Pièges et mésusages en analyse de données

Cohen, 1994, The Earth is round (P < .05), Am Psychol, 49, 997, 10.1037/0003-066X.49.12.997

Altman, 1994, Problems in dichotomizing continuous variables, Am J Epidemiol, 139, 442, 10.1093/oxfordjournals.aje.a117020

Cristea, 2018, P values in display items are ubiquitous and almost invariably significant: A survey of top science journals, PLOS ONE, 13, e0197440, 10.1371/journal.pone.0197440

Cohen, 1938, The misuse of statistics, J Am Stat Assoc, 33, 657, 10.1080/01621459.1938.10502344

Campbell, 2004

Edler, 2003, Randomized clinical trial: myths around elementary statistical principles, Oncol Res Treat, 26, 551, 10.1159/000074150

Goodman, 2016, What does research reproducibility mean?, Sci Transl Med, 8, 341ps12, 10.1126/scitranslmed.aaf5027

Greenland, 2019, Valid P-values behave exactly as they should: some misleading criticisms of P-values and their resolution with S-values, Am Stat, 73, 106, 10.1080/00031305.2018.1529625

Heinze, 2017, Five myths about variable selection, Transpl Int, 30, 6, 10.1111/tri.12895

Ioannidis, 2019, What have we (not) learnt from millions of scientific papers with P values?, Am Stat, 73, 20, 10.1080/00031305.2018.1447512

Makin, 2019, Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. Rodgers P, Parsons N, Holmes N, editors, eLife, 8, e48175, 10.7554/eLife.48175

Nakagawa, 2007, Effect size, confidence interval and statistical significance: a practical guide for biologists, Biol Rev, 82, 591, 10.1111/j.1469-185X.2007.00027.x

van Smeden, 2020, Reflection on modern methods: five myths about measurement error in epidemiological research, Int J Epidemiol, 49, 338, 10.1093/ije/dyz251

Amrhein, 2019, Scientists rise up against statistical significance, Nature, 567, 305, 10.1038/d41586-019-00857-9

Benjamin, 2018, Redefine statistical significance, Nat Hum Behav, 2, 6, 10.1038/s41562-017-0189-z

Devezer, 2021, The case for formal methodology in scientific reform, R Soc Open Sci, 8, 200805, 10.1098/rsos.200805

McShane, 2019, Abandon statistical significance, Am Stat., 73, 235, 10.1080/00031305.2018.1527253

Wasserstein, 2016, The ASA statement on P-values: context, process, and purpose, Am Stat, 70, 129, 10.1080/00031305.2016.1154108

Stang, 2010, The ongoing tyranny of statistical significance testing in biomedical research, Eur J Epidemiol, 2, 225, 10.1007/s10654-010-9440-x

Yaddanapudi, 2016, The American Statistical Association statement on P-values explained, J Anaesthesiol Clin Pharmacol, 32, 421, 10.4103/0970-9185.194772

Greenland, 2023, Divergence versus decision P-values: A distinction worth making in theory and keeping in practice: Or, how divergence P-values measure evidence even when decision P-values do not, Scand J Stat, 50, 54, 10.1111/sjos.12625

Garamszegi, 2017, Perturbations on the uniform distribution of p-values can lead to misleading inferences from null-hypothesis testing, Trends Neurosci Educ, 8-9, 18, 10.1016/j.tine.2017.10.001

Nieuwenhuis, 2011, Erroneous analyses of interactions in neuroscience: a problem of significance, Nat Neurosci, 14, 1105, 10.1038/nn.2886

Freckleton, 2002, On the misuse of residuals in ecology: regression of residuals vs. multiple regression, J Anim Ecol, 71, 542, 10.1046/j.1365-2656.2002.00618.x

Franke, 2012, The Chi-Square Test: often used and more often misinterpreted, Am J Eval, 33, 448, 10.1177/1098214011426594

Johnson, 2013, Revised standards for statistical evidence, Proc Natl Acad Sci, 110, 19313, 10.1073/pnas.1313476110

Head, 2015, The extent and consequences of P-hacking in science, PLOS Biol, 13, e1002106, 10.1371/journal.pbio.1002106

Stefan, 2023, Big little lies: a compendium and simulation of P-hacking strategies, R Soc Open Sci, 10, 220346, 10.1098/rsos.220346

Westfall, 2011, On using the bootstrap for multiple comparisons, J Biopharm Stat, 21, 1187, 10.1080/10543406.2011.607751

Blume, 2018, Second-generation P-values: Improved rigor, reproducibility, & transparency in statistical analyses. Smalheiser NR, ed, PLos One, 13, e0188299, 10.1371/journal.pone.0188299

Dmitrienko, 2018, Multiplicity considerations in clinical trials, N Engl J Med., 378, 2115, 10.1056/NEJMra1709701

Lantz, 2013, The large sample size fallacy, Scand J Caring Sci, 27, 487, 10.1111/j.1471-6712.2012.01052.x

Dettori, 2019, P-value worship: is the idol significant?, Glob Spine J, 9, 357, 10.1177/2192568219838538

Fethney, 2010, Statistical and clinical significance, and how to use confidence intervals to help interpret both, Aust Crit Care, 23, 93, 10.1016/j.aucc.2010.03.001

Hentschke, 2011, Computation of measures of effect size for neuroscience data sets, Eur J Neurosci, 34, 1887, 10.1111/j.1460-9568.2011.07902.x

Lee, 2016, Alternatives to P value: confidence interval and effect size, Korean J Anesthesiol, 69, 555, 10.4097/kjae.2016.69.6.555

Ioannidis, 2019, The importance of predefined rules and prespecified statistical analyses: do not abandon significance, JAMA, 321, 2067, 10.1001/jama.2019.4582

Lakens, 2021, The practical alternative to the P value is the correctly used P value, Perspect Psychol Sci, 16, 639, 10.1177/1745691620958012

Benjamin, 2019, Three recommendations for improving the use of P-values, Am Stat, 73, 186, 10.1080/00031305.2018.1543135

Hurlbert, 2019, Coup de grace for a tough old bull: “statistically significant” expires, Am Stat, 73, 352, 10.1080/00031305.2018.1543616

van de Schoot, 2021, Bayesian statistics and modelling, Nat Rev Methods Primer, 1, 1, 10.1038/s43586-020-00001-2

Nosek, 2018, The preregistration revolution, Proc Natl Acad Sci, 115, 2600, 10.1073/pnas.1708274114

DeCoster, 2009, A conceptual and empirical examination of justifications for dichotomization, Psychol Methods, 14, 349, 10.1037/a0016956

MacCallum, 2002, On the practice of dichotomization of quantitative variables, Psychol Methods, 7, 19, 10.1037/1082-989X.7.1.19

Holländer, 2004, Confidence intervals for the effect of a prognostic factor after selection of an ‘optimal’ cutpoint, Stat Med, 23, 1701, 10.1002/sim.1611

Marra, 2010, Penalised regression splines: theory and application to medical research, Stat Methods Med Res, 19, 107, 10.1177/0962280208096688

Randall, 2021, How did we get here: what are droplets and aerosols and how far do they go?. A historical perspective on the transmission of respiratory infectious diseases, Interface Focus, 11, 20210049, 10.1098/rsfs.2021.0049

Berk, 2013, Valid post-selection inference, Ann Stat, 41, 802, 10.1214/12-AOS1077

Whittingham, 2006, Why do we still use stepwise modelling in ecology and behaviour?, J Anim Ecol, 75, 1182, 10.1111/j.1365-2656.2006.01141.x

Hoeting, 2009, The importance of accounting for spatial and temporal correlation in analyses of ecological data, Ecol Appl, 19, 574, 10.1890/08-0836.1

Leeb, 2005, Model selection and inference: facts and fiction, Econ Theory, 21, 21, 10.1017/S0266466605050036

Leeb, 2008, Can one estimate the unconditional distribution of post-model-selection estimators?, Econ Theory, 24, 338, 10.1017/S0266466608080158

Cinelli, 2022, A crash course in good and bad controls, Sociol Methods Res, 10.1177/00491241221099552

Schisterman, 2009, Overadjustment bias and unnecessary adjustment in epidemiologic studies, Epidemiology, 20, 488, 10.1097/EDE.0b013e3181a819a1

Tobler, 1970, A computer movie simulating urban growth in the Detroit region, Econ Geogr, 46, 234, 10.2307/143141

Griffith, 1992, What is spatial autocorrelation? Reflections on the past 25 years of spatial statistics, Espace Geogr, 21, 265, 10.3406/spgeo.1992.3091

Cori, 2013, A new framework and software to estimate time-varying reproduction numbers during epidemics, Am J Epidemiol, 178, 1505, 10.1093/aje/kwt133

MacNab, 2011, On Gaussian Markov random fields and Bayesian disease mapping, Stat Methods Med Res, 20, 49, 10.1177/0962280210371561

Gómez-Rubio, 2021, Estimating spatial econometrics models with integrated nested Laplace approximation, Mathematics, 9, 2044, 10.3390/math9172044

Conley, 1999, GMM estimation with cross sectional dependence, J Econom, 92, 1, 10.1016/S0304-4076(98)00084-0

Dupont, 2022, Spatial+: A novel approach to spatial confounding, Biometrics, 78, 1279, 10.1111/biom.13656

Khan, 2023

Britton, 2019, Estimation in emerging epidemics: biases and remedies, J R Soc Interface, 16, 20180670, 10.1098/rsif.2018.0670

Lipsitch, 2015, Potential biases in estimating absolute and relative case-fatality risks during outbreaks, PLoS Negl Trop Dis, 9, e0003846, 10.1371/journal.pntd.0003846

Russell, 2020, Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020, Eurosurveillance, 25, 2000256, 10.2807/1560-7917.ES.2020.25.12.2000256

Alizon, 2022, Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020, Eurosurveillance, 27, 2100406, 10.2807/1560-7917.ES.2022.27.6.2100406

Hay, 2021, Estimating epidemiologic dynamics from cross-sectional viral load distributions, Science, 373, eabh0635, 10.1126/science.abh0635

Woodfine, 2015, Berkson's paradox in medical care, J Intern Med, 278, 424, 10.1111/joim.12363

Monge, 2023, The imprinting effect of Covid-19 vaccines: an expected selection bias in observational studies, BMJ, 381, e074404, 10.1136/bmj-2022-074404

Brenner, 1997, Variation of sensitivity, specificity, likelihood ratios and predictive values with disease prevalence, Stat Med, 16, 981, 10.1002/(SICI)1097-0258(19970515)16:9<981::AID-SIM510>3.0.CO;2-N

Westreich, 2014, Epidemiology visualized: The prosecutor's fallacy, Am J Epidemiol, 179, 1125, 10.1093/aje/kwu025

Keogh, 2020, STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1 — Basic theory and simple methods of adjustment, Stat Med, 39, 2197, 10.1002/sim.8532

Neuhaus, 1999, Bias and efficiency loss due to misclassified responses in binary regression, Biometrika, 86, 843, 10.1093/biomet/86.4.843

Loken, 2017, Measurement error and the replication crisis, Science, 355, 584, 10.1126/science.aal3618

Shaw, 2020, STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2 — More complex methods of adjustment and advanced topics, Stat Med, 39, 2232, 10.1002/sim.8531

Innes, 2021, The measurement error elephant in the room: challenges and solutions to measurement error in epidemiology, Epidemiol Rev, 43, 94, 10.1093/epirev/mxab011

Sedgwick, 2015, Understanding the ecological fallacy, BMJ, 351, h4773, 10.1136/bmj.h4773

von Kügelgen, 2021, Simpson's paradox in COVID-19 case fatality rates: a mediation analysis of age-related causal effects, IEEE Trans Artif Intell, 2, 18, 10.1109/TAI.2021.3073088

Lee, 2021, Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework, J Clin Epidemiol, 134, 79, 10.1016/j.jclinepi.2021.01.008

Enders, 2022

Seaman, 2013, What is meant by “missing at random”?, Stat Sci, 28, 257, 10.1214/13-STS415

Goldberg, 2021, Data missing not at random in mobile health research: assessment of the problem and a case for sensitivity analyses, J Med Internet Res, 23, e26749, 10.2196/26749

Lachin, 2016, Fallacies of last observation carried forward analyses, Clin Trials, 13, 161, 10.1177/1740774515602688

Eekhout, 2012, A systematic review of how they are reported and handled, Epidemiology, 23, 729, 10.1097/EDE.0b013e3182576cdb

Hunt, 2021, A systematic review of how missing data are handled and reported in multi-database pharmacoepidemiologic studies, Pharmacoepidemiol Drug Saf, 30, 819, 10.1002/pds.5245

Little, 2021, Missing data assumptions, Annu Rev Stat Its Appl, 8, 89, 10.1146/annurev-statistics-040720-031104

Adams-Huet, 2009, Bridging clinical investigators and statisticians: writing the statistical methodology for a research proposal, J Investig Med, 57, 818, 10.2310/JIM.0b013e3181c2996c

Silberzahn, 2018, Many analysts, one data set: making transparent how variations in analytic choices affect results, Adv Methods Pract Psychol Sci, 1, 337, 10.1177/2515245917747646

Greenhalgh, 2022, Adapt or die: how the pandemic made the shift from EBM to EBM+ more urgent, BMJ Evid-Based Med, 27, 253, 10.1136/bmjebm-2022-111952

Peng, 2015, The reproducibility crisis in science: A statistical counterattack, Significance, 12, 30, 10.1111/j.1740-9713.2015.00827.x

Ipsos, 2022