Due 4/18/19 8 Pm EST Original And On Time 400 Words

Due 41819 8pm Estoriginal And On Time400 Words Not Including Titl

Reflect on the weekly introduction about survival analysis. If you recall, survival analysis examines probability of survival. This is a useful type of longitudinal analysis. Longitudinal analysis evaluates data collected over a period of time through longitudinal studies.

Survival analysis techniques allow for the inclusion of time until an event occurs as an essential variable in the relationship of risk and outcome. In public health, use of survival analysis is critical to the study of risks, interventions, treatments, and outcomes. Discussion: Survival With these thoughts in mind: Post find and discuss the following key elements of the article (INCLUDE IN PAPER) Identify variables: independent variable(s), dependent variable(s), and confounders. What was the research question? Why was Kaplan-Meier used? What was the main result(s)? What was the interpretation? What are your thoughts on the limitation(s) of the study?

Paper For Above instruction

Survival analysis is a crucial statistical method employed in many public health studies to analyze the time until an event of interest occurs, such as death, disease recurrence, or recovery. It allows researchers to understand not only whether an event occurs but also when it occurs, providing insights into risk factors and outcomes over time. An article examining survival analysis within a public health context was reviewed to discuss its methodological approach, key variables, results, and limitations.

In the selected study, the primary independent variable was the treatment intervention, which aimed to improve patient survival rates. The dependent variable was the time until the event of interest—typically death or disease recurrence—measured in days or months. Confounders included age, gender, baseline health status, and comorbid conditions, which could influence survival independently of the treatment. Controlling for confounders was essential to isolate the effect of the intervention on survival outcomes.

The research question focused on whether the new treatment intervention significantly increased survival time compared to standard care. The authors hypothesized that patients receiving the intervention would have a longer median survival time and a higher survival probability at various time points. To analyze survival probabilities over time, Kaplan-Meier survival curves were employed. The Kaplan-Meier estimator is a non-parametric statistic used extensively in survival analysis to estimate the survival function from lifetime data, especially when some data are censored—meaning that the event has not occurred for some individuals during the study period.

The main results indicated that patients receiving the new treatment demonstrated significantly higher survival probabilities over a 12-month follow-up period. The Kaplan-Meier survival curves showed a clear separation between the treatment and control groups, with the treatment group maintaining higher survival probabilities at each time point. The log-rank test, often used to compare these curves statistically, confirmed that the observed differences were significant (p

The interpretation of these results is that the new treatment effectively improved survival outcomes, supporting its potential implementation in clinical practice. However, it is essential to consider limitations. A key limitation was the relatively small sample size, which may have reduced the statistical power and generalizability of the findings. Additionally, potential confounding variables not measured or controlled for, such as socioeconomic status or genetic factors, could have influenced survival independently of the treatment. The study also had a short follow-up duration, which might not capture long-term effects or late recurrences.

In conclusion, the use of survival analysis, specifically Kaplan-Meier estimators, provides valuable insights into the timing and probability of events within public health studies. While the study presents promising results, limitations such as sample size and follow-up duration highlight the need for further research to confirm these findings and assess long-term outcomes. Understanding these methodological aspects is crucial for interpreting survival data accurately and applying findings to broader public health contexts.

References

  • Bradburn, M. J., Clark, D. B., Love, S. B., & Altman, D. G. (2003). Survival analysis Part II: Multivariable data analysis — an introduction to concepts and methods. British Journal of Cancer, 89(3), 431-436.
  • Klein, J. P., & Moeschberger, M. L. (2003). Survival analysis: Techniques for censored and truncated data. Springer Science & Business Media.
  • Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457-481.
  • Hosmer, D. W., Lemeshow, S., & May, S. (2008). Applied survival analysis: Regression modeling of time-to-event data. John Wiley & Sons.
  • Harrell, F. E. Jr. (2015). Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. Springer.
  • Collett, D. (2015). Analysis of survival data. CRC press.
  • Rosenberg, P. S., & Anderson, W. F. (2011). Confounding in contemporary epidemiology: A review of key concepts. American Journal of Epidemiology, 173(4), 410-416.
  • Altman, D. G., & De Stavola, B. L. (2006). Practical importance of measures of effect and public health impact of research. Statistics in Medicine, 25(19), 3624-3631.
  • Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187-202.
  • Schoenfeld, D. (1983). Partial residuals for proportional hazards models. Biometrika, 70(2), 239-251.

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