Article Critique Discussion: Week 2
de Gouveia Belinelo, P., Nielsen, A., Goddard, B., Platt, L., Da Silva Sena, C. R., Robinson, P. D., Whitehead, B., Hilton, J., Gulliver, T., Roddick, L., Pearce, K., Murphy, V. E., Gibson, P. G., Collison, A., & Mattes, J. (2020). Clinical and lung function outcomes in a cohort of children with severe asthma. BMC Pulmonary Medicine, 20(1), 66. https://doi.org/10.1186/s12890-020-1101-6
Summary of Study
de Gouveia Belinelo et al. (2020) conducted an age matched case control study to examine the relationship between asthma control and lung function outcomes over time for a prospective birth cohort Growing into Asthma (GIA). The investigators delineated severe childhood asthma into three groups ” Severe Asthma Clinic children (SAC)”, “GIA children with asthma” and “GIA children without asthma.” The researchers also evaluated the interaction of pulmonary function test effects for lung clearance index (LCI), forced expiratory volume (FEV) and uncontrolled severe asthma outcomes over time (at first visit and follow-up of SAC children). The purpose of this critique is to review the research study’s use of two-way ANOVA test, the methodology, the results, and the implications of the findings and the limitations of the Study
Research Question
RQ1: What is the effect between asthma control methods and outcomes while managing children with severe asthma in the nurse-led severe asthma clinic (SAC)?
Variables
Independent Variables (IV):
- Asthma Status-:
- Severe Asthma Clinic children (SAC)”,
- “GIA children with asthma”
- “GIA children without asthma.”
- SAC follow-up visit:
- First Visit first appointment/home visit in the SAC
- The follow-up assessment performed after an average of 26 months from the first visit
- Intervention:
- Intervention including biological treatment
- Intervention excluding biological treatment
Dependent Variables (DV):
- Clinical outcomes and Lung function:
- Forced expiratory volume (FEV1)
- Lung clearance index (LCI)
- Forced vital capacity (FVC)
Confounder Variables (CV):
Age, Height, Weight, BMI, ACT Asthma Control Test (ACT), Sex
Why was two-way ANOVA Used
The analysis of variance (ANOVA) is a statistical technique ANOVA, similar to the t-test, that compares the differences between the means from independent groups (Laerd, n.d). The two-way ANOVA procedures are associated with the comparison means or estimation of variances for a continuous outcome variable and two or more nominal variables (Laerd, n.d); Warner, 2013). de Gouveia Belinelo et al. created two-way ANOVA statistical models to compare means of categorical variables groups for (asthma status, pre-post visits and interventions) with clinical/ lung function -continuous outcome variable (Laerd, n.d; Warner, 2013). Variability within scores is explained using the models R Square and the global statistic of fitness using the ANOVA’s F statistical test for significance (Warner, 2013).
What was the main result(s)?
De Gouveia Belinelo et al. (2020) reported findings related to the research question and hypothesis using a two-way factor ANOVA test to compare findings for children with severe asthma (SA) had a lower FEV1, and FVC predicted before and after bronchodilator inhalation, and a higher mean Lung Clearance Index (LCI). Nearly 80% of children with SA had an abnormal LCI, and 48% had a reduced FEV1% at the first SAC visit. Asthma control and FEV1% predicted significantly improved at a follow-up visit, while LCI remained abnormal in the majority of children (83%).
What was the interpretation?
Gradually, children with severe asthma displayed improved clinical outcomes and lung function while lung ventilation in similarities persevered.
Critique of Discussion of Results and limitation(s) of the study.
De Gouveia Belinelo et al. (2020) met the variable measurement level and structure requirement; however, failed to provide information on the evidence of having performed the assumptions for model fitness. While De Gouveia Belinelo et all. (2020) provided possible study limitations (i.e. choice of design, sampling methodology,), they failed to provide a stated hypothesis and the interaction terms for the two-way ANOVA. Furthermore, the study had a very small homogenous sample frame. The investigators also examined the need for generalizability but failed to go further with the assessment.
While De Gouveia Belinelo et all. (2020) provided investigators provided possible study limitations (i.e. choice of design, sampling methodology and sample size), they failed to provide a stated hypothesis, and the interaction terms for the two-way ANOVA. Also the cell sizes for the gender groups were too small for Levene’s test of Homogeneity to be meaningful or for interpretation (Warner,2013). Furthermore, the study had a very small homogenous sample frame. The investigators also examined the need for generalizability but failed to go further with the assessment.
References
Laerd, (n.d) Two-way ANOVA in SPSS Statistics. Retrieved from com/spss-tutorials/two-way-anova-using-spss-statistics.php”>https://statistics.laerd.com/spss-tutorials/two-way-anova-using-spss-statistics.php
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: SAGE Publications.Article Critique Discussion: Week 2
de Gouveia Belinelo, P., Nielsen, A., Goddard, B., Platt, L., Da Silva Sena, C. R., Robinson, P. D., Whitehead, B., Hilton, J., Gulliver, T., Roddick, L., Pearce, K., Murphy, V. E., Gibson, P. G., Collison, A., & Mattes, J. (2020). Clinical and lung function outcomes in a cohort of children with severe asthma. BMC Pulmonary Medicine, 20(1), 66. https://doi.org/10.1186/s12890-020-1101-6
Summary of Study
de Gouveia Belinelo et al. (2020) conducted an age matched case control study to examine the relationship between asthma control and lung function outcomes over time for a prospective birth cohort Growing into Asthma (GIA). The investigators delineated severe childhood asthma into three groups ” Severe Asthma Clinic children (SAC)”, “GIA children with asthma” and “GIA children without asthma.” The researchers also evaluated the interaction of pulmonary function test effects for lung clearance index (LCI), forced expiratory volume (FEV) and uncontrolled severe asthma outcomes over time (at first visit and follow-up of SAC children). The purpose of this critique is to review the research study’s use of two-way ANOVA test, the methodology, the results, and the implications of the findings and the limitations of the Study
Research Question
RQ1: What is the effect between asthma control methods and outcomes while managing children with severe asthma in the nurse-led severe asthma clinic (SAC)?
Variables
Independent Variables (IV):
- Asthma Status-:
- Severe Asthma Clinic children (SAC)”,
- “GIA children with asthma”
- “GIA children without asthma.”
- SAC follow-up visit:
- First Visit first appointment/home visit in the SAC
- The follow-up assessment performed after an average of 26 months from the first visit
- Intervention:
- Intervention including biological treatment
- Intervention excluding biological treatment
Dependent Variables (DV):
- Clinical outcomes and Lung function:
- Forced expiratory volume (FEV1)
- Lung clearance index (LCI)
- Forced vital capacity (FVC)
Confounder Variables (CV):
Age, Height, Weight, BMI, ACT Asthma Control Test (ACT), Sex
Why was two-way ANOVA Used
The analysis of variance (ANOVA) is a statistical technique ANOVA, similar to the t-test, that compares the differences between the means from independent groups (Laerd, n.d). The two-way ANOVA procedures are associated with the comparison means or estimation of variances for a continuous outcome variable and two or more nominal variables (Laerd, n.d); Warner, 2013). de Gouveia Belinelo et al. created two-way ANOVA statistical models to compare means of categorical variables groups for (asthma status, pre-post visits and interventions) with clinical/ lung function -continuous outcome variable (Laerd, n.d; Warner, 2013). Variability within scores is explained using the models R Square and the global statistic of fitness using the ANOVA’s F statistical test for significance (Warner, 2013).
What was the main result(s)?
De Gouveia Belinelo et al. (2020) reported findings related to the research question and hypothesis using a two-way factor ANOVA test to compare findings for children with severe asthma (SA) had a lower FEV1, and FVC predicted before and after bronchodilator inhalation, and a higher mean Lung Clearance Index (LCI). Nearly 80% of children with SA had an abnormal LCI, and 48% had a reduced FEV1% at the first SAC visit. Asthma control and FEV1% predicted significantly improved at a follow-up visit, while LCI remained abnormal in the majority of children (83%).
What was the interpretation?
Gradually, children with severe asthma displayed improved clinical outcomes and lung function while lung ventilation in similarities persevered.
Critique of Discussion of Results and limitation(s) of the study.
De Gouveia Belinelo et al. (2020) met the variable measurement level and structure requirement; however, failed to provide information on the evidence of having performed the assumptions for model fitness. While De Gouveia Belinelo et all. (2020) provided possible study limitations (i.e. choice of design, sampling methodology,), they failed to provide a stated hypothesis and the interaction terms for the two-way ANOVA. Furthermore, the study had a very small homogenous sample frame. The investigators also examined the need for generalizability but failed to go further with the assessment.
While De Gouveia Belinelo et all. (2020) provided investigators provided possible study limitations (i.e. choice of design, sampling methodology and sample size), they failed to provide a stated hypothesis, and the interaction terms for the two-way ANOVA. Also the cell sizes for the gender groups were too small for Levene’s test of Homogeneity to be meaningful or for interpretation (Warner,2013). Furthermore, the study had a very small homogenous sample frame. The investigators also examined the need for generalizability but failed to go further with the assessment.
References
Laerd, (n.d) Two-way ANOVA in SPSS Statistics. Retrieved from https://statistics.laerd.com/spss-tutorials/two-way-anova-using-spss-statistics.php
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: SAGE Publications.
2 days ago
RE: Discussion 1 – Week 3
Discussion: ANOVA
Article Under Review:
Pettorruso, M., d’Andrea, G., Martinotti, G., Cocciolillo, F., Miuli, A., Di Muzio, I.,
Collevecchio, R., Verrastro, V., De-Giorgio, F., Janiri, L., di Giannantonio, M., Di Giuda, D., & Camardese, G. (2020). Hopelessness, dissociative symptoms, and suicide risk in major depressive disorder: clinical and biological Correlates. Brain sciences, 10(8), 519. https://doi.org/10.3390/brainsci10080519.
Definition & Assumptions of Two-Way ANOVA
In discussing the article above for a two-way ANOVA, I state the definition and assumptions of a two-way ANOVA.
A two-way ANOVA compares the mean differences between groups that have been split on two independent variables (called factors) with the purpose of understanding if there is an interaction between the two independent variables on the dependent variable (Laerd, 2018).
It is used to know how two independent variables, in combination, affect a dependent variable. (Bevans, 2020). Leard (2018) stated the following assumptions for a two-way ANOVA:
Assumption #1: Dependent variable should be measured at the continuous level (i.e., interval or ratio variables).
Assumption #2: The two independent variables should consist of two or more categorical, independent groups.
Assumption #3: There should be independence of observations, which means that there is no relationship between the observations in each group or between the groups themselves. Assumption #4: There should be no significant outliers.
Assumption #5: The dependent variable should be approximately normally distributed for each combination of the groups of the two independent variables.
Assumption #6: Homogeneity of variances for each combination of the groups of the two independent variables.
Variable Types:
Though not explicitly stated, it appears to me from the article that the researchers used the following variables:
Independent variables: The two independent (categorical) variables are high suicide risk and low suicide risk. I assume that Assumption 2 was violated since their independent variables do not consist of two or more categorical independent groups.
Dependent variable: The dependent variable (continuous) is the Striatal 123I-FP-CIT Binding Ratios for DAT availability. ANOVA test analysis was done at six different stages – Right Putamen, Left Putamen, Right Caudate, Left Caudate, Right Striatum, and Left Striatum.
Confounding variables: sex & age
Research Question: The research question was not categorically stated. However, I assume that The research question related to the article’s aims could have been displayed: Is there an association between suicide risk and DAT availability?
Main Result: Among other results, the primary outcome of this study, as it relates to ANOVA, is that the high suicide risk group showed lower DAT availability (in terms of mean difference) in Left Putamen (p=0.021<0.05) and Right Caudate (p=0.041<0.05) compared to the low suicide risk group (see Table 3).
Interpretation: The result shows that patients with major depressive disorder (MDD) with high suicidality showed lower DAT levels in the right caudate and left putamen. This finding of low dopamine transporter (DAT) levels in subcortical areas in subjects with severe hopelessness and high suicidality suggests the possible use of dopaminergic system modulators as strategies to contain suicidal risk in these patients.
Limitation of Study: The researchers stated four limitations. According to the researchers, the small sample size could impact their speculation. Also, the instrument of measuring suicidality has not been validated; of course, negativity affects the standard of the outcome. Furthermore, many confounding variables were also not taken into consideration.
References:
Bevans, R. (2020). An introduction to the two-way ANOVA. Retrieved from:
https://www.scribbr.com/statistics/two-way-anova/
Lard Statistics (2018). Multiple regression analysis using SPSS statistics.
Retrieved from https://statistics.laerd.com/spss-tutorials/multiple-regression-using-spss-statistics.php.
0 comments