Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics

1999 ◽  
Vol 82 (1-2) ◽  
pp. 171-196 ◽  
Author(s):  
Daniel Yekutieli ◽  
Yoav Benjamini
2000 ◽  
Vol 25 (1) ◽  
pp. 60-83 ◽  
Author(s):  
Yoav Benjamini ◽  
Yosef Hochberg

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.


Genetics ◽  
2003 ◽  
Vol 164 (2) ◽  
pp. 829-833
Author(s):  
Chiara Sabatti ◽  
Susan Service ◽  
Nelson Freimer

Abstract We explore the implications of the false discovery rate (FDR) controlling procedure in disease gene mapping. With the aid of simulations, we show how, under models commonly used, the simple step-down procedure introduced by Benjamini and Hochberg controls the FDR for the dependent tests on which linkage and association genome screens are based. This adaptive multiple comparison procedure may offer an important tool for mapping susceptibility genes for complex diseases.


2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii71-iii71
Author(s):  
T Kaisman-Elbaz ◽  
Y Elbaz ◽  
V Merkin ◽  
L Dym ◽  
A Noy ◽  
...  

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.


2020 ◽  
Vol 223 (1) ◽  
pp. 19-22
Author(s):  
Jingjing Zhu ◽  
Chong Wu ◽  
Lang Wu

Abstract It is critical to identify potential causal targets for SARS-CoV-2, which may guide drug repurposing options. We assessed the associations between genetically predicted protein levels and COVID-19 severity. Leveraging data from the COVID-19 Host Genetics Initiative comparing 6492 hospitalized COVID-19 patients and 1 012 809 controls, we identified 18 proteins with genetically predicted levels to be associated with COVID-19 severity at a false discovery rate of &lt;0.05, including 12 that showed an association even after Bonferroni correction. Of the 18 proteins, 6 showed positive associations and 12 showed inverse associations. In conclusion, we identified 18 candidate proteins for COVID-19 severity.


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