Rachael V. Phillips

2.2k total citations · 1 hit paper
20 papers, 444 citations indexed

About

Rachael V. Phillips is a scholar working on Statistics and Probability, Molecular Biology and Epidemiology. According to data from OpenAlex, Rachael V. Phillips has authored 20 papers receiving a total of 444 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Statistics and Probability, 4 papers in Molecular Biology and 4 papers in Epidemiology. Recurrent topics in Rachael V. Phillips's work include Advanced Causal Inference Techniques (10 papers), Statistical Methods and Inference (5 papers) and Statistical Methods and Bayesian Inference (4 papers). Rachael V. Phillips is often cited by papers focused on Advanced Causal Inference Techniques (10 papers), Statistical Methods and Inference (5 papers) and Statistical Methods and Bayesian Inference (4 papers). Rachael V. Phillips collaborates with scholars based in United States, United Kingdom and Netherlands. Rachael V. Phillips's co-authors include Alan Hubbard, Ivana Malenica, Romain Pirracchio, Andrew Bishara, Jean Feng, Leo Anthony Celi, Hana Lee, Susan Gruber, Mark J. van der Laan and Martyn T. Smith and has published in prestigious journals such as American Journal of Epidemiology, Environment International and International Journal of Epidemiology.

In The Last Decade

Rachael V. Phillips

20 papers receiving 435 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare

2022 195 citations Jean Feng, Rachael V. Phillips et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Rachael V. Phillips United States	11	119	86	75	63	57	20	444
Ignacio Atal France	11	46 0.4×	53 0.6×	39 0.5×	29 0.5×	21 0.4×	20	516
Anna Ostropolets United States	11	19 0.2×	64 0.7×	24 0.3×	22 0.3×	57 1.0×	33	459
Elizabeth Lorenzi United States	6	38 0.3×	42 0.5×	27 0.4×	21 0.3×	65 1.1×	15	273
Jessica Gronsbell Canada	11	14 0.1×	149 1.7×	29 0.4×	13 0.2×	102 1.8×	25	423
William Mitchell United States	9	137 1.2×	59 0.7×	3 0.0×	110 1.7×	28 0.5×	22	421
Alind Gupta Canada	10	10 0.1×	40 0.5×	31 0.4×	23 0.4×	37 0.6×	20	300
Qinyu Zhao China	13	30 0.3×	50 0.6×	12 0.2×	42 0.7×	100 1.8×	28	582
Yiyue Lou United States	10	46 0.4×	77 0.9×	26 0.3×	652 10.3×	20 0.4×	32	969
Thomas Bolton United Kingdom	6	44 0.4×	135 1.6×	5 0.1×	58 0.9×	21 0.4×	7	466
Jacqueline Cellini United States	10	135 1.1×	58 0.7×	3 0.0×	64 1.0×	12 0.2×	15	438

Countries citing papers authored by Rachael V. Phillips

Since Specialization

Citations

This map shows the geographic impact of Rachael V. Phillips's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rachael V. Phillips with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rachael V. Phillips more than expected).

Fields of papers citing papers by Rachael V. Phillips

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rachael V. Phillips. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rachael V. Phillips. The network helps show where Rachael V. Phillips may publish in the future.

Co-authorship network of co-authors of Rachael V. Phillips

This figure shows the co-authorship network connecting the top 25 collaborators of Rachael V. Phillips. A scholar is included among the top collaborators of Rachael V. Phillips based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rachael V. Phillips. Rachael V. Phillips is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Smith, Matthew J., Rachael V. Phillips, Camille Maringe, & Miguel Ángel Luque-Fernández. (2025). Performance of Cross‐Validated Targeted Maximum Likelihood Estimation. Statistics in Medicine. 44(15-17). e70185–e70185. 1 indexed citations

2.

Li, Haodong, Jeremy Coyle, Rachael V. Phillips, et al.. (2024). Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study. JMIR Public Health and Surveillance. 10. e53322–e53322. 1 indexed citations

3.

Phillips, Rachael V. & Mark J. van der Laan. (2024). Comment on “Randomization Tests to Address Disruptions in Clinical Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions”. Statistics in Biopharmaceutical Research. 16(4). 417–422. 1 indexed citations

4.

Smith, Matthew J., Rachael V. Phillips, Miguel Ángel Luque-Fernández, & Camille Maringe. (2023). Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review. Annals of Epidemiology. 86. 34–48.e28. 18 indexed citations

5.

Phillips, Rachael V., Mark J. van der Laan, Hana Lee, & Susan Gruber. (2023). Practical considerations for specifying a super learner. International Journal of Epidemiology. 52(4). 1276–1285. 58 indexed citations

6.

Malenica, Ivana, Rachael V. Phillips, Antoine Chambaz, et al.. (2023). Personalized online ensemble machine learning with applications for dynamic data streams. Statistics in Medicine. 42(7). 1013–1044. 3 indexed citations

7.

Smith, Matthew J., Rachael V. Phillips, Miguel Ángel Luque-Fernández, & Camille Maringe. (2023). Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review. arXiv (Cornell University). 1 indexed citations

8.

Gruber, Susan, Rachael V. Phillips, Hana Lee, John Concato, & Mark van der Laan. (2023). Evaluating and improving real-world evidence with Targeted Learning. BMC Medical Research Methodology. 23(1). 178–178. 4 indexed citations

9.

Gruber, Susan, Rachael V. Phillips, Hana Lee, et al.. (2023). Targeted Learning: Toward a Future Informed by Real-World Evidence. Statistics in Biopharmaceutical Research. 16(1). 11–25. 7 indexed citations

10.

Feng, Jean, Rachael V. Phillips, Ivana Malenica, et al.. (2022). Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. npj Digital Medicine. 5(1). 66–66. 195 indexed citations breakdown →

11.

Li, Haodong, Sonali Rosete, Jeremy Coyle, et al.. (2022). Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials. Statistics in Medicine. 41(12). 2132–2165. 8 indexed citations

12.

Gruber, Susan, Hana Lee, Rachael V. Phillips, Martin Ho, & Mark van der Laan. (2022). Developing a Targeted Learning-Based Statistical Analysis Plan. Statistics in Biopharmaceutical Research. 15(3). 468–475. 14 indexed citations

13.

Horvitz, Nir, Rachael V. Phillips, Zhongqi Miao, et al.. (2022). A comparison of COVID-19 outbreaks across US Combined Statistical Areas using new methods for estimating R 0 and social distancing behaviour. Epidemics. 41. 100640–100640. 2 indexed citations

14.

Phillips, Rachael V., Andrés Cárdenas, Alan Hubbard, et al.. (2022). Epigenome-wide association studies of occupational exposure to benzene and formaldehyde. Epigenetics. 17(13). 2259–2277. 11 indexed citations

15.

Gruber, Susan, Rachael V. Phillips, Hana Lee, & Mark J. van der Laan. (2022). Data-Adaptive Selection of the Propensity Score Truncation Level for Inverse-Probability–Weighted and Targeted Maximum Likelihood Estimators of Marginal Point Treatment Effects. American Journal of Epidemiology. 191(9). 1640–1651. 19 indexed citations

16.

Cárdenas, Andrés, Roel Vermeulen, Raj P. Fadadu, et al.. (2021). Epigenetic aging biomarkers and occupational exposure to benzene, trichloroethylene and formaldehyde. Environment International. 158. 106871–106871. 40 indexed citations

17.

Phillips, Rachael V., Alan Hubbard, Andreas Stahl, et al.. (2020). Chronic arsenic exposure impairs adaptive thermogenesis in male C57BL/6J mice. American Journal of Physiology-Endocrinology and Metabolism. 318(5). E667–E677. 12 indexed citations

18.

Holden, Karl, Wadah Ibrahim, Dahlia Salman, et al.. (2020). Use of the ReCIVA device in breath sampling of patients with acute breathlessness: a feasibility study. ERJ Open Research. 6(4). 119–2020. 16 indexed citations

19.

Legrand, Matthieu, Rachael V. Phillips, Ivana Malenica, et al.. (2020). Differences in clinical deterioration among three sub-phenotypes of COVID-19 patients at the time of first positive test: results from a clustering analysis. Intensive Care Medicine. 47(1). 113–115. 15 indexed citations

20.

Phillips, Rachael V., Linda Rieswijk, Alan Hubbard, et al.. (2019). Human exposure to trichloroethylene is associated with increased variability of blood DNA methylation that is enriched in genes and pathways related to autoimmune disease and cancer. Epigenetics. 14(11). 1112–1124. 18 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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