Multiple Comparison Procedures

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works

The control of the false discovery rate in multiple testing under dependency

Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490 · see citing works

Significance analysis of microarrays applied to the ionizing radiation response

Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568 · see citing works

Nonparametric permutation tests for functional neuroimaging: A primer with examples

Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256 · see citing works

Effect of Treatment of Gestational Diabetes Mellitus on Pregnancy Outcomes

Crowther, C. A., Hiller, J. E., Moss, J. R., McPhee, A. J., Jeffries, W. S., & Robinson, J. S. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa042973 · Cited by 3,159 · see citing works

Lutetium-177–PSMA-617 for Metastatic Castration-Resistant Prostate Cancer

Sartor, O., de Bono, J., Chi, K. N., Fizazi, K., Herrmann, K., Rahbar, K., Tagawa, S. T., Nordquist, L. T., Vaishampayan, N., El-Haddad, G., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2107322 · Cited by 2,864 · see citing works

More powerful procedures for multiple significance testing

Hochberg, Y., & Benjamini, Y. (1990) · Statistics in Medicine · doi:10.1002/sim.4780090710 · Cited by 2,715 · see citing works

… and the Cross-Section of Expected Returns

Harvey, C. R., Liu, Y., & Zhu, H. (2016) · Review of Financial Studies · doi:10.1093/rfs/hhv059 · Cited by 2,119 · see citing works

Controlling the risk of spurious findings from meta-regression

Higgins, J. P. T., & Thompson, S. G. (2004) · Statistics in Medicine · doi:10.1002/sim.1752 · Cited by 1,267 · see citing works

Alemtuzumab versus interferon beta 1a as first-line treatment for patients with relapsing-remitting multiple sclerosis: a randomised controlled phase 3 trial

Cohen, J. A., Coles, A. J., Arnold, D. L., Confavreux, C., Fox, E. J., Hartung, H.-P., Havrdova, E., Selmaj, K. W., Weiner, H. L., Fisher, E., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61769-3 · Cited by 1,205 · see citing works

Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: a randomised controlled phase 3 trial

Coles, A. J., Twyman, C. L., Arnold, D. L., Cohen, J. A., Confavreux, C., Fox, E. J., Hartung, H.-P., Havrdova, E., Selmaj, K. W., Weiner, H. L., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61768-1 · Cited by 1,198 · see citing works

A comparison of classification techniques for the P300 Speller

Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2006) · Journal of Neural Engineering · doi:10.1088/1741-2560/3/4/007 · Cited by 762 · see citing works

Estimation of significance thresholds for genomewide association scans

Dudbridge, F., & Gusnanto, A. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20297 · Cited by 703 · see citing works

Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing

Romano, J. P., & Wolf, M. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000000539 · Cited by 609 · see citing works

The ecological validity of laboratory and analogue assessment methods of ADHD symptoms

Barkley, R. A. (1991) · Journal of Abnormal Child Psychology · doi:10.1007/bf00909976 · Cited by 419 · see citing works

Withdrawal Syndrome after the Double-Blind Cessation of Caffeine Consumption

Silverman, K., Evans, S. M., Strain, E. C., & Griffiths, R. R. (1992) · New England Journal of Medicine · doi:10.1056/nejm199210153271601 · Cited by 380 · see citing works

Gender differences in preferences for psychological treatment, coping strategies, and triggers to help-seeking

Liddon, L., Kingerlee, R., & Barry, J. A. (2018) · British Journal of Clinical Psychology · doi:10.1111/bjc.12147 · Cited by 387 · see citing works

When to adjust alpha during multiple testing: a consideration of disjunction, conjunction, and individual testing

Rubin, M. (2021) · Synthese · doi:10.1007/s11229-021-03276-4 · Cited by 371 · see citing works

Oblique effect: a neural basis in the visual cortex

Li, B., Peterson, M. R., & Freeman, R. D. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00954.2002 · Cited by 328 · see citing works

Robotic touch shifts perception of embodiment to a prosthesis in targeted reinnervation amputees

Marasco, P. D., Kim, K., Colgate, J. E., Peshkin, M. A., & Kuiken, T. A. (2011) · Brain · doi:10.1093/brain/awq361 · Cited by 335 · see citing works

Generalizations of the familywise error rate

Lehmann, E. L., & Romano, J. P. (2005) · The Annals of Statistics · doi:10.1214/009053605000000084 · Cited by 320 · see citing works

Efficacy and tolerability of vildagliptin monotherapy in drug-naïve patients with type 2 diabetes

Pi-Sunyer, F. X., Schweizer, A., Mills, D., & Dejager, S. (2007) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2006.12.009 · Cited by 312 · see citing works

A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence

Benjamini, Y., & Liu, W. (1999) · Journal of Statistical Planning and Inference · doi:10.1016/s0378-3758(99)00040-3 · Cited by 242 · see citing works

Self-managed, computerised speech and language therapy for patients with chronic aphasia post-stroke compared with usual care or attention control (Big CACTUS): a multicentre, single-blinded, randomised controlled trial

Palmer, R., Dimairo, M., Cooper, C., Enderby, P., Brady, M., Bowen, A., Latimer, N., Julious, S., Cross, E., Alshreef, A., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30192-9 · Cited by 200 · see citing works

Clinical and Bacterial Markers of Periodontitis and Their Association with Incident All-Cause and Alzheimer’s Disease Dementia in a Large National Survey

Beydoun, M. A., Beydoun, H. A., Hossain, S., El-Hajj, Z. W., Weiss, J., & Zonderman, A. B. (2020) · Journal of Alzheimer's Disease · doi:10.3233/jad-200064 · Cited by 201 · see citing works

Statistical limitations in functional neuroimaging II. Signal detection and statistical inference

Petersson, K. M., Nichols, T. E., Poline, J.-B., & Holmes, A. P. (1999) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.1999.0478 · Cited by 186 · see citing works

Simultaneous changes in central and peripheral components of the hypothalamus-pituitary-thyroid axis in lipopolysaccharide-induced acute illness in mice

Boelen, A., Kwakkel, J., Thijssen-Timmer, D., Alkemade, A., Fliers, E., & Wiersinga, W. (2004) · Journal of Endocrinology · doi:10.1677/joe.0.1820315 · Cited by 159 · see citing works

Motivation for choice and healthiness perception of calorie-reduced dairy products. A cross-cultural study

Johansen, S. B., Næs, T., & Hersleth, M. (2011) · Appetite · doi:10.1016/j.appet.2010.11.137 · Cited by 111 · see citing works

Dose finding—a challenge in statistics.

Bretz, F., Hsu, J., Pinheiro, J., & Liu, Y. (2008) · Biometrical Journal · doi:10.1002/bimj.200810438 · Cited by 101 · see citing works

Long-term clinical outcomes of high-dose mepolizumab treatment for hypereosinophilic syndrome

Kuang, F. L., Fay, M. P., Ware, J., Wetzler, L., Holland-Thomas, N., Brown, T., Ortega, H., Steinfeld, J., Khoury, P., & Klion, A. D. (2018) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2018.04.033 · Cited by 94 · see citing works

The use of meta-analytic statistical significance testing

Polanin, J. R., & Pigott, T. D. (2015) · Research Synthesis Methods · doi:10.1002/jrsm.1124 · Cited by 78 · see citing works

Predicting Patient Survival after Liver Transplantation Using Evolutionary Multi-Objective Artificial Neural Networks

Cruz-Ramírez, M., Hervás-Martínez, C., Fernández, J. C., Briceño, J., & de la Mata, M. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2013.02.004 · Cited by 79 · see citing works

Applying active learning to assertion classification of concepts in clinical text

Chen, Y., Mani, S., & Xu, H. (2012) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.11.003 · Cited by 57 · see citing works

Mindfulness in Motion: a Mindfulness-Based Intervention to Reduce Stress and Enhance Quality of Sleep in Scandinavian Employees

Klatt, M., Norre, C., Reader, B., Yodice, L., & White, S. (2017) · Mindfulness · doi:10.1007/s12671-016-0621-x · Cited by 56 · see citing works

Computerised speech and language therapy or attention control added to usual care for people with long-term post-stroke aphasia: the Big CACTUS three-arm RCT

Palmer, R., Dimairo, M., Latimer, N., Cross, E., Brady, M., Enderby, P., Bowen, A., Julious, S., Harrison, M., Alshreef, A., et al. (2020) · Health Technology Assessment · doi:10.3310/hta24190 · Cited by 53 · see citing works

Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification

Hett, K., Ta, V.-T., Catheline, G., Tourdias, T., Manjón, J. V., Coupé, P., Initiative, A. D. N., Weiner, M. W., Aisen, P., Petersen, R., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49970-9 · Cited by 49 · see citing works

Cytokines are associated with longitudinal changes in cognitive performance among urban adults

Beydoun, M. A., Weiss, J., Obhi, H. K., Beydoun, H. A., Dore, G. A., Liang, H., Evans, M. K., & Zonderman, A. B. (2019) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2019.04.027 · Cited by 45 · see citing works

Development of gatekeeping strategies in confirmatory clinical trials

Dmitrienko, A., Millen, B. A., Brechenmacher, T., & Paux, G. (2011) · Biometrical Journal · doi:10.1002/bimj.201100036 · Cited by 43 · see citing works

Inattention and hyperactivity in association with autism spectrum disorders in the CHARGE study

Lyall, K., Schweitzer, J. B., Schmidt, R. J., Hertz-Picciotto, I., & Solomon, M. (2017) · Research in Autism Spectrum Disorders · doi:10.1016/j.rasd.2016.11.011 · Cited by 42 · see citing works

<p>Adherence to self-monitoring of blood glucose in Chinese patients with type 2 diabetes: current status and influential factors based on electronic questionnaires</p>

Wang, X., Luo, J.-F., Qi, L., Long, Q., Guo, J., & Wang, H.-H. (2019) · Patient Preference and Adherence · doi:10.2147/ppa.s211668 · Cited by 37 · see citing works

A Bayesian Approach to the Correction for Multiplicity

de Jong, T. (2019) · doi:10.31234/osf.io/s56mk · Cited by 35 · see citing works

Challenges and Solutions to Pre- and Post-Randomization Subgroup Analyses

Desai, M., Pieper, K. S., & Mahaffey, K. (2014) · Current Cardiology Reports · doi:10.1007/s11886-014-0531-2 · Cited by 26 · see citing works

InRange: Comparison of the second-generation basal insulin analogues glargine 300 u/ml and degludec 100 u/ml in persons with type 1 diabetes using continuous glucose monitoring-study design

Battelino, T., Bosnyak, Z., Danne, T., Mukherjee, B., Edelman, S., Pilorget, V., Choudhary, P., Renard, E., & Bergenstal, R. (2020) · Diabetes Therapy · doi:10.1007/s13300-020-00781-6 · Cited by 23 · see citing works

Influencing Public Acceptance of Artificial Intelligence (AI) in Healthcare Delivery

Aras, S., Drakos, C., Manimangalam, V., Nasir, M. A., Burns, C., Smith, D., & Equils, O. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1664345 · Cited by 15 · see citing works

Association between oxytocin receptor gene polymorphisms and autistic traits as measured by the <scp>A</scp>utism‐<scp>S</scp>pectrum <scp>Q</scp>uotient in a non‐clinical <scp>J</scp>apanese population

Kawamura, Y., Liu, X., Shimada, T., Otowa, T., Kakiuchi, C., Akiyama, T., Umekage, T., & Sasaki, T. (2011) · Asia-Pacific Psychiatry · doi:10.1111/j.1758-5872.2011.00132.x · Cited by 7 · see citing works

Simultaneous confidence intervals for ranks with application to ranking institutions

Al Mohamad, D., Goeman, J. J., & van Zwet, E. W. (2022) · Biometrics · doi:10.1111/biom.13419 · Cited by 1 · see citing works

Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool

Holstege, F., Jorgensen, M., Padh, K., Parie, J., Proroković, K., Persson, J., & Snoek, L. (2025) · arXiv · doi:10.48550/arxiv.2502.01713