Sample Size Calculation for Controlling False Discovery Proportion

The false discovery proportion (FDP), the proportion of incorrect rejections among all rejections, is a direct measure of abundance of false positive findings in multiple testing. Many methods have been proposed to control FDP, but they are too conservative to be useful for power analysis. Study designs for controlling the mean of FDP, which is false discovery rate, have been commonly used. However, there has been little attempt to design study with direct FDP control to achieve certain level of efficiency. We provide a sample size calculation method using the variance formula of the FDP under weak-dependence assumptions to achieve the desired overall power. The relationship between design parameters and sample size is explored. The adequacy of the procedure is assessed by simulation. We illustrate the method using estimated correlations from a prostate cancer dataset

Some Statistical Methods on Design, Modeling and Analysis of High-Dimensional Data

In modern research, massive high-dimensional data are frequently generated by advancing technologies and combining multi-aspect data sources, and pose new challenges to statisticians. This thesis addresses various aspects, including study design, multiple hypotheses testing, nonlinear regression modeling and development of risk models related to high-dimensional data. When a large number of hypotheses are tested simultaneously, controlling traditional type I error rate for each test at 5% will lead to an excessive number of false positives. The false discovery proportion (FDP) is a direct measure of the abundance of false positive findings, defined as the proportion of incorrect rejections among all rejections. We propose a sample size calculation method to control FDP and ensure overall power of a study. In addition, it is highly desired to have an accurate prediction interval for the FDP. We propose a formula-based and a permutation-based prediction interval, respectively, for weak and strong dependence between test statistics. Developing flexible and parsimonious nonlinear models which can achieve dimension reduction is important for practical implementation and interpretation. Motivated by an ovarian cancer epidemiologic study, we consider the application and inference of a partially linear single index proportional hazard model, which includes a linear component and a nonparametric single index component. Polynomial spline approximation is used to estimate the nonlinear component, and asymptotic properties of the resulting estimators are established. We also develop a relative risk prediction model for cancer recurrence in primary melanoma patients. We identify a microRNA signature from hundreds of candidate variables and build a risk prediction model for melanoma recurrence. The model is evaluated using an independent cohort. In summary, we have conducted multiple projects to develop statistical methods to cope with problems in study design, as well as modeling and analysis of high-dimensional data

A Tight Prediction Interval for False Discovery Proportion under Dependence

The false discovery proportion (FDP) is a useful measure of abundance of false positives when a large number of hypotheses are being tested simultaneously. Methods for controlling the expected value of the FDP, namely the false discovery rate (FDR), have become widely used. It is highly desired to have an accurate prediction interval for the FDP in such applications. Some degree of dependence among test statistics exists in almost all applications involving multiple testing. Methods for constructing tight prediction intervals for the FDP that take account of dependence among test statistics are of great practical importance. This paper derives a formula for the variance of the FDP and uses it to obtain an upper prediction interval for the FDP, under some semi-parametric assumptions on dependence among test statistics. Simulation studies indicate that the proposed formula-based prediction interval has good coverage probability under commonly assumed weak dependence. The prediction interval is generally more accurate than those obtained from existing methods. In addition, a permutation-based upper prediction interval for the FDP is provided, which can be useful when dependence is strong and the number of tests is not too large. The proposed prediction intervals are illustrated using a prostate cancer dataset

Peginterferon β-1a every 2 weeks increased achievement of no evidence of disease activity over 4 years in the ADVANCE and ATTAIN studies in patients with relapsing–remitting multiple sclerosis

Background: No evidence of disease activity (NEDA) is a composite measurement, incorporating clinical and magnetic resonance imaging (MRI) elements of disease activity to sensitively evaluate the therapeutic efficacy of treatments for relapsing–remitting multiple sclerosis (RRMS). Objective: To assess the NEDA status of patients treated with peginterferon β-1a in the ADVANCE and ATTAIN studies and explore its predictive value on longer-term clinical outcomes. Methods: ATTAIN was a 2-year extension of the pivotal 2-year ADVANCE study of peginterferon β-1a for RRMS. Achievement of clinical NEDA, MRI NEDA, or overall NEDA was calculated cumulatively and by year over 4 years. Clinical outcomes during ATTAIN were analyzed based on NEDA status at the end of ADVANCE. Results: Significantly more patients treated with peginterferon β-1a every 2 weeks than every 4 weeks achieved clinical NEDA (60.6% versus 50.6%, p  = 0.0063) and MRI NEDA (28.3% versus 15.8%, p  = 0.0005) through year 4 and overall NEDA through year 3 (20.9% versus 13.9%, p  = 0.0160). Over 4 years, 15.8% of patients in the every 2 weeks group and 10.7% of patients in the every 4 weeks group maintained overall NEDA ( p  = 0.0584). Achievement of clinical NEDA, MRI NEDA, or overall NEDA in ADVANCE was predictive of annualized relapse rate in ATTAIN; achievement of clinical NEDA in ADVANCE was also predictive of NEDA achievement and confirmed disability worsening in ATTAIN. Conclusions: Peginterferon β-1a every 2 weeks is associated with higher levels of NEDA compared with placebo in year 1 or peginterferon β-1a every 4 weeks in years 2–4. Overall NEDA within the first 2 years of treatment may be prognostic of long-term clinical outcomes. Clinicaltrials.gov : NCT0133201

Quantitative evaluation of all hexamers as exonic splicing elements

We describe a comprehensive quantitative measure of the splicing impact of a complete set of RNA 6-mer sequences by deep sequencing successfully spliced transcripts. All 4096 6-mers were substituted at five positions within two different internal exons in a 3-exon minigene, and millions of successfully spliced transcripts were sequenced after transfection of human cells. The results allowed the assignment of a relative splicing strength score to each mutant molecule. The effect of 6-mers on splicing often depended on their location; much of this context effect could be ascribed to the creation of different overlapping sequences at each site. Taking these overlaps into account, the splicing effect of each 6-mer could be quantified, and 6-mers could be designated as enhancers (ESEseqs) and silencers (ESSseqs), with an ESRseq score indicating their strength. Some 6-mers exhibited positional bias relative to the two splice sites. The distribution and conservation of these ESRseqs in and around human exons supported their classification. Predicted RNA secondary structure effects were also seen: Effective enhancers, silencers and 3′ splice sites tend to be single stranded, and effective 5′ splice sites tend to be double stranded. 6-mers that may form positive or negative synergy with another were also identified. Chromatin structure may also influence the splicing enhancement observed, as a good correspondence was found between splicing performance and the predicted nucleosome occupancy scores of 6-mers. This approach may prove of general use in defining nucleic acid regulatory motifs, substitute for functional SELEX in most cases, and provide insights about splicing mechanisms

Long-term outcomes of peginterferon beta-1a in multiple sclerosis: results from the ADVANCE extension study, ATTAIN

Background: ADVANCE was a phase III trial of the efficacy and safety of subcutaneous peginterferon beta-1a 125 µg every 2 or 4 weeks in patients with relapsing-remitting multiple sclerosis (RRMS). ATTAIN was a 2-year extension study of ADVANCE. The aim was to evaluate the long-term safety, tolerability, and efficacy of peginterferon beta-1a 125 µg every 2 or 4 weeks in ATTAIN. Methods: ADVANCE dosing schedules were maintained in ATTAIN, except that every-4-weeks dosing patients were switched to every-2-weeks dosing after conversion of the study to an open-label protocol. ATTAIN was considered complete when the last patient completed the 96-week extension study. Primary endpoints included adverse event (AE) and serious AE (SAE) incidence. Secondary endpoints included relapse, magnetic resonance imaging, and disability outcomes. Results: Of the 1512 patients randomized in ADVANCE, 1076 (71%) continued treatment in ATTAIN; of these, 842 (78%) completed the open-label extension study. During ATTAIN, 478 patients (87%) in the every-2-weeks group and 471 patients (89%) in the every-4-weeks group experienced an AE; SAEs were reported in 90 patients (16%) in the every-2-weeks group and 113 patients (21%) in the every-4-weeks group. The most frequent AEs reported were injection site reactions and flu-like symptoms, both of which numerically decreased over time. Peginterferon beta-1a every 2 weeks versus every 4 weeks significantly reduced the adjusted annualized relapse rate over 6 years (0.188 versus 0.263, p = 0.0052) and the risk of relapse over 5 years (36% versus 49%, p = 0.0018). Fewer new T1, new/newly enlarging T2, and gadolinium-enhancing magnetic resonance imaging lesions were observed with every-2-weeks dosing than every-4-weeks dosing over 4 years. Conclusions: Results from the ADVANCE extension study, ATTAIN, confirm the favorable long-term safety and tolerability profile of peginterferon beta-1a in patients with RRMS and provide additional evidence for the clinical and radiological benefits associated with this therapy

Application of the Asthma Phenotype Algorithm from the Severe Asthma Research Program to an Urban Population

<div><h3>Rationale</h3><p>Identification and characterization of asthma phenotypes are challenging due to disease complexity and heterogeneity. The Severe Asthma Research Program (SARP) used unsupervised cluster analysis to define 5 phenotypically distinct asthma clusters that they replicated using 3 variables in a simplified algorithm. We evaluated whether this simplified SARP algorithm could be used in a separate and diverse urban asthma population to recreate these 5 phenotypic clusters.</p> <h3>Methods</h3><p>The SARP simplified algorithm was applied to adults with asthma recruited to the New York University/Bellevue Asthma Registry (NYUBAR) to classify patients into five groups. The clinical phenotypes were summarized and compared.</p> <h3>Results</h3><p>Asthma subjects in NYUBAR (n = 471) were predominantly women (70%) and Hispanic (57%), which were demographically different from the SARP population. The clinical phenotypes of the five groups generated by the simplified SARP algorithm were distinct across groups and distributed similarly to those described for the SARP population. Groups 1 and 2 (6 and 63%, respectively) had predominantly childhood onset atopic asthma. Groups 4 and 5 (20%) were older, with the longest duration of asthma, increased symptoms and exacerbations. Group 4 subjects were the most atopic and had the highest peripheral eosinophils. Group 3 (10%) had the least atopy, but included older obese women with adult-onset asthma, and increased exacerbations.</p> <h3>Conclusions</h3><p>Application of the simplified SARP algorithm to the NYUBAR yielded groups that were phenotypically distinct and useful to characterize disease heterogeneity. Differences across NYUBAR groups support phenotypic variation and support the use of the simplified SARP algorithm for classification of asthma phenotypes in future prospective studies to investigate treatment and outcome differences between these distinct groups.</p> <h3>Trial Registration</h3><p>Clinicaltrials.gov <a href="http://www.clinicaltrials.gov/ct2/show/NCT00212537">NCT00212537</a></p> </div