The main hypothesis tested is to show the existence or absence of any differences between two histological features to the patients' clinical treatment and outcome: Invasive lobular carcinoma (ILC) and Invasive ductal carcinoma (IDC). The aim was first to establish risk factors and use them as variables for patient survival through the time of the test. This approach has the potential of producing quantifiable risk factors that can help clinicians to design different clinical management strategies based on easy and fast diagnostic procedures. In this article, we will try to elucidate the merit of this paper to achieving the aims it sets.
The Body: In order to achieve some statistical significance the authors analysed a big study population of 49309 patients, and split the population into two groups depending on the histological features of the breast tumour so that 4140 fell under the Invasive lobular carcinoma (ILC) requirements, and the other 45, 169 under the Invasive ductal carcinoma (IDC). ...
Have these considerations taken in account; it would have established further subgroups, exposing the whole study to similar drawbacks of earlier studies where the size of the groups was limited. The factors analysed to test the hypothesis were selected from the literature, both laboratory and clinic: Estrogen receptor (ER) levels, progesterone receptor (PgR) levels, DNA ploidy, S-phase fraction, HER-2 status, Epidermal growth factor receptor (EGFR) levels and p53 status. Other factors, if chosen, may have affected the outcome of the results such as the expression of cyclin D1, a known regulator of cell cycle, this regulator is a known prognostic factor for breast cancer.
Having examined the main statistical samples and variables used in this study, we now have to concentrate on the statistical methods used in the study to achieve its conclusions. To describe, the populations used, the authors used frequencies and medians; the comparison between the characteristics for correlation was achieved through contingency analysis, square and Fisher's exact tests. Contingency tables are by far the best analytical tool suited for the comparison of two variables, in this article the authors compare as in table one between ILC and IDC and how dependent are they on the characteristics they tested. Contingency coefficient usage has the added the added benefit of readable interpretation over a simple square as it shows independence at value 0, and show increasing dependence as the values approach 1. The square is most appropriate for the analysis of large population as it is the case in this study; however, Fisher's exact test is a robust tool for analysing the dependence of two variables independent of the population size as it uses exact probabilities