Mahesh Parmar

58.1k total citations · 16 hit papers
299 papers, 27.0k citations indexed

About

Mahesh Parmar is a scholar working on Pulmonary and Respiratory Medicine, Statistics and Probability and Oncology. According to data from OpenAlex, Mahesh Parmar has authored 299 papers receiving a total of 27.0k indexed citations (citations by other indexed papers that have themselves been cited), including 107 papers in Pulmonary and Respiratory Medicine, 84 papers in Statistics and Probability and 70 papers in Oncology. Recurrent topics in Mahesh Parmar's work include Statistical Methods in Clinical Trials (82 papers), Prostate Cancer Treatment and Research (59 papers) and Ovarian cancer diagnosis and treatment (40 papers). Mahesh Parmar is often cited by papers focused on Statistical Methods in Clinical Trials (82 papers), Prostate Cancer Treatment and Research (59 papers) and Ovarian cancer diagnosis and treatment (40 papers). Mahesh Parmar collaborates with scholars based in United Kingdom, United States and Canada. Mahesh Parmar's co-authors include Lesley Stewart, Valter Torri, Patrick Royston, Richard Stephens, Matthew R. Sydes, Laurence S. Freedman, David J. Spiegelhalter, Richard Kaplan, Stanley Dische and A Harvey and has published in prestigious journals such as The Lancet, Nature Medicine and Journal of Clinical Oncology.

In The Last Decade

Mahesh Parmar

288 papers receiving 26.3k citations

Hit Papers

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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.

Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints

1998 3.9k citations Mahesh Parmar et al. Statistics in Medicine profile →
Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study

2017 2.2k citations Louise Brown, Mahesh Parmar et al. The Lancet profile →
Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects

2002 1.1k citations Patrick Royston, Mahesh Parmar Statistics in Medicine profile →
Preoperative radiotherapy versus selective postoperative chemoradiotherapy in patients with rectal cancer (MRC CR07 and NCIC-CTG C016): a multicentre, randomised trial

2009 1.1k citations Richard Stephens, Mahesh Parmar et al. The Lancet profile →
Paclitaxel plus platinum-based chemotherapy versus conventional platinum-based chemotherapy in women with relapsed ovarian cancer: the ICON4/AGO-OVAR-2.2 trial

2003 885 citations Mahesh Parmar, Gunnar B. Kristensen et al. The Lancet profile →
Effect of the plane of surgery achieved on local recurrence in patients with operable rectal cancer: a prospective study using data from the MRC CR07 and NCIC-CTG CO16 randomised clinical trial

2009 708 citations Mahesh Parmar, Richard Stephens et al. The Lancet profile →
Escalated-dose versus standard-dose conformal radiotherapy in prostate cancer: first results from the MRC RT01 randomised controlled trial

2007 686 citations Matthew R. Sydes, John M. Russell et al. profile →
Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome

2013 623 citations Patrick Royston, Mahesh Parmar BMC Medical Research Methodology profile →
Survival Analysis: A Practical Approach.

1996 596 citations Mahesh Parmar et al. profile →
Meta-analysis of the literature or of individual patient data: is there a difference?

1993 549 citations Mahesh Parmar et al. The Lancet profile →
Dexamethasone and supportive care with or without whole brain radiotherapy in treating patients with non-small cell lung cancer with brain metastases unsuitable for resection or stereotactic radiotherapy (QUARTZ): results from a phase 3, non-inferiority, randomised trial

2016 453 citations P. Mulvenna, Matthew Nankivell et al. The Lancet profile →
Definitions for Response and Progression in Ovarian Cancer Clinical Trials Incorporating RECIST 1.1 and CA 125 Agreed by the Gynecological Cancer Intergroup (GCIG)

2011 452 citations Ignace Vergote, Gunnar B. Kristensen et al. profile →
Continuous hyperfractionated accelerated radiotherapy (CHART) versus conventional radiotherapy in non-small-cell lung cancer: a randomised multicentre trial

1997 421 citations Mahesh Parmar et al. The Lancet profile →
Combined androgen deprivation therapy and radiation therapy for locally advanced prostate cancer: a randomised, phase 3 trial

2011 382 citations Mahesh Parmar, Matthew R. Sydes et al. The Lancet profile →
Escalated-dose versus control-dose conformal radiotherapy for prostate cancer: long-term results from the MRC RT01 randomised controlled trial

2014 344 citations Claire Murphy, John M. Russell et al. profile →
Adjuvant or early salvage radiotherapy for the treatment of localised and locally advanced prostate cancer: a prospectively planned systematic review and meta-analysis of aggregate data

2020 185 citations Matthew R. Sydes, Mahesh Parmar et al. The Lancet profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mahesh Parmar United Kingdom	71	11.0k	8.3k	6.7k	2.8k	2.6k	299	27.0k
Marc Buyse Belgium	80	7.7k 0.7×	15.5k 1.9×	4.9k 0.7×	1.7k 0.6×	2.9k 1.1×	404	29.0k
Eric J. Feuer United States	68	11.0k 1.0×	17.9k 2.1×	5.8k 0.9×	2.3k 0.8×	910 0.3×	252	40.4k
Larry Rubinstein United States	54	20.2k 1.8×	22.4k 2.7×	7.3k 1.1×	7.4k 2.6×	1.9k 0.7×	196	47.6k
Deborah Schrag United States	96	9.1k 0.8×	19.8k 2.4×	7.8k 1.2×	1.9k 0.7×	835 0.3×	439	38.7k
Lori E. Dodd United States	28	9.5k 0.9×	11.4k 1.4×	4.2k 0.6×	4.4k 1.6×	698 0.3×	72	24.2k
Val Gebski Australia	67	7.1k 0.6×	7.7k 0.9×	7.0k 1.0×	1.1k 0.4×	412 0.2×	470	20.5k
Laurence Collette Belgium	79	15.2k 1.4×	12.7k 1.5×	11.3k 1.7×	3.0k 1.1×	858 0.3×	317	31.6k
Michael W. Kattan United States	121	31.6k 2.9×	9.4k 1.1×	11.3k 1.7×	5.0k 1.8×	2.9k 1.1×	807	52.8k
Richard Kaplan United Kingdom	48	20.2k 1.8×	22.4k 2.7×	7.8k 1.2×	7.5k 2.7×	893 0.3×	173	46.0k
Patrick C. Walsh United States	104	29.5k 2.7×	3.9k 0.5×	8.0k 1.2×	1.9k 0.7×	1.2k 0.4×	577	40.5k

Countries citing papers authored by Mahesh Parmar

Since Specialization

Citations

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

Fields of papers citing papers by Mahesh Parmar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mahesh Parmar

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

Oza, Bhavna, Eleni Frangou, Tim Eisen, et al.. (2025). Determining the Impact of Histology on the Incidence, Pattern, and Timing of Recurrences in Patients with Renal Cell Carcinoma: A Pooled Analysis from the SORCE and ASSURE Trials. European Urology Open Science. 79. 19–26.

James, Nicholas D., Hannah Rush, Matthew Nankivell, et al.. (2025). Transdermal oestradiol (tE2) patches as androgen deprivation therapy (ADT): Efficacy and safety of combining with androgen receptor pathway inhibitors (ARPIs) in metastatic (M1) prostate cancer—Randomised comparison from the STAMPEDE trial platform.. Journal of Clinical Oncology. 43(5_suppl). 21–21. 1 indexed citations

Frangou, Eleni, Grant D. Stewart, Ian D. Davis, et al.. (2025). LBA93 First results from RAMPART: An international phase III randomised-controlled trial of adjuvant durvalumab monotherapy or combined with tremelimumab for resected primary renal cell carcinoma (RCC) led by MRC CTU at UCL. Annals of Oncology. 36. S1635–S1635.

Parmar, Mahesh, et al.. (2023). A Comparative Analysis for Filter-Based Feature Selection Techniques with Tree-based Classification. International Journal on Recent and Innovation Trends in Computing and Communication. 11(10s). 360–369.

Jones, Craig, Peter Dutey‐Magni, Janet E. Brown, et al.. (2023). 1768MO Incidence of fracture related hospitalisations in men with de novo high risk localised and metastatic hormone sensitive prostate cancer: Analysis of routinely collected healthcare data from the STAMPEDE docetaxel and zoledronic acid comparisons. Annals of Oncology. 34. S956–S957. 4 indexed citations

Parmar, Mahesh, et al.. (2023). Sentiment Analysis Using Deep Neural Network 1D Convolutional with Long Short Term Memory. 7. 1–5. 1 indexed citations

Tunis, Sean, Mahesh Parmar, Howard A. Burris, Michelle M. LeBeau, & Larry G. Kessler. (2023). Approaches and data needed for real-world evaluation of multicancer early detection tests.. Journal of Clinical Oncology. 41(16_suppl). e15069–e15069. 1 indexed citations

Limkin, Elaine Johanna, Pierre Blanchard, Benjamin Lacas, et al.. (2023). Season of radiotherapy and outcomes of head & neck cancer patients in the MACH-NC & MARCH meta-analyses. Radiotherapy and Oncology. 190. 110011–110011. 1 indexed citations

Dakin, Rachel, J Williamson, Judith Newton, et al.. (2022). Motor Neuron Disease Systematic Multi-Arm Adaptive Randomised Trial (MND-SMART): a multi-arm, multi-stage, adaptive, platform, phase III randomised, double-blind, placebo-controlled trial of repurposed drugs in motor neuron disease. BMJ Open. 12(7). e064173–e064173. 18 indexed citations

10.

Li, Vivien, Baptiste Leurent, Frederik Barkhof, et al.. (2022). Designing Multi-arm Multistage Adaptive Trials for Neuroprotection in Progressive Multiple Sclerosis. Neurology. 98(18). 754–764. 12 indexed citations

11.

Vergote, Ignace, Corneel Coens, Matthew Nankivell, et al.. (2019). Neoadjuvant Chemotherapy Versus Debulking Surgery in Advanced Tubo-Ovarian Cancers: Pooled Analysis of Individual Patient Data From the EORTC 55971 and CHORUS Trials. Obstetrical & Gynecological Survey. 74(3). 156–158. 30 indexed citations

12.

Blyuss, Oleg, Matthew Burnell, Andy Ryan, et al.. (2018). Comparison of Longitudinal CA125 Algorithms as a First-Line Screen for Ovarian Cancer in the General Population. Clinical Cancer Research. 24(19). 4726–4733. 39 indexed citations

13.

Mulvenna, P., Matthew Nankivell, Rachael Barton, et al.. (2016). Dexamethasone and supportive care with or without whole brain radiotherapy in treating patients with non-small cell lung cancer with brain metastases unsuitable for resection or stereotactic radiotherapy (QUARTZ): results from a phase 3, non-inferiority, randomised trial. The Lancet. 388(10055). 2004–2014. 453 indexed citations breakdown →

14.

Burnell, Matthew, Catherine A. Cox, Andy Ryan, et al.. (2014). Association of skirt size and postmenopausal breast cancer risk in older women: a cohort study within the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). BMJ Open. 4(9). e005400–e005400. 6 indexed citations

15.

Royston, Patrick & Mahesh Parmar. (2013). Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome. BMC Medical Research Methodology. 13(1). 152–152. 623 indexed citations breakdown →

16.

Hatton, M., Matthew Nankivell, Stephen Falk, et al.. (2010). Induction Chemotherapy and Continuous Hyperfractionated Accelerated Radiotherapy (CHART) for Patients With Locally Advanced Inoperable Non–Small-Cell Lung Cancer: The MRC INCH Randomized Trial. International Journal of Radiation Oncology*Biology*Physics. 81(3). 712–718. 26 indexed citations

17.

Menon, Usha, Matthew Burnell, Aarti Sharma, et al.. (2007). Decline in use of hormone therapy among postmenopausal women in the United Kingdom. Menopause The Journal of The North American Menopause Society. 14(3). 462–467. 38 indexed citations

18.

Babiker, Abdel, et al.. (2006). Evaluation of sample size and power for multi-arm survival trials allowing for non-uniform accrual, non-proportional hazards, loss to follow-up and cross-over. Statistics in Medicine. 25(15). 2521–2542. 46 indexed citations

19.

Grant, Adrian, D. G. Altman, Abdel Babiker, et al.. (2005). Issues in data monitoring and interim analysis of trials. Health Technology Assessment. 9(7). 1–238, iii. 205 indexed citations

20.

Panici, Pierluigi Benedetti, Adriana Bermúdez, Anne Floquet, et al.. (2003). Neoadjuvant chemotherapy for locally advanced cervical cancer: a systematic review and meta-analysis of individual patient data from 21 randomised trials. UCL Discovery (University College London). 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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