229 research outputs found

    Selecting Differentially Expressed Genes from Microarray Experiments

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    High throughput technologies, such as gene expression arrays and protein mass spectrometry, allow one to simultaneously evaluate thousands of potential biomarkers that distinguish different tissue types. Of particular interest here is cancer versus normal organ tissues. We consider statistical methods to rank genes (or proteins) in regards to differential expression between tissues. Various statistical measures are considered and we argue that two measures related to the Receiver Operating Characteristic Curve are particularly suitable for this purpose. We also propose that sampling variability in the gene rankings be quantified and suggest using the “selection probability function”, the probability distribution of rankings for each gene. This is estimated via the bootstrap. A real data set derived from gene expression arrays of 23 normal and 30 ovarian cancer tissues are analyzed. Simulation studies are also used to assess the relative performance of different statistical gene ranking measures and our quantification of sampling variability. Our approach leads naturally to a procedure for sample size calculations appropriate for exploratory studies that seek to identify differentially expressed genes

    Recommendation to Use Exact P-values in Biomarker Discovery Research

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    Background: In biomarker discovery studies, markers are ranked for validation using P-values. Standard P-value calculations use normal approximations that may not be valid for small P-values and small sample sizes common in discovery research. Methods: We compared exact P-values, valid by definition, with normal and logit-normal approximations in a simulated study of 40 cases and 160 controls. The key measure of biomarker performance was sensitivity at 90% specificity. Data for 3000 uninformative markers and 30 true markers were generated randomly, with 10 replications of the simulation. We also analyzed real data on 2371 antibody array markers measured in plasma from 121 cases with ER/PR positive breast cancer and 121 controls. Results: Using the same discovery criterion, the valid exact P-values lead to discovery of 24 true and 82 false biomarkers while approximate P-values yielded 15 true and 15 false biomarkers (normal approximation) and 20 true and 86 false biomarkers (logit-normal approximation). Moreover, the estimated numbers of true markers among those discovered were substantially incorrect for approximate P-values: normal estimated 0 true markers discovered but found 15; logit-normal estimated 42 but found 20. The exact method estimated 22, close to the actual number of 24 true discoveries. With real data, exact and approximate P-values ranked candidate breast cancer biomarkers very differently. Conclusions: Exact P-values should be used because they are universally valid. Approximate P-values can lead to inappropriate biomarker selection rules and incorrect conclusions. Impact: Rigorous data analysis methodology in discovery research may improve the yield of biomarkers that validate clinically

    Use of alternative time scales in Cox proportional hazard models: implications for time-varying environmental exposures

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    Issues surrounding choice of time scales in Cox proportional hazard regression models have received limited attention in the literature. Although the choice between time on study and ‘attained’ age time scales has been examined, the calendar time scale may be of interest when modeling health effects of environmental exposures with noteworthy secular trends such as ambient particulate matter air pollution in large epidemiological cohort studies. The authors use simulation studies to examine performance (bias, mean squared error, coverage probabilities, and power) of models using all three time scales when the primary exposure of interest depends on calendar time. Results show that performance of models fit to the calendar time scale varies inversely with the strength of the linear association between the time-varying primary exposure and calendar time. Although models fit to attained age and time on study that do not adjust for calendar time were relatively robust, the authors conclude that care should be exercised when using time scales that are highly correlated with exposures of interest

    Effects of personal characteristics on serum CA125, mesothelin, and HE4 levels in healthy postmenopausal women at high-risk for ovarian cancer.

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    OBJECTIVE: To evaluate if serum levels of candidate ovarian cancer biomarkers vary with individual characteristics of healthy women who are likely candidates for an ovarian cancer screening program. METHODS: We analyzed serum CA125, mesothelin, and HE4 levels in a sample of 155 healthy postmenopausal women at increased risk for developing ovarian cancer based on personal and family cancer history. Information on reproductive, family and medical histories, lifestyle factors, and anthropometry was collected by self-report. Twenty-two factors were examined using univariate and multiple linear regression models for the three biomarker levels. RESULTS: In the multivariate models, CA125 levels were significantly higher in women who had used talcum powder (P = 0.02) and were lower in women who were parous (P = 0.05). Mesothelin levels were significantly higher in older women (P = 0.01) and lower in heavier women (P = 0.03). HE4 levels were higher in older women (P = 0.001) and in women who began menstruating at an older age (P = 0.03). CONCLUSIONS: CA125, mesothelin, and HE4 levels in healthy, postmenopausal women at increased risk for ovarian cancer are significantly associated with a few ovarian cancer risk factors. Since the effects of these personal characteristics on these serum markers are not large, their incorporation in screening algorithms may be unnecessary. This is true especially if a longitudinal algorithm is used because the marker level at the previous screen reflects personal characteristics such as age, body mass index, and age of menarche. Understanding the influence of personal factors on levels of novel early detection markers in healthy, unaffected women may have clinical utility in interpreting biomarker levels

    Ambient Fine Particulate Matter Exposure and Myocardial Ischemia in the Environmental Epidemiology of Arrhythmogenesis in the Women’s Health Initiative (EEAWHI) Study

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    BackgroundAmbient particulate matter (PM) air pollution is associated with coronary heart disease, but the pathways underlying the association remain to be elucidated.MethodsWe studied the association between PM and ischemia among 57,908 Women’s Health Initiative clinical trial participants from 1999–2003. We used the Minnesota Code criteria to identify ST-segment and T-wave abnormalities, and estimated T amplitude (microvolt) from resting, standard 12-lead electrocardiogram (ECG). We used U.S. Environmental Protection Agency’s monitor data to estimate concentrations of PM < 2.5 ÎŒm (PM2.5) at geocoded participant addresses over 6 days before the ECGs (lag0 through lag5). We excluded 2,379 women with ECG QRS duration ≄ 120 msec.ResultsOverall, 6% of the remaining 55,529 women (52–90 years of age; 83% non-Hispanic white) had ST abnormalities and 16% had T abnormalities. Lead-specific T amplitude was normally distributed (range of means from −14 to 349 ÎŒV). PM2.5 (mean ± SD) averaged over lag0–2 was 14 ± 7 ÎŒg/m3. In logistic and linear regression models adjusted for demographic, clinical, temporal, and climatic factors, a 10-ÎŒg/m3 increase in lag0–2 PM2.5 was associated with a 4% [95% confidence interval (CI), −3%, to 10%] increase in the odds of ST abnormality and a 5% (95% CI, 0% to 9%) increase in the odds of T abnormality. We observed corresponding decreases in T amplitude in all exam sites and leads except lead V1, reaching a minimum of −2 ÎŒV (95% CI, −5 to 0 ÎŒV) in lead V3.ConclusionsShort-term PM2.5 exposure is associated with ECG evidence of myocardial ischemia among postmenopausal women. The principal manifestations include subclinical but potentially arrhythmogenic ST–T abnormalities and decreases in T amplitude

    Embedding clinical interventions into observational studies

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    Novel approaches to observational studies and clinical trials could improve the cost-effectiveness and speed of translation of research. Hybrid designs that combine elements of clinical trials with observational registries or cohort studies should be considered as part of a long-term strategy to transform clinical trials and epidemiology, adapting to the opportunities of big data and the challenges of constrained budgets. Important considerations include study aims, timing, breadth and depth of the existing infrastructure that can be leveraged, participant burden, likely participation rate and available sample size in the cohort, required sample size for the trial, and investigator expertise. Community engagement and stakeholder (including study participants) support are essential for these efforts to succeed

    Efficacy of yoga for vasomotor symptoms: a randomized controlled trial

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    OBJECTIVE: This study aims to determine the efficacy of yoga in alleviating vasomotor symptoms (VMS) frequency and bother. METHODS: This study was a three-by-two factorial, randomized controlled trial. Eligible women were randomized to yoga (n = 107), exercise (n = 106), or usual activity (n = 142), and were simultaneously randomized to a double-blind comparison of ω-3 fatty acid (n = 177) or placebo (n = 178) capsules. Yoga intervention consisted of 12 weekly 90-minute yoga classes with daily home practice. Primary outcomes were VMS frequency and bother assessed by daily diaries at baseline, 6 weeks, and 12 weeks. Secondary outcomes included insomnia symptoms (Insomnia Severity Index) at baseline and 12 weeks. RESULTS: Among 249 randomized women, 237 (95%) completed 12-week assessments. The mean baseline VMS frequency was 7.4 per day (95% CI, 6.6 to 8.1) in the yoga group and 8.0 per day (95% CI, 7.3 to 8.7) in the usual activity group. Intent-to-treat analyses included all participants with response data (n = 237). There was no difference between intervention groups in the change in VMS frequency from baseline to 6 and 12 weeks (mean difference [yoga--usual activity] from baseline at 6 wk, -0.3 [95% CI, -1.1 to 0.5]; mean difference [yoga--usual activity] from baseline at 12 wk, -0.3 [95% CI, -1.2 to 0.6]; P = 0.119 across both time points). Results were similar for VMS bother. At week 12, yoga was associated with an improvement in insomnia symptoms (mean difference [yoga - usual activity] in the change in Insomnia Severity Index, 1.3 [95% CI, -2.5 to -0.1]; P = 0.007). CONCLUSIONS: Among healthy women, 12 weeks of yoga class plus home practice, compared with usual activity, do not improve VMS frequency or bother but reduce insomnia symptoms

    Ambient Particulate Air Pollution and Ectopy—The Environmental Epidemiology of Arrhythmogenesis in Women's Health Initiative Study, 1999–2004

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    The relationships between ambient PM2.5 and PM10 and arrhythmia and the effect modification by cigarette smoking were investigated. Data from EPA air quality monitors and an established national-scale, log-normal kriging method were used to spatially estimate daily mean concentrations of PM at addresses of 57,422 individuals from 59 examination sites in 24 US states in 1999-2004. The acute and subacute exposures were estimated as mean, geocoded address-specific PM concentrations on the day of, 0-2 days before, and averaged over 30 days before the ECG (Lag0; Lag1; Lag2; Lag1-30). At the time of standard 12-lead resting ECG, the mean age (SD) of participants was 67.5 (6.9) years (84% non-Hispanic White; 6% current smoker; 15% with coronary heart disease; 5% with ectopy). After the identification of significant effect modifiers, two-stage random-effects models were used to calculate center-pooled odds ratios and 95% confidence intervals (OR, 95% CI) of arrhythmia per 10 ÎŒg/m3 increase in PM concentrations. Among current smokers, Lag0 and Lag1 PM concentrations were significantly associated ventricular ectopy (VE) - the OR (95% CI) for VE among current smokers was 2 (1.32-3.3) and 1.32 (1.07-1.65) at Lag1 PM2.5 and PM10, respectively. The interactions between current smoking and acute exposures (Lag0; Lag1; Lag2) were significant in relationship to VE. Acute exposures were not significantly associated with supraventricular ectopy (SVE), or with VE among non-smokers. Subacute (Lag1-30) exposures were not significantly associated with arrhythmia. Acute PM2.5 and PM10 exposure is directly associated with the odds of VE among smokers, suggesting that they are more vulnerable to the arrhythmogenic effects of PM
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