Prognostic factors: guidelines for investigation design and state of the art analytical methods.
The proliferation of putative prognostic factors, derived prognostic indices and computerised prediction of outcome in surgical oncology has led to some confusion over the exact methods available for deriving clinically significant prognostic factors. The realisation that the interaction between factors is often complex and non-linear has led to the development of new statistical techniques. The aim of this article is to review the currently available methods of analysis. A review of the relevant literature available from statistical, medical and computer science sources was performed. Information has been conveyed at a level aimed at producing a practical understanding of the techniques involved rather than their underlying mathematical basis. There are now clear guidelines for the investigation of putative prognostic factors (Table 1). The established role of linear statistical models and prognostic indices remains vitally important for the majority of diseases with many derived prognostic indices having been validated in a prospective fashion. However, in order to improve the delineation of prognostic factors other more complex methods of analysis are now being utilised. Furthermore, the recognition of complex dynamic non-linearity within biological systems has led to the increasing use of non-linear statistical techniques and artificial intelligence. As such it is incumbent upon the modern clinician to be able to understand the basic assumptions required for multivariate analysis and also to realise when alternative statistical techniques should be employed.
The University of Hull Academic Surgical Unit, Castle Hill Hospital, UK.
P J
PJ
Drew
D M
DM
Ilstrup
M J
MJ
Kerin
J R
JR
Monson
D001185:Artificial Intelligence; D002986:Clinical Trials as Topic / Q000379:methods*; D016000:Cluster Analysis; D016002:Discriminant Analysis; D005163:Factor Analysis, Statistical; D013502:General Surgery / Q000379:methods*; D017408:Guidelines as Topic; D016015:Logistic Models; D015233:Models, Statistical; D011379:Prognosis; D012044:Regression Analysis