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The article by Burgess, Cornelius, Love, Graham, Richards, and Ramirez (2005) reports the results of a study which investigated the prevalence of and the potential factors of risk for anxiety and depression among females who have the early breast cancer during the first five years after the disease had been diagnosed. 222 women who had early breast cancer were selected; out of these, 170 participants provided full responses to the interviewers either for five years after being diagnosed or after a recurrence had been detected. The participants were recruited for nearly eight weeks after the diagnosis and then were interviewed every 1.5 years, which lasted for up to five years. The respondents were classified into two groups: those who suffered from anxiety, depression, or both, on the one hand, and those who did not suffer from any of these, on the other (Burgess et al., 2005).
Several statistical analyses were run; in particular, descriptive statistics were calculated for demographical variables; logistic regression was carried out to examine the risk factors and their impact at the period which closely followed the diagnosis; and Coxs proportional hazards model was employed to assess the risk factors for anxiety and depression occurrence over a more extended period (Burgess et al., 2005). Figure 1 below displays the logistic regression results, as provided by the authors (Burgess et al., 2005).
The logistic regression analysis was utilized because it permits for assessing the probability that a case falls into one of the two categories of a dichotomous dependent variable using several independent variables as predictors (Warner, 2013). As a part of multiple regression analysis, the odds ratio is often calculated (Field, 2013). The odds ratio is useful, for it allows for comparing the probability (expressed as odds) that an event will occur in the first group to that in the second one (Field, 2013).
Figure 1 provides the results of both logistic regression and survival analysis. The results of the regression are displayed as odds ratios. It can be seen that during the time which is close to the diagnosis (<1 to 4 months), only past psychological treatment significantly predicted anxiety or depression: OR = 1.90; 95% CI for OR: (0.99 to 3.66); p =.05 (Burgess et al., 2005, p. 3). This means that the odds of suffering from anxiety or depression was, on the average, 1.90 times greater (95% CI: from 0.99 to 3.66) if a patient had received past psychological treatment than if she had not received it (Field, 2013).
Also, the lack of intimate confiding relationship was found to be moderately significantly (George & Mallery, 2016) associated with the occurrence of depression and anxiety: OR = 1.67; 95% CI for OR: (0.91 to 3.01); p =.1 (Burgess et al., 2005, p. 3). In other words, the odds of suffering from depression and anxiety if there had been a lack of intimate confiding relationship was, on the average, 1.67 times greater than if there had been no such lack.
However, such factors as the young age, severe events in life, and severe difficulties were found not to significantly affect the odds ratio of depression and anxiety occurrence soon after the diagnosis; p-values were equal to.18,.95, and.95 for these factors, respectively.
The article by Burgess et al. (2005) is related to the field of interest of the author of this paper because it investigates the factors affecting the occurrence of anxiety and depression in individuals. The author might utilize this articles results to help better future customers predict the risks of depression and anxiety, primarily if these patients are found to suffer from such a severe health condition as early breast cancer.
References
Burgess, C., Cornelius, V., Love, S., Graham, J., Richards, M., & Ramirez, A. (2005). Depression and anxiety in women with early breast cancer: Five year observational cohort study. The British Medical Journal, 330, 1-4. Web.
Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). Thousand Oaks, CA: SAGE Publications.
George, D., & Mallery, P. (2016). IBM SPSS Statistics 23 step by step: A simple guide and reference (14th ed.). New York, NY: Routledge.
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: SAGE Publications.
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