Descriptive Statistics Overview

Descriptive Statistics Overview

Descriptive Statistics Overview 150 150 Peter

Descriptive Statistics Overview

Purpose:

The purpose of this assignment is to review the statistics presented in the articles you reviewed in relation to your clinical topic. You will provide a brief overview of statistics in the studies.

Directions:

Define the clinical key question of interest.
Identify the studies of the database search that represent the highest levels of evidence found Identify the statistics discussed in the study as it relates to confidence intervals or statistical description.
Discuss the statistical results of the studies identified.
Write a 3- to 5-page paper reviewing the evidence as it relates to confidence.
Introduction and Conclusion
Follow APA 7th edition format.

Sample Paper

Scientific and Analytic

The clinical question of among patients admitted to a mental health care facility (p), what is the effectiveness of cognitive-behavioral therapy(I) compared to not providing cognitive behavioural therapy(C) for the general condition of patients after (O)6 months (T) is important in healthcare settings, especially in mental health care facilities. This is because non-pharmacological approaches to the treatment of mental health have been shown to have less severe side effects and better outcomes compared to pharmacological treatments that involve antipsychotic medication administration (Chiang et al., 2017).

Studies of the Database Search that Represent the Highest Levels of Evidence Found

The studies of the databases that were considered that represent the highest levels of evidence found include Cuijpers et al. (2019) meta-analysis of randomized controlled trials located within Embase database Level I and Fordham et al. (2021) meta-analysis of randomized controlled trials located within Pubmed database Level I.

Identification of Statistics Described in the Studies

Cuijpers et al. (2019) Study relies on the random effects pooling model of random analysis to present pairwise meta-analysis for all direct comparisons in the study. Cuijpers et al. (2019) rely on the accumulation of I2 statistics to assess the homogeneity of the effect of sizes which is an Indicator of heterogeneity in different percentages as well as the T2. Cuijpers et al. (2019) rely on the non-central χ2–based approach within the heterogi module for stata to calculate 95% confidence intervals around the I2 statistic. Cuijpers et al. (2019) also relies on the Egger test returning the publication bias of the various sources used and testing the statistical significance of such bias. Cuijpers et al. (2019) also rely on relative risks and comparative standardized mean differences to effectively report at least 95% prediction intervals. The prediction intervals, therefore, indicate the range of the true effect size of 95%, in which all the populations included in the considered studies will fall. Finally, Cuijpers et al. (2019) relying on the cumulative ranking curve to rank the treatment formats, which is mainly based on relying on an estimated random-effects model.

Fordham et al. (2021) rely on the DerSimonian and Laird method for meta-analysis, which relies on STATA v.13.Fordham et al. (2021) rely on the random-effects model to have random effect meta-analysis and fixed-effect meta-analysis. Fordham et al. (2021) statistical analysis with Heterogeneity of I2. In the study, the heterogeneity measure was, therefore, equal to 75%, which acted as the cutoff for acceptable heterogeneity within the meta-analysis. In the meta-analysis, the other author predicted various prediction intervals for the primary analysis. In the study, Fordham et al. (2021) also relied on the standardized mean difference, which was the most commonly applied outcome measure, so as to produce specific and standardized estimates related to the main differences. The authors therefore identified as standard deviations that relied on the risk of biased trial within a higher-quality review.

Statistical Results of the Studies Identified

Cuijpers et al. (2019) study highlight that group individual guided self-help and even telephone CBT were statistically significant and would be even more effective done and guided self-help CBT which provided a standardized mean difference of between 0.34 to 0.59. On the other hand, Cuijpers et al. (2019) did not find any statistically significant difference between guided self-help individual group and telephone CBT except in incidences where there was significant superiority of a guided self-help CBT compared to group CBT.

On the other hand, in Fordham et al. (2021) study and through their statistical analysis, the authors found that heterogeneity across different conditions was low, which highlighted an average of 32%. Fordham et al. (2021) also observed an SMD of at least 0.23% and 95% confidence intervals. Fordham et al. (2021) study therefore clearly highlighted that heterogeneity was easily reflected in different studies as a result of prediction intervals which significantly indicated the overall effect as being between 0.03 and 0.50. Statistical studies clearly highlighted that there was a lot of positive effect for different conditions and a small negative effect of CBT. Considerable heterogeneity was found between estimates of cognitive-behavioral therapy effectiveness on depression outcomes for at least 14 conditions with a heterogeneity level of nearly 81%. The depression analysis included a bias of at least 0.17 and confidence intervals of between1.21 to 1.44.

Conclusion

In summary, the studies of the databases that were considered that represent the highest levels of evidence found include Cuijpers et al. (2019) meta-analysis of randomized controlled trials located within Embase database Level I and Fordham et al. (2021) meta-analysis of randomized controlled trials located within Pubmed database Level I. Cuijpers et al. (2019) study relies on the random effects pooling model of random analysis to present pairwise meta-analysis for all direct comparisons in the study. Fordham et al. (2021) rely on the DerSimonian and Laird method for meta-analysis, which relies on STATA v.13.Cuijpers et al. (2019) study highlight that group individual guided self-help and even telephone CBT were statistically significant and would be even more effective done and guided self-help CBT which provided a standardized mean difference of between 0.34 to 0.59. On the other hand, in Fordham et al. (2021) study and through their statistical analysis, the authors found that heterogeneity across different conditions was low, which highlighted an average of 32%.

References

Chiang, K. J., Tsai, J. C., Liu, D., Lin, C. H., Chiu, H. L., & Chou, K. R. (2017). Efficacy of cognitive-behavioral therapy in patients with bipolar disorder: A meta-analysis of randomized controlled trials. PLOS ONE12(5), e0176849. https://doi.org/10.1371/journal.pone.0176849

Cuijpers, P., Noma, H., Karyotaki, E., Cipriani, A., & Furukawa, T. A. (2019). Effectiveness and Acceptability of Cognitive Behavior Therapy Delivery Formats in Adults With Depression. JAMA Psychiatry76(7), 700. https://doi.org/10.1001/jamapsychiatry.2019.0268

Fordham, B., Sugavanam, T., Edwards, K., Stallard, P., Howard, R., das Nair, R., Copsey, B., Lee, H., Howick, J., Hemming, K., & Lamb, S. E. (2021). The evidence for cognitive behavioral therapy in any condition, population or context: a meta-review of systematic reviews and panoramic meta-analysis. Psychological Medicine51(1), 21–29. https://doi.org/10.1017/s0033291720005292