An introduction to g methods AI Naimi, SR Cole, EH Kennedy International journal of epidemiology 46 (2), 756-762, 2017 | 334 | 2017 |
Stacked generalization: an introduction to super learning AI Naimi, LB Balzer European journal of epidemiology 33, 459-464, 2018 | 290 | 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 | 208 | 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 | 164 | 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 | 157 | 2014 |
Estimating risk ratios and risk differences using regression AI Naimi, BW Whitcomb American journal of epidemiology 189 (6), 508-510, 2020 | 120 | 2020 |
Mediation analysis for health disparities research AI Naimi, ME Schnitzer, EEM Moodie, LM Bodnar American journal of epidemiology 184 (4), 315-324, 2016 | 115 | 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 | 114 | 2014 |
Big data: a revolution that will transform how we live, work, and think AI Naimi, DJ Westreich American Journal of Epidemiology 179 (9), 1143-1144, 2014 | 111* | 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 | 105 | 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 | 84 | 2016 |
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 | 71 | 2013 |
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 | 68 | 2011 |
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 | 66 | 2015 |
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 | 60* | 2023 |
Teaching yourself about structural racism will improve your machine learning WR Robinson, A Renson, AI Naimi Biostatistics 21 (2), 339-344, 2020 | 51 | 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 | 51 | 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 | 49 | 2020 |
Machine learning for fetal growth prediction AI Naimi, RW Platt, JC Larkin Epidemiology 29 (2), 290-298, 2018 | 44 | 2018 |
Estimating the effect of cumulative occupational asbestos exposure on time to lung cancer mortality: using structural nested failure-time models to account for healthy-worker … AI Naimi, SR Cole, MG Hudgens, DB Richardson Epidemiology 25 (2), 246-254, 2014 | 40 | 2014 |