typically have higher statistical power than cross-sectional designs. However, the plethora of inputs needed for repeated measures designs can make sample size selection, a critical step in designing a successful study, difficult. Using a dental pain study as a driving example, we provide guidance for selecting an appropriate sample size for testing a time by treatment interaction for studies with repeated measures. We describe how to (1) gather the required inputs for the sample size calculation, (2) choose appropriate software to perform the calculation, and (3) address practical considerations such as missing data, multiple aims, and continuous covariates.

GLIMMPSE: Online Power Computation for Linear Models with and without a Baseline Covariate.

GLIMMPSE is a free, web-based software tool that calculates power and sample size for the general linear multivariate model with Gaussian errors (http://glimmpse.SampleSizeShop.org/). GLIMMPSE provides a user-friendly interface for the computation of power and sample size. We consider models with fixed predictors, and models with fixed predictors and a single Gaussian covariate. Validation experiments demonstrate that GLIMMPSE matches the accuracy of previously published results, and performs well against simulations. We provide several online tutorials based on research in head and neck cancer. The tutorials demonstrate the use of GLIMMPSE to calculate power and sample size.

Power for balanced linear mixed models with complex missing data processes.

When designing repeated measures studies, both the amount and the pattern of missing outcome data can affect power. The chance that an observation is missing may vary across measurements, and missingness may be correlated across measurements. For example, in a physiotherapy study of patients with Parkinson's disease, increasing intermittent dropout over time yielded missing measurements of physical function. In this example, we assume data are missing completely at random, since the chance that a data point was missing appears to be unrelated to either outcomes or covariates. For data missing completely at random, we propose noncentral F power approximations for the Wald test for balanced linear mixed models with Gaussian responses. The power approximations are based on moments of missing data summary statistics. The moments were derived assuming a conditional linear missingness process. The approach provides approximate power for both complete-case analyses, which include independent sampling units where all measurements are present, and observed-case analyses, which include all independent sampling units with at least one measurement. Monte Carlo simulations demonstrate the accuracy of the method in small samples. We illustrate the utility of the method by computing power for proposed replications of the Parkinson's study.

Marginal vitamin B-6 deficiency decreases plasma (n-3) and (n-6) PUFA concentrations in healthy men and women.

Previous animal studies showed that severe vitamin B-6 deficiency altered fatty acid profiles of tissue lipids, often with an increase of linoleic acid and a decrease of arachidonic acid. However, little is known about the extent to which vitamin B-6 deficiency affects human fatty acid profiles. The aim of this study was to determine the effects of marginal vitamin B-6 deficiency on fatty acid profiles in plasma, erythrocytes, and peripheral blood mononuclear cells (PBMC) of healthy adults fed a 28-d, low-vitamin B-6 diet. Healthy participants (n = 23) received a 2-d, controlled, vitamin B-6-adequate diet followed by a 28-d, vitamin B-6-restricted diet to induce a marginal deficiency. Plasma HDL and LDL cholesterol concentrations, FFA concentrations, and erythrocyte and PBMC membrane fatty acid compositions did not significantly change from baseline after the 28-d restriction. Plasma total arachidonic acid, EPA, and DHA concentrations decreased from (mean ± SD) 548 ± 96 to 490 ± 94 µmol/L, 37 ± 13 to 32 ± 13 µmol/L, and 121 ± 28 to 109 ± 28 µmol/L [positive false discovery rate (pFDR) adjusted P < 0.05], respectively. The total (n-6):(n-3) PUFA ratio in plasma exhibited a minor increase from 15.4 ± 2.8 to 16.6 ± 3.1 (pFDR adjusted P < 0.05). These data indicate that short-term vitamin B-6 restriction decreases plasma (n-3) and (n-6) PUFA concentrations and tends to increase the plasma (n-6):(n-3) PUFA ratio. Such changes in blood lipids may be associated with the elevated risk of cardiovascular disease in vitamin B-6 insufficiency.