| Big data: A revolution that will transform how we live, work, and think S John Walker International Journal of Advertising 33 (1), 181-183, 2014 | 547* | 2014 |
| An introduction to g methods AI Naimi, SR Cole, EH Kennedy International journal of epidemiology 46 (2), 756-762, 2017 | 382 | 2017 |
| Stacked generalization: an introduction to super learning AI Naimi, LB Balzer European journal of epidemiology 33, 459-464, 2018 | 338 | 2018 |
| The parametric g-formula for time-to-event data: intuition and a worked example AP Keil, JK Edwards, DB Richardson, AI Naimi, SR Cole Epidemiology 25 (6), 889-897, 2014 | 220 | 2014 |
| Reflection on modern methods: demystifying robust standard errors for epidemiologists MA Mansournia, M Nazemipour, AI Naimi, GS Collins, MJ Campbell International Journal of Epidemiology 50 (1), 346-351, 2021 | 200 | 2021 |
| Constructing inverse probability weights for continuous exposures: a comparison of methods AI Naimi, EEM Moodie, N Auger, JS Kaufman Epidemiology 25 (2), 292-299, 2014 | 166 | 2014 |
| Estimating risk ratios and risk differences using regression AI Naimi, BW Whitcomb American journal of epidemiology 189 (6), 508-510, 2020 | 149 | 2020 |
| Mediation analysis for health disparities research AI Naimi, ME Schnitzer, EEM Moodie, LM Bodnar American journal of epidemiology 184 (4), 315-324, 2016 | 132 | 2016 |
| Extreme heat and risk of early delivery among preterm and term pregnancies N Auger, AI Naimi, A Smargiassi, E Lo, T Kosatsky Epidemiology 25 (3), 344-350, 2014 | 120 | 2014 |
| Mediation misgivings: ambiguous clinical and public health interpretations of natural direct and indirect effects AI Naimi, JS Kaufman, RF MacLehose International journal of epidemiology 43 (5), 1656-1661, 2014 | 115 | 2014 |
| Secular trends in preeclampsia incidence and outcomes in a large Canada database: a longitudinal study over 24 years N Auger, ZC Luo, AM Nuyt, JS Kaufman, AI Naimi, RW Platt, WD Fraser Canadian Journal of Cardiology 32 (8), 987. e15-987. e23, 2016 | 88 | 2016 |
| Challenges in obtaining valid causal effect estimates with machine learning algorithms AI Naimi, AE Mishler, EH Kennedy American Journal of Epidemiology 192 (9), 1536-1544, 2023 | 80 | 2023 |
| Analysis of occupational asbestos exposure and lung cancer mortality using the g formula SR Cole, DB Richardson, H Chu, AI Naimi American journal of epidemiology 177 (9), 989-996, 2013 | 72 | 2013 |
| Human chorionic gonadotropin partially mediates phthalate association with male and female anogenital distance JJ Adibi, MK Lee, AI Naimi, E Barrett, RH Nguyen, S Sathyanarayana, ... The Journal of Clinical Endocrinology & Metabolism 100 (9), E1216-E1224, 2015 | 70 | 2015 |
| Altered mitochondrial regulation in quadriceps muscles of patients with COPD AI Naimi, J Bourbeau, H Perrault, J Baril, C Wright‐Paradis, A Rossi, ... Clinical physiology and functional imaging 31 (2), 124-131, 2011 | 70 | 2011 |
| Teaching yourself about structural racism will improve your machine learning WR Robinson, A Renson, AI Naimi Biostatistics 21 (2), 339-344, 2020 | 53 | 2020 |
| Causal inference in occupational epidemiology: accounting for the healthy worker effect by using structural nested models AI Naimi, DB Richardson, SR Cole American journal of epidemiology 178 (12), 1681-1686, 2013 | 53 | 2013 |
| Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes LM Bodnar, AR Cartus, SI Kirkpatrick, KP Himes, EH Kennedy, ... The American journal of clinical nutrition 111 (6), 1235-1243, 2020 | 51 | 2020 |
| Machine learning for fetal growth prediction AI Naimi, RW Platt, JC Larkin Epidemiology 29 (2), 290-298, 2018 | 49 | 2018 |
| AIPW: An R Package for Augmented Inverse Probability–Weighted Estimation of Average Causal Effects Y Zhong, EH Kennedy, LM Bodnar, AI Naimi American Journal of Epidemiology 190 (12), 2690-2699, 2021 | 43 | 2021 |
Stephen R ColeProfessor of Epidemiology, UNC Chapel HillE-mailová adresa ověřena na: unc.edu