Dynamic prediction of longitudinal markers and time to disease progression in metastatic melanoma patients


This project aims to develop individualised dynamic event prediction models, combining simultaneously longitudinal biomarker data and time-to-event data.


Dr Serigne Lo, Professor Georgina Long

Research Location

Sydney Medical School - Generic

Program Type



In advance stage melanoma patients, some baseline clinical factors (e.g. ECOG), tumour marker (e.g. sum of the product of bi-dimensional diameters (SPOD)) and some blood serum (e.g. serum lactate dehydrogenase (LDH), Circulating tumor DNA (ctDNA)) have been proven to be independent factors associated with treatment outcomes.1-5 During treatment, physicians intuitively adjust their prediction of prognosis at each clinical visit with the change in clinical status, tumour marker and blood serum level. With the help of novel statistical methods to model outcomes, it is now possible to build individualised dynamic event prediction models, combining simultaneously longitudinal data such as tumour markers or blood serum level and time-to-event data. This type of models is called joint-models and they have received much attention in the statistics literature.6-11 Joint-models are demonstrated to reduce bias in estimates of the treatment effects and provide improvements of efficiency in the assessment of treatment effects and other prognostic factors.12 Such dynamic prediction tools are critical for physicians to be able to make better informed decisions regarding their actions and thus improve the survival chance of the patient. So far, most of the joint-models are for longitudinal outcome and right censored time-to-event data. Little has been done with interval censoring due to the fact that a corresponding partial likelihood function is not available in a closed form. Only parametric survival models which require a specification of a suitable distribution have been proposed.13,14 This project fills in this gap. The purpose of this thesis is to extend the joint-models approach to survival time that allows interval censoring times using semi-parametric survival models. The new dynamic model will be based on a 3-step approach: (1) build of a mixed-effects model to describe longitudinal marker (tumor or blood serum) progression
(2) jointly modeling the mixed-effects model with the Maximum Penalized Likelihood method that can easily handle interval-censored data developed by Ma, Heritier and Lo (2014).15 Progression-free survival and death from the start of treatment will be the two survival outcomes that will be investigated separately.
(3) and using the joint model to build subject-specific prediction risk models. The aim of this project will be also to identify the best biomarkers that can accurately predict treatment response in advance melanoma patients. The biomarker candidates will include disease volume (SPOD), LDH, ctDNA, PD-L1 expression, tumor infiltrating lymphocyte (TIL) density. A package in R (a free statistical software) associated with these developments will be proposed and will be available for public use from CRAN package repository.

Additional Information

1. Ribas A, Hamid O, Daud A et al. Association of Pembrolizumab With Tumor Response and Survival Among Patients With Advanced Melanoma. Jama 2016; 315(15), 1600-1609.
2. Diem S, Kasenda B, Spain L et al. Serum lactate dehydrogenase as an early marker for outcome in patients treated with anti-PD-1 therapy in metastatic melanoma. British journal of cancer, 2016; 114(3), 256-261.
3. Ribas A, Hodi FS, Kefford R et al. Efficacy and safety of the anti-PD-1 monoclonal antibody MK-3475 in 411 patients (pts) with melanoma (MEL). Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2014; 32:5s(suppl; abstr LBA9000^).
4. Larkin J, Chiarion-Sileni V, Gonzalez R et al. 3303 Efficacy and safety in key patient subgroups of nivolumab (NIVO) alone or combined with ipilimumab (IPI) versus IPI alone in treatment-naive patients with advanced melanoma (MEL) (CheckMate 067). European journal of cancer 2015; 51 S664-S665
5. Lee JH, Long GV, Boyd SC, Lo S, Menzies A, Tembe V, Guminski A, Jakrot V, Scolyer R, Mann G, Kefford R, Carlino MS, Rizos H Circulating tumor DNA predicts response to anti-PD-1 antibodies in metastatic melanoma (manuscript to be submitted).
6. Tsiatis AA, Davidian M. Joint modelling of longitudinal and time-to-event data: an overview. Statist Sin. 2004; 14:809-834.
7. Diggle P, Farewell D, Henderson R. Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal (with discussion). Appl Statist 2007; 56:499-550.
8. Diggle P, Sousa I, Chetwynd A. Joint modelling of repeated measurements and time-to-event outcomes: the Fourth Armitage Lecture. Statist Med 2008; 27:2981-2998.
9. Dobson A, Henderson R. Diagnostics for joint longitudinal and dropout time modelling. Biometrics 2003; 59:741-751. [PubMed: 14969451]
10. Rizopoulos, D, Verbeke, G, and Lesaffre, E. Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data. Journal of the Royal Statistical Society, Series B 2009; 71, 637-654.
11. Rizopoulos, D, Verbeke, G, and Molenberghs, G. Shared parameter models under random effects misspecification. Biometrika 2008; 95, 63-74.
12. Ibrahim JG, Chu H, and Chen LM. Basic Concepts and Methods for Joint Models of Longitudinal and Survival Data. Journal of Clinical Oncology 2010; 28:10, 2796-2816.
13. Sparling YH, Younes N, Lachi JM, Bautista OM. Parametric survival models for interval-censored data with time-dependent covariates. Biostatistics 2006; 7:599-614. [PubMed: 16597670]
14. Gueorguieva R, Rosenheck R, Lin H. Joint modelling of longitudinal outcome and interval-censored competing risk dropout in a schizophrenia clinical trial. J R Stat Soc Ser A Stat Soc. 2012 April ; 175(2): 417-433.
15. Ma J, Heritier S and Lo S. On the maximum penalized likelihood approach for proportional hazard models with right censored survival data,Computational Statistics and Data Analysis 2014; 74, 142-156.

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Metastatic melanoma, biomarker, longitudinal outcome, survival outcome, joint-models, risk prediction model.

Opportunity ID

The opportunity ID for this research opportunity is: 2135

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