Sains Malaysiana 55(8)(2026): 1310-1323

http://doi.org/10.17576/jsm-2026-5508-07
 

A Comparison of Network Meta Analysis using Hazard Ratios and RMST in Clinical Data

(Perbandingan Meta Analisis Rangkaian menggunakan Nisbah Bahaya dan RMST dalam Data Klinikal)

 

AMRITENDU BHATTACHARYA1, RAVILISETTY REVATHI2,* & BOYA VENKATESU3

 

1School of Technology, Woxsen University, Kamkole, Hyderabad, 502345, Telangana, India

2Department of Mathematics, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, 603110, Tamil Nadu, India

3School of Business, Woxsen University, Kamkole, Hyderabad, 502345, Telangana, India

 

Received: 4 August 2025/Accepted: 5 August 2026

 

Abstract

Hazard Ratio based Network Meta-Analysis (NMA) has gained popularity in evidence synthesis in data with time-to-event outcomes. However, the most commonly used Cox-Proportional Hazard ratio suffers due to lack of baseline information utilization. The proportionality assumption, which assumes that ratio of the hazards between any two treatment arms or strata is constant over time for the entire duration of study, is also violated in most Cox-regression models. In the current work we demonstrate few of the alternate approaches by firstly reconstructing the time-to-event dataset from Kaplan Meier curves from results of clinical trials available in public domain using relevant software such as ‘IPDfromKM’ and ‘DigitizeIt’ and then recomputing the restricted mean survival time (RMST) estimates as well as parametric estimates assuming exponential, Weibull and Gompertz distributions to be used respectively, each in a separate NMA. The methods are compared and the advantages of each approach is highlighted. For illustration purpose, Kaplan Meier based results from four trials of Advanced Renal Cell Cancer, are used for analysis. The results of Cox-Proportional Hazard Ratio were compared with parametric distribution-based Hazard Ratio obtained in our analysis. The RMST estimates-based analysis provided additional insights. Based on the NMA, the comparative treatment landscape is provided in terms of a treatment ranking of the included combination therapies.

Keywords: Bayesian methods in meta-analysis; hazard ratio estimation; network meta-analysis; restricted mean survival time; survival analysis

Abstrak

Meta-Analisis Rangkaian (NMA) berasaskan Nisbah Bahaya telah memperoleh populariti dalam sintesis bukti dalam data dengan hasil masa-ke-peristiwa. Walau bagaimanapun, nisbah Bahaya Perkadaran Cox yang paling biasa digunakan terjejas disebabkan oleh kekurangan penggunaan maklumat asas. Andaian perkadaran yang mengandaikan bahawa nisbah bahaya antara mana-mana dua cabang atau strata rawatan adalah malar dari semasa ke semasa sepanjang tempoh kajian, juga dilanggar dalam kebanyakan model regresi Cox. Dalam kajian semasa, kami menunjukkan beberapa pendekatan alternatif dengan terlebih dahulu membina semula set data masa-ke-peristiwa daripada lengkung Kaplan Meier daripada keputusan ujian klinikal yang tersedia dalam domain awam menggunakan perisian yang berkaitan seperti 'IPDfromKM' dan 'DigitizeIt' dan kemudian mengira semula anggaran masa hidup min terhad (RMST) serta anggaran parametrik dengan mengandaikan taburan eksponen Weibull dan Gompertz yang akan digunakan setiap satu dalam NMA berasingan. Kaedah dibandingkan dan kelebihan setiap pendekatan diserlahkan. Untuk tujuan ilustrasi, keputusan berasaskan Kaplan Meier daripada empat percubaan Kanser Sel Renal Lanjutan, digunakan untuk analisis. Keputusan Nisbah Bahaya Berkadaran Cox telah dibandingkan dengan Nisbah Bahaya berasaskan taburan parametrik yang diperoleh dalam analisis kami. Analisis berasaskan anggaran RMST memberikan pandangan tambahan. Berdasarkan NMA, landskap rawatan perbandingan disediakan dari segi kedudukan rawatan terapi gabungan yang disertakan.

Kata kunci: Analisis kemandirian; anggaran nisbah bahaya; kaedah Bayesian dalam meta-analisis; meta-analisis rangkaian; purata masa kemandirian terhad

 

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*Corresponding author; email: revathiravilisetty@gmail.com

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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