Zbigniew Kadziola

1.7k total citations · 1 hit paper
54 papers, 1.3k citations indexed

About

Zbigniew Kadziola is a scholar working on Cardiology and Cardiovascular Medicine, Economics and Econometrics and Statistics and Probability. According to data from OpenAlex, Zbigniew Kadziola has authored 54 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cardiology and Cardiovascular Medicine, 11 papers in Economics and Econometrics and 9 papers in Statistics and Probability. Recurrent topics in Zbigniew Kadziola's work include Health Systems, Economic Evaluations, Quality of Life (11 papers), Advanced Causal Inference Techniques (8 papers) and Venous Thromboembolism Diagnosis and Management (6 papers). Zbigniew Kadziola is often cited by papers focused on Health Systems, Economic Evaluations, Quality of Life (11 papers), Advanced Causal Inference Techniques (8 papers) and Venous Thromboembolism Diagnosis and Management (6 papers). Zbigniew Kadziola collaborates with scholars based in United States, United Kingdom and China. Zbigniew Kadziola's co-authors include Nicholas R. Lemoine, Mark N. Levine, Ajay K. Kakkar, Michael J. Thomas, R C N Williamson, Gordon Rustin, Mary Quigley, V V Kakkar, Alan Brnabic and F. De Lorenzo and has published in prestigious journals such as Journal of Clinical Oncology, Blood and PLoS ONE.

In The Last Decade

Zbigniew Kadziola

50 papers receiving 1.2k 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.

Low Molecular Weight Heparin, Therapy With Dalteparin, and Survival in Advanced Cancer: The Fragmin Advanced Malignancy Outcome Study (FAMOUS)

2004 519 citations Zbigniew Kadziola et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Zbigniew Kadziola United States	15	543	382	215	187	148	54	1.3k
Frederiek F van Doormaal Netherlands	14	581 1.1×	340 0.9×	280 1.3×	210 1.1×	227 1.5×	19	1.4k
Hüseyin Yüce United States	21	220 0.4×	414 1.1×	149 0.7×	78 0.4×	8 0.1×	87	1.1k
Marina Panova‐Noeva Germany	19	330 0.6×	342 0.9×	365 1.7×	178 1.0×	26 0.2×	63	1.3k
Joseph F. O’Donnell United States	13	212 0.4×	116 0.3×	234 1.1×	73 0.4×	51 0.3×	29	975
Irina Proskorovsky United States	17	55 0.1×	169 0.4×	239 1.1×	262 1.4×	5 0.0×	65	1.2k
Jan Feyzi United States	13	51 0.1×	325 0.9×	1.1k 5.0×	112 0.6×	25 0.2×	15	2.4k
Mitchell Silver United States	18	606 1.1×	397 1.0×	39 0.2×	458 2.4×	259 1.8×	50	1.4k
Sorrel Wolowacz United States	16	268 0.5×	435 1.1×	38 0.2×	248 1.3×	6 0.0×	50	1.1k
Krzysztof Piotr Malinowski Poland	20	117 0.2×	397 1.0×	90 0.4×	303 1.6×	14 0.1×	146	1.3k
Geneviève Plu‐Bureau France	34	421 0.8×	378 1.0×	448 2.1×	199 1.1×	9 0.1×	120	3.9k

Countries citing papers authored by Zbigniew Kadziola

Since Specialization

Citations

This map shows the geographic impact of Zbigniew Kadziola'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 Zbigniew Kadziola with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zbigniew Kadziola more than expected).

Fields of papers citing papers by Zbigniew Kadziola

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zbigniew Kadziola. 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 Zbigniew Kadziola. The network helps show where Zbigniew Kadziola may publish in the future.

Co-authorship network of co-authors of Zbigniew Kadziola

This figure shows the co-authorship network connecting the top 25 collaborators of Zbigniew Kadziola. A scholar is included among the top collaborators of Zbigniew Kadziola 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 Zbigniew Kadziola. Zbigniew Kadziola 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

Done, Nicolae, Alan Brnabic, Ilya Lipkovich, et al.. (2026). Comparative effectiveness of tirzepatide and semaglutide for obesity management in US clinical practice: a 6-month retrospective cohort study. Journal of Endocrinological Investigation. 49(2). 413–423.

Brnabic, Alan, Ilya Lipkovich, Zbigniew Kadziola, et al.. (2025). Next-Generation Sequencing–Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non–Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods. JMIR Cancer. 11. e64399–e64399.

Tan, Xiaoqing, Shu Yang, Wenyu Ye, et al.. (2025). Double machine learning methods for estimating average treatment effects: a comparative study. Journal of Biopharmaceutical Statistics. 35(6). 1176–1195.

Pinter, Andreas, Antonio Costanzo, Sanjay Khattri, et al.. (2023). Comparative Effectiveness and Durability of Biologics in Clinical Practice: Month 12 Outcomes from the International, Observational Psoriasis Study of Health Outcomes (PSoHO). Dermatology and Therapy. 14(6). 1479–1493. 8 indexed citations

Zagar, Anthony J., Zbigniew Kadziola, Ilya Lipkovich, David Madigan, & D Faries. (2022). Evaluating bias control strategies in observational studies using frequentist model averaging. Journal of Biopharmaceutical Statistics. 32(2). 247–276. 11 indexed citations

Cui, Zhanglin Lin, Zbigniew Kadziola, Ilya Lipkovich, et al.. (2021). Predicting optimal treatment regimens for patients with HR+/HER2- breast cancer using machine learning based on electronic health records. Journal of Comparative Effectiveness Research. 10(9). 777–795. 6 indexed citations

Yang, Shu, et al.. (2021). Practical recommendations on double score matching for estimating causal effects. Statistics in Medicine. 41(8). 1421–1445. 8 indexed citations

Saure, Daniel, Alexander Schacht, Zbigniew Kadziola, & Alan Brnabic. (2020). Combination of several matching adjusted indirect comparisons (MAICs) with an application in psoriasis. Pharmaceutical Statistics. 19(5). 532–540. 3 indexed citations

Fautrel, Bruno, Baojin Zhu, Peter C. Taylor, et al.. (2020). Comparative effectiveness of improvement in pain and physical function for baricitinib versus adalimumab, tocilizumab and tofacitinib monotherapies in rheumatoid arthritis patients who are naïve to treatment with biologic or conventional synthetic disease-modifying antirheumatic drugs: a matching-adjusted indirect comparison. RMD Open. 6(1). e001131–e001131. 27 indexed citations

10.

Cui, Zhanglin Lin, et al.. (2020). MS3 PREDICTING OPTIMAL TREATMENT REGIMENS FOR HR+/HER2- BREAST CANCER BASED ON ELECTRONIC HEALTH RECORDS USING RANDOM FOREST. Value in Health. 23. S8–S9. 1 indexed citations

11.

Petto, Helmut, Zbigniew Kadziola, Alan Brnabic, Daniel Saure, & Mark Belger. (2018). Alternative Weighting Approaches for Anchored Matching-Adjusted Indirect Comparisons via a Common Comparator. Value in Health. 22(1). 85–91. 24 indexed citations

12.

Belger, Mark, Alan Brnabic, Zbigniew Kadziola, Helmut Petto, & D Faries. (2015). Alternative Weighting Approaches For Matching Adjusted Indirect Comparisons (Maic). Value in Health. 18(3). A31–A32. 6 indexed citations

13.

Montgomery, William, et al.. (2015). Correlation between changes in quality of life and symptomatic improvement in Chinese patients switched from typical antipsychotics to olanzapine. Neuropsychiatric Disease and Treatment. 11. 177–177. 5 indexed citations

14.

Kadziola, Zbigniew, Shu Yang, Guido W. Imbens, Zhanglin Lin Cui, & D Faries. (2014). Propensity Score Matching and Subclassification With Multi-Level Treatments. Value in Health. 17(7). A587–A587. 1 indexed citations

15.

Stensland, Michael D., et al.. (2014). Factors associated with early response to olanzapine and clinical and functional outcomes of early responders treated for schizophrenia in the People’s Republic of China. Neuropsychiatric Disease and Treatment. 10. 869–869. 4 indexed citations

16.

Novick, Diego, William Montgomery, Jaume Aguado, et al.. (2013). Which somatic symptoms are associated with an unfavorable course in Asian patients with major depressive disorder?. Journal of Affective Disorders. 149(1-3). 182–188. 71 indexed citations

17.

Montgomery, William, et al.. (2012). PMH5 Does Participation in a Weight Control Program Also Improve Clinical and Functional Outcomes for Chinese Patients with Schizophrenia Treated with Olanzapine?. Value in Health. 15(7). A669–A669. 1 indexed citations

18.

Li, Chien-Te, Salih Zeki Güçlü, Shukui Qin, et al.. (2011). Smoking and Prognostic Factors in an Observational Setting in Patients with Advanced Non-Small Cell Lung Carcinoma. Journal of Cancer. 2. 52–61. 14 indexed citations

19.

Gebska, Milena A., et al.. (2003). Low-molecular-weight heparin in the acute and long-term treatment of deep vein thrombosis. Thrombosis and Haemostasis. 89(4). 674–680. 67 indexed citations

20.

Lorenzo, F. De, Zbigniew Kadziola, & V. V. Kakkar. (1999). Sympathetic activation and myocardial ischaemia: a role for cold adaptation?. Clinical Science. 96(6). 663–663. 6 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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