Week 6 Discussion
Select a practice-change problem and, from the literature, an intervention to impact
outcomes. Imagine you are attempting to determine if the intervention is more effective
than current practice. Explain the various types of non-parametric statistical tests that
might be used to analyze the data collected during the implementation of the intervention.
Provide a rationale for the use of non-parametric tests for this data set.
Liver cancer is the sixth most common type of cancer and the third leading cause of death from
cancer (Singh et al., 2020). According to Singh et al. (2020), in hepatocellular carcinoma (HCC),
dysplastic macro nodules evolve early and develop into cancer. Therefore, screening should
enable us to find lesions early. The research study by Zheng et al., 2018 discussed the lab tests to
detect the serum lncRNA urothelial carcinoma associated 1 (UCA1), which is effective in
identifying patients with HCC, aids in the diagnosis, and helps with clinical practice for patients
with early-stage HCC along with imaging studies. Various nonparametric statistical tests can be
used to analyze the data when parametric test data distribution assumptions still need to be met
(Schober & Vetter, 2020). For example, the Mann-Whitney U test compares two independent
groups nonparametrically. The Kruskal–Wallis test can replace one-way analysis of variance for
groups with more than two (ANOVA). The Wilcoxon signed rank test compares two paired (not
independent) groups or two repeated tests done on the same person, assuming that differences
between the groups are the same. The Friedman test compares more than two paired groups
nonparametrically. Nonparametric correlation analysis uses Spearman rank correlation (Schober
& Vetter, 2020).
The sample size is an essential assumption in selecting the appropriate statistical method
(Schober & Vetter, 2020). The applicable parametric test can be used if a sample size is
reasonably large. However, if the sample size is too small, it is possible that you will not be able
to validate the distribution of the data. Thus, the application of nonparametric tests is the only
suitable option (Schober & Vetter, 2020). The Mann-Whitney U-test was used to compare the
data between groups in Zheng et al. (2018) study. Categorical data were analyzed using the chi-
square test. Receiver-operating characteristic (ROC) curves were used to determine how good
serum UCA1 is at diagnosing HCC. Overall survival was compared by the Kaplan–Meier
method. Univariate and multivariate Cox regression analyses were performed to examine the
relationships between patient survival and prognostic variables.
Schober, P., & Vetter, T. R. (2020). Nonparametric statistical methods in
medical research. Anesthesia & Analgesia, 131(6), 1862-1863.
https://doi.org/10.1213/ane.0000000000005101
Singh, G., Yoshida, E. M., Rathi, S., Marquez, V., Kim, P., Erb, S. R., & Salh, B. S.
(2020). Biomarkers for hepatocellular cancer. World Journal of Hepatology, 12(9), 558-
573. https://doi.org/10.4254/wjh.v12.i9.558
Zheng, Z., Pang, C., Yang, Y., Duan, Q., Zhang, J., & Liu, W. (2018). Serum long
noncoding RNA urothelial carcinoma-associated 1: A novel biomarker for diagnosis and
prognosis of hepatocellular carcinoma. Journal of International Medical Research, 46(1),
348–356. https://doi.org/10.1177/0300060517726441
Select a practice-change problem and, from the literature, an intervention to impact
outcomes. Imagine you are attempting to determine if the intervention is more effective
than current practice. Explain the various types of non-parametric statistical tests that
might be used to analyze the data collected during the implementation of the intervention.
Provide a rationale for the use of non-parametric tests for this data set.
Liver cancer is the sixth most common type of cancer and the third leading cause of death from
cancer (Singh et al., 2020). According to Singh et al. (2020), in hepatocellular carcinoma (HCC),
dysplastic macro nodules evolve early and develop into cancer. Therefore, screening should
enable us to find lesions early. The research study by Zheng et al., 2018 discussed the lab tests to
detect the serum lncRNA urothelial carcinoma associated 1 (UCA1), which is effective in
identifying patients with HCC, aids in the diagnosis, and helps with clinical practice for patients
with early-stage HCC along with imaging studies. Various nonparametric statistical tests can be
used to analyze the data when parametric test data distribution assumptions still need to be met
(Schober & Vetter, 2020). For example, the Mann-Whitney U test compares two independent
groups nonparametrically. The Kruskal–Wallis test can replace one-way analysis of variance for
groups with more than two (ANOVA). The Wilcoxon signed rank test compares two paired (not
independent) groups or two repeated tests done on the same person, assuming that differences
between the groups are the same. The Friedman test compares more than two paired groups
nonparametrically. Nonparametric correlation analysis uses Spearman rank correlation (Schober
& Vetter, 2020).
The sample size is an essential assumption in selecting the appropriate statistical method
(Schober & Vetter, 2020). The applicable parametric test can be used if a sample size is
reasonably large. However, if the sample size is too small, it is possible that you will not be able
to validate the distribution of the data. Thus, the application of nonparametric tests is the only
suitable option (Schober & Vetter, 2020). The Mann-Whitney U-test was used to compare the
data between groups in Zheng et al. (2018) study. Categorical data were analyzed using the chi-
square test. Receiver-operating characteristic (ROC) curves were used to determine how good
serum UCA1 is at diagnosing HCC. Overall survival was compared by the Kaplan–Meier
method. Univariate and multivariate Cox regression analyses were performed to examine the
relationships between patient survival and prognostic variables.
Schober, P., & Vetter, T. R. (2020). Nonparametric statistical methods in
medical research. Anesthesia & Analgesia, 131(6), 1862-1863.
https://doi.org/10.1213/ane.0000000000005101
Singh, G., Yoshida, E. M., Rathi, S., Marquez, V., Kim, P., Erb, S. R., & Salh, B. S.
(2020). Biomarkers for hepatocellular cancer. World Journal of Hepatology, 12(9), 558-
573. https://doi.org/10.4254/wjh.v12.i9.558
Zheng, Z., Pang, C., Yang, Y., Duan, Q., Zhang, J., & Liu, W. (2018). Serum long
noncoding RNA urothelial carcinoma-associated 1: A novel biomarker for diagnosis and
prognosis of hepatocellular carcinoma. Journal of International Medical Research, 46(1),
348–356. https://doi.org/10.1177/0300060517726441
