Category Archives: computational biology

Complexity versus simplicity in relating tumour size change to survival in oncology drug development

Every pharmaceutical company would like to be able to predict the survival benefit of a new cancer treatment compared to an existing treatment as early as possible in drug development.  This quest for the “holy grail” has led to tremendous efforts from the statistical modelling community to develop models that link variables related to change in disease state to survival times.  The main variable of interest, for obvious reasons, is tumour size measured via imaging.  The marker derived from imaging is called the Sum of Longest Diameters (SLD).  It represents the sum of longest diameters of target lesions, which end up being large lesions that are easy to measure.  Therefore the marker is not representative of the entire tumour burden within the patient.  However, a change within the first X weeks of treatment in SLD is used within drug development to make decisions regarding whether to continue the development of a drug or not.  Therefore, changes in SLD have been the interest of most, if not all, statistical models of survival.

There are two articles that currently analyse the relationship between changes in SLD and survival in quite different ways across multiple studies in non-small cell lung cancer.

The first approach (http://www.ncbi.nlm.nih.gov/pubmed/19440187) by the Pharmacometrics (pharmaco-statistical modelling) group within the FDA involved quite a complex approach.  They used a combination of semi-parametric and parametric survival modelling techniques together with a mixed modelling approach to develop their final survival model.  The final model was able to fit to all past data but the authors had to generate different parameter sets for different sub-groups.  The amount of technical ability required to generate these results is clearly out of the realms of most scientists and requires specialist knowledge.  This approach can quite easily be defined as being complex.

The second approach (http://www.ncbi.nlm.nih.gov/pubmed/25667291) by the Biostatistics group within the FDA involved a simple plotting approach!  In the article the authors categorise on-treatment changes in SLD using a popular clinical approach to create drug response groups.  They then assess whether the ratio of drug response between the arms of clinical studies related to the final outcome of the study.  The outcomes of interest were time to disease progression and survival.  The approach actually worked quite well!  A strong relationship was found between ratio of drug response and the differences in disease progression.  Although not as strong, the relationship to survival was also quite promising.  This approach simply involved plotting data and can be clearly done by most if not all scientists once the definitions of variables are understood.

The two approaches are clearly very different when it comes to complexity: one involved plotting while the other required degree-level statistical knowledge!  It could also be argued that the results of the plotting approach are far more useful for drug development than the statistical modelling approach as it clearly answers the question of interest.  These studies show how sometimes thinking about how to answer the question through visualisation and also taking simple approaches can be incredibly powerful.