scholarly journals Differentially private false discovery rate control

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Cynthia Dwork ◽  
Weijie Su ◽  
Li Zhang
Keyword(s):  
False Discovery Rate ◽  
Multiple Testing ◽  
Differential Privacy ◽  
Broad Class ◽  
Multiple Hypothesis Testing ◽  
P Value ◽  
P Values ◽  
Testing Procedures ◽  
False Discovery ◽  
Rigorous Framework

Differential privacy provides a rigorous framework for privacy-preserving data analysis. This paper proposes the first differentially private procedure for controlling the false discovery rate (FDR) in multiple hypothesis testing. Inspired by the Benjamini-Hochberg procedure (BHq), our approach is to first repeatedly add noise to the logarithms of the p-values to ensure differential privacy and to select an approximately smallest p-value serving as a promising candidate at each iteration; the selected p-values are further supplied to the BHq and our private procedure releases only the rejected ones. Moreover, we develop a new technique that is based on a backward submartingale for proving FDR control of a broad class of multiple testing procedures, including our private procedure, and both the BHq step- up and step-down procedures. As a novel aspect, the proof works for arbitrary dependence between the true null and false null test statistics, while FDR control is maintained up to a small multiplicative factor.

On the Operational Characteristics of the Benjamini and Hochberg False Discovery Rate Procedure

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Gerwyn H Green ◽  
Peter J. Diggle
Keyword(s):  
False Discovery Rate ◽  
Multiple Testing ◽  
False Positives ◽  
Experimental Conditions ◽  
Single Experiment ◽  
Testing Procedures ◽  
False Discovery ◽  
Expression Studies ◽  
Operational Characteristics ◽  
Gene Expression Studies

Multiple testing procedures are commonly used in gene expression studies for the detection of differential expression, where typically thousands of genes are measured over at least two experimental conditions. Given the need for powerful testing procedures, and the attendant danger of false positives in multiple testing, the False Discovery Rate (FDR) controlling procedure of Benjamini and Hochberg (1995) has become a popular tool. When simultaneously testing hypotheses, suppose that R rejections are made, of which Fp are false positives. The Benjamini and Hochberg procedure ensures that the expectation of Fp/R is bounded above by some pre-specified proportion. In practice, the procedure is applied to a single experiment. In this paper we investigate the across-experiment variability of the proportion Fp/R as a function of three experimental parameters. The operational characteristics of the procedure when applied to dependent hypotheses are also considered.

SOME COMMENTS ON INSTABILITY OF FALSE DISCOVERY RATE ESTIMATION

2006 ◽  
Vol 04 (05) ◽  
pp. 1057-1068 ◽  
Author(s):  
XING QIU ◽  
ANDREI YAKOVLEV
Keyword(s):  
False Discovery Rate ◽  
Microarray Data ◽  
Multiple Testing ◽  
Microarray Data Analysis ◽  
Expression Data ◽  
Testing Procedures ◽  
Empirical Estimates ◽  
False Discovery ◽  
Multiple Testing Procedures ◽  
False Discovery Rate Estimation

Some extended false discovery rate (FDR) controlling multiple testing procedures rely heavily on empirical estimates of the FDR constructed from gene expression data. Such estimates are also used as performance indicators when comparing different methods for microarray data analysis. The present communication shows that the variance of the proposed estimators may be intolerably high, the correlation structure of microarray data being the main cause of their instability.

scholarly journals An Improved EWMA-Based Method for Outbreak Detection in Multiple Regions

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Sesha K. Dassanayaka ◽  
Joshua French
Keyword(s):  
False Discovery Rate ◽  
Multiple Testing ◽  
Average Run Length ◽  
Critical Values ◽  
Outbreak Detection ◽  
P Values ◽  
Run Length ◽  
Salmonella Newport ◽  
False Discovery ◽  
Multiple Regions

We present a simple, fast, and easily interpretable procedure that results in faster detection of outbreaks in multiple spatial regions. Disease counts from neighboring regions are aggregated to compute a Poisson CUSUM statistic for each region. Instead of controlling the average run length error criterion in the testing process, we instead utilize the false discovery rate. Additionally, p-values are used to make decisions instead of traditional critical-values. The use of the false discovery rate and p-values in testing allows us to utilize more powerful multiple testing methodologies. The procedure is successfully applied to detect the 2011 Salmonella Newport outbreak in Germany.

scholarly journals P14.21 Tehila Kaisman-Elbaz MD/PhD

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii71-iii71
Author(s):  
T Kaisman-Elbaz ◽  
Y Elbaz ◽  
V Merkin ◽  
L Dym ◽  
A Noy ◽  
...  
Keyword(s):  
Overall Survival ◽  
Multivariate Analysis ◽  
False Discovery Rate ◽  
Medical Center ◽  
Tumor Resection ◽  
Multiple Hypothesis Testing ◽  
Hemoglobin Level ◽  
Distribution Width ◽  
False Discovery ◽  
Dismal Prognosis

Abstract BACKGROUND Glioblastoma is known for its dismal prognosis though its dependency on patients’ readily available RBCs parameters defining the patient’s anemic status such as hemoglobin level and Red blood cells distribution Width (RDW) is not fully established. Several works demonstrated a connection between low hemoglobin level or high RDW values to overall glioblastoma patient’s survival, but in other works, a clear connection was not found. This study addresses this unclarity. MATERIAL AND METHODS In this work, 170 glioblastoma patients, diagnosed and treated in Soroka University Medical Center (SUMC) in the last 12 years were retrospectively inspected for their survival dependency on pre-operative RBCs parameters using multivariate analysis followed by false discovery rate procedure due to the multiple hypothesis testing. A survival stratification tree and Kaplan-Meier survival curves that indicate the patient’s prognosis according to these parameters were prepared. RESULTS Beside KPS>70 and tumor resection supplemented by oncological treatment, age<70 (HR=0.4, 95% CI 0.24–0.65), low hemoglobin level (HR=1.79, 95% CI 1.06–2.99) and RDW<14% (HR=0.57, 95% CI 0.37–0.88) were found to be prognostic to patients’ overall survival in multivariate analysis, accounting for false discovery rate of less than 5%. CONCLUSION A survival stratification highlighted a non-anemic subgroup of nearly 30% of the cohort’s patients whose median overall survival was 21.1 months (95% CI 16.2–27.2) - higher than the average Stupp protocol overall median survival of about 15 months. A discussion on the beneficial or detrimental effect of RBCs parameters on glioblastoma prognosis and its possible causes is given.

Abstract MP11: Circulating Plasma Biomarkers Associated With Brain Arteriovenous Malformations

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Sarah E Wetzel-Strong ◽  
Shantel M Weinsheimer ◽  
Jeffrey Nelson ◽  
Ludmila Pawlikowska ◽  
Dewi Clark ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Statistical Significance ◽  
Protein Profiling ◽  
P Value ◽  
P Values ◽  
Plasma Biomarkers ◽  
Standard Curve ◽  
Disease States ◽  
Heparin Plasma ◽  
Circulating Levels

Objective: Circulating plasma protein profiling may aid in the identification of cerebrovascular disease signatures. This study aimed to identify circulating angiogenic and inflammatory biomarkers that may serve as biomarkers to differentiate sporadic brain arteriovenous malformation (bAVM) patients from other conditions with brain AVMs, including hereditary hemorrhagic telangiectasia (HHT) patients. Methods: The Quantibody Human Angiogenesis Array 1000 (Raybiotech) is an ELISA multiplex panel that was used to assess the levels of 60 proteins related to angiogenesis and inflammation in heparin plasma samples from 13 sporadic unruptured bAVM patients (69% male, mean age 51 years) and 37 patients with HHT (40% male, mean age 47 years, n=19 (51%) with bAVM). The Quantibody Q-Analyzer tool was used to calculate biomarker concentrations based on the standard curve for each marker and log-transformed marker levels were evaluated for associations between disease states using a multivariable interval regression model adjusted for age, sex, ethnicity and collection site. Statistical significance was based on Bonferroni correction for multiple testing of 60 biomarkers (P< 8.3x10 - 4 ). Results: Circulating levels of two plasma proteins differed significantly between sporadic bAVM and HHT patients: PDGF-BB (P=2.6x10 -4 , PI= 3.37, 95% CI:1.76-6.46) and CCL5 (P=6.0x10 -6 , PI=3.50, 95% CI=2.04-6.03). When considering markers with a nominal p-value of less than 0.01, MMP1 and angiostatin levels also differed between patients with sporadic bAVM and HHT. Markers with nominal p-values less than 0.05 when comparing sporadic brain AVM and HHT patients also included angiostatin, IL2, VEGF, GRO, CXCL16, ITAC, and TGFB3. Among HHT patients, the circulating levels of UPAR and IL6 were elevated in patients with documented bAVMs when considering markers with nominal p-values less than 0.05. Conclusions: This study identified differential expression of two promising plasma biomarkers that differentiate sporadic bAVMs from patients with HHT. Furthermore, this study allowed us to evaluate markers that are associated with the presence of bAVMs in HHT patients, which may offer insight into mechanisms underlying bAVM pathophysiology.

On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics

2000 ◽  
Vol 25 (1) ◽  
pp. 60-83 ◽  
Author(s):  
Yoav Benjamini ◽  
Yosef Hochberg
Keyword(s):  
False Discovery Rate ◽  
Multiple Testing ◽  
Meta Analysis ◽  
Test Statistics ◽  
Adaptive Procedure ◽  
New Approach ◽  
False Discovery ◽  
Behavioral Studies ◽  
Simultaneous Testing ◽  
Independent Test

A new approach to problems of multiple significance testing was presented in Benjamini and Hochberg (1995), which calls for controlling the expected ratio of the number of erroneous rejections to the number of rejections–the False Discovery Rate (FDR). The procedure given there was shown to control the FDR for independent test statistics. When some of the hypotheses are in fact false, that procedure is too conservative. We present here an adaptive procedure, where the number of true null hypotheses is estimated first as in Hochberg and Benjamini (1990), and this estimate is used in the procedure of Benjamini and Hochberg (1995). The result is still a simple stepwise procedure, to which we also give a graphical companion. The new procedure is used in several examples drawn from educational and behavioral studies, addressing problems in multi-center studies, subset analysis and meta-analysis. The examples vary in the number of hypotheses tested, and the implication of the new procedure on the conclusions. In a large simulation study of independent test statistics the adaptive procedure is shown to control the FDR and have substantially better power than the previously suggested FDR controlling method, which by itself is more powerful than the traditional family wise error-rate controlling methods. In cases where most of the tested hypotheses are far from being true there is hardly any penalty due to the simultaneous testing of many hypotheses.

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