This is a good start for your evaluation plan. However, you did not identify a realistic statistical analysis plan; EBP is not a statistical analysis method. Considering the type of data you are gathering (CAUTI occurrences), then what will be an appropriate statistical analysis method to evaluate the data obtained in accordance with your course readings?
Evaluation of data regarding CAUTI occurrences requires an appropriate statistical analysis plan that aligns with the nature of the data and the research objectives. The aim of this evaluation is to gain insight into the effectiveness of an intervention or measure in reducing CAUTI incidents. This response will outline a realistic statistical analysis plan that takes into account the type of data being collected.
Given that CAUTI occurrences are typically measured as counts and involve discrete events, it would be appropriate to employ statistical methods designed for count data analysis. Poisson regression is a commonly used method for analyzing count data, and it allows for the modeling of the relationship between the occurrence of an event (in this case, CAUTI) and various explanatory variables.
Poisson regression is based on the assumption that the rate of occurrence of the event follows a Poisson distribution. The dependent variable in this case would be the count of CAUTI incidents, and the independent variables would include the intervention or measure being evaluated, as well as any relevant covariates and confounding factors.
To implement the Poisson regression analysis, the data would need to be organized in a suitable format. Each observation would consist of a count of CAUTI occurrences, along with the corresponding values of the independent variables. The data would likely be collected over a period of time, and the observations would be structured accordingly.
Before conducting the Poisson regression analysis, it would be important to assess the goodness-of-fit of the Poisson regression model to the data. This can be done through the examination of the residual deviance and the calculation of the Akaike information criterion (AIC). These measures provide an indication of how well the model fits the data, with lower values indicating better fit.
Another consideration in the statistical analysis plan would be the identification and inclusion of potential confounding variables. Confounders are variables that are related to both the intervention or measure being evaluated and the outcome (CAUTI occurrences) and could potentially distort the relationship between the two. Controlling for these confounders in the analysis is necessary to obtain accurate estimates of the intervention’s effect.
In addition to the Poisson regression analysis, it may also be relevant to consider other statistical approaches depending on the specific research question and data characteristics. For example, if the data includes repeated measurements on the same individuals or facilities, a mixed-effects model or generalized estimating equations (GEEs) could be appropriate to account for the correlated nature of the data.
Furthermore, if the research question involves examining the impact of the intervention at different time points or in different subgroups, a time series analysis or subgroup analysis could be considered, respectively. These additional analyses would enhance the depth of the evaluation and provide a more comprehensive understanding of the intervention’s effects.
In conclusion, an appropriate statistical analysis plan for evaluating data on CAUTI occurrences would involve the use of Poisson regression to model the relationship between the count of CAUTI incidents and relevant independent variables. Additionally, considerations such as checking the fit of the model, controlling for confounding variables, and exploring alternative statistical approaches based on the research question and data characteristics are crucial for a comprehensive evaluation. By employing these statistical methods, researchers can obtain meaningful insights into the effectiveness of interventions or measures in reducing CAUTI occurrences.