Alternative indicators in cancer survival analysis: Estimation on cause-specific and relative survival setting using flexible regression models and pseudo-observations

Analyses of time-to-event outcomes almost infallibly rely either on the survival probability at a given time or on the hazard ratio(s) associated with some variable(s) of interest. However, these quantities may be confusing and hard to communicate to the general public. Furthermore, when cancer is the disease of interest most population-based studies focus on the net survival. Net survival is crucial for comparison purposes between populations, but it is less appropriate for planning a health policy or describing a patient’s prognosis, because it is defined in the hypothetical world. Therefore, it is essential that we use alternative survival indicators that could cover these needs, and that could be estimated using population-based data, where the cause of death is usually not available/accurate. Useful alternative indicators that could summarize the survival experience efficiently at both population and individual levels include: the Crude Probability of Death (CPr) and the number of Life years Lost (LYL) detailed by cause of death. These indicators may be expressed using the cause-specific, the subdistribution, and the excess hazard depending on the availability of the cause of death information (ie, either in the cause-specific or the relative survival setting). Their estimation could be achieved with non-parametric methods and regression models. The aim of this PhD is to add to this topic by presenting two new methods for estimating the CPr and the LYL using flexible regression models (in both settings) and the pseudoobservations approach (in the relative survival setting). These methods have the additional advantage of providing covariate effects on the quantities of interest. This thesis includes one paper summarising the alternative indicators, two scientific papers that focus on the new methods, and two R tutorials that show how the new methods may be applied to R software

Summarizing and communicating on survival data according to the audience: a tutorial on different measures illustrated with population-based cancer registry data

Aurélien Belot, Aminata Ndiaye, Miguel-Angel Luque-Fernandez, Dimitra-Kleio Kipourou, Camille Maringe, Francisco Javier Rubio, Bernard Rachet Cancer Survival Group, Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK Abstract: Survival data analysis results are usually communicated through the overall survival probability. Alternative measures provide additional insights and may help in communicating the results to a wider audience. We describe these alternative measures in two data settings, the overall survival setting and the relative survival setting, the latter corresponding to the particular competing risk setting in which the cause of death is unavailable or unreliable. In the overall survival setting, we describe the overall survival probability, the conditional survival probability and the restricted mean survival time (restricted to a prespecified time window). In the relative survival setting, we describe the net survival probability, the conditional net survival probability, the restricted mean net survival time, the crude probability of death due to each cause and the number of life years lost due to each cause over a prespecified time window. These measures describe survival data either on a probability scale or on a timescale. The clinical or population health purpose of each measure is detailed, and their advantages and drawbacks are discussed. We then illustrate their use analyzing England population-based registry data of men 15–80 years old diagnosed with colon cancer in 2001–2003, aiming to describe the deprivation disparities in survival. We believe that both the provision of a detailed example of the interpretation of each measure and the software implementation will help in generalizing their use. Keywords: survival, competing risks, relative survival setting, conditional survival, restricted mean survival time, net survival, crude probability of death, number of life years los

On the estimation of the effect of weight change on a health outcome using observational data, by utlilising the target trial emulation framework

Background/Objectives: When studying the effect of weight change between two time points on a health outcome using observational data, two main problems arise initially (i) ‘when is time zero?’ and (ii) ‘which confounders should we account for?’ From the baseline date or the 1st follow-up (when the weight change can be measured)? Different methods have been previously used in the literature that carry different sources of bias and hence produce different results. Methods: We utilised the target trial emulation framework and considered weight change as a hypothetical intervention. First, we used a simplified example from a hypothetical randomised trial where no modelling is required. Then we simulated data from an observational study where modelling is needed. We demonstrate the problems of each of these methods and suggest a strategy. Interventions: weight loss/gain vs maintenance. Results: The recommended method defines time-zero at enrolment, but adjustment for confounders (or exclusion of individuals based on levels of confounders) should be performed both at enrolment and the 1st follow-up. Conclusions: The implementation of our suggested method [adjusting for (or excluding based on) confounders measured both at baseline and the 1st follow-up] can help researchers attenuate bias by avoiding some common pitfalls. Other methods that have been widely used in the past to estimate the effect of weight change on a health outcome are more biased. However, two issues remain (i) the exposure is not well-defined as there are different ways of changing weight (however we tried to reduce this problem by excluding individuals who develop a chronic disease); and (ii) immortal time bias, which may be small if the time to first follow up is short