Quantitative Analysis
Question
I'm struggling with the second part of this paper and whether I need to go into further detail on the findings.
This paper is worth 50% of my module grade
Part I: Analyse and report the data
1. Using the data file provided, run the following statistical tests:
a. Descriptive statistics for IQ and CAARMS symptoms
i.
Descriptive Statistics |
|||||
|
N |
Minimum |
Maximum |
Mean |
Std. Deviation |
Positive Symptoms |
82 |
0 |
18 |
8.33 |
4.579 |
Negative Symptoms |
82 |
0 |
18 |
9.28 |
4.907 |
Cognitive Symptoms |
82 |
0 |
12 |
6.04 |
3.412 |
IQ |
82 |
81 |
124 |
101.93 |
9.937 |
Valid N |
82 |
|
|
|
|
ii.
Eighty-one young people were recruited by mental health professionals to take part in the study, using semistructured interviews to assessing indication for imminent development of a first-episode psychotic disorder, and determining if an individual meets the Comprehensive Assessment of At-Risk Mental States (CAARMS) for being at ultra-high risk (UHR) for onset of a first psychotic disorder (Yung et al., 2005). The mean ‘IQ’ for all participants was 101.93 (SD = 9.94), and the mean ‘Negative Symptoms’ was 9.28 (SD = 4.91).
b. Run correlations between the variables for IQ and CAARMS symptoms.
i.
Correlations |
||||||
Variable |
Variable2 |
Statistic |
||||
Correlation |
Count |
Lower C.I. |
Upper C.I. |
Notes |
||
IQ |
Positive Symptoms |
-.731 |
82 |
-.818 |
-.611 |
|
Negative Symptoms |
-.696 |
82 |
-.793 |
-.564 |
|
|
Cognitive Symptoms |
-.717 |
82 |
-.808 |
-.592 |
|
|
Missing value handling: PAIRWISE, EXCLUDE. C.I. Level: 95.0 |
We found a strong negative correlation between IQ and CAARMS.
C Run multiple regression using the CAARMS symptoms as the predictor variables, and IQ as the outcome variable.
i.
Coefficientsa |
||||||
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
B |
Std. Error |
Beta |
||||
1 |
(Constant) |
44.294 |
4.062 |
|
10.904 |
<.001 |
IQ |
-.344 |
.040 |
-.696 |
-8.660 |
<.001 |
|
a. Dependent Variable: Negative_Symptoms |
Model Summary |
||||
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
1 |
.717a |
.514 |
.508 |
2.393 |
a. Predictors: (Constant), IQ |
ANOVAa |
||||||
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
484.660 |
1 |
484.660 |
84.614 |
<.001b |
Residual |
458.230 |
80 |
5.728 |
|
|
|
Total |
942.890 |
81 |
|
|
|
|
a. Dependent Variable: Cognitive_Symptoms |
||||||
b. Predictors: (Constant), IQ |
Model Summary |
||||
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
1 |
.731a |
.534 |
.529 |
3.144 |
a. Predictors: (Constant), IQ |
ANOVAa |
||||||
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
907.498 |
1 |
907.498 |
91.827 |
<.001b |
Residual |
790.612 |
80 |
9.883 |
|
|
|
Total |
1698.110 |
81 |
|
|
|
|
a. Dependent Variable: Positive_Symptoms |
||||||
b. Predictors: (Constant), IQ |
Coefficientsa |
||||||
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
B |
Std. Error |
Beta |
||||
1 |
(Constant) |
42.664 |
3.600 |
|
11.852 |
<.001 |
IQ |
-.337 |
.035 |
-.731 |
-9.583 |
<.001 |
|
a. Dependent Variable: Positive_Symptoms |
Model Summary |
||||
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
1 |
.717a |
.514 |
.508 |
2.393 |
a. Predictors: (Constant), IQ |
ANOVAa |
||||||
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
484.660 |
1 |
484.660 |
84.614 |
<.001b |
Residual |
458.230 |
80 |
5.728 |
|
|
|
Total |
942.890 |
81 |
|
|
|
|
a. Dependent Variable: Cognitive_Symptoms |
||||||
b. Predictors: (Constant), IQ |
Coefficientsa |
||||||
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
B |
Std. Error |
Beta |
||||
1 |
(Constant) |
31.128 |
2.741 |
|
11.358 |
<.001 |
IQ |
-.246 |
.027 |
-.717 |
-9.199 |
<.001 |
|
a. Dependent Variable: Cognitive_Symptoms |
Solution
Quantitative Analysis
If a pharmaceutical is going to be of any use to patients, it needs to have a clinically relevant therapeutic impact defined. First, it is essential to identify the most minor clinically significant difference in IQ and CAARMS that the patient considers clinically meaningful. The amount of the improvement and the patient’s appreciation for it are taken into account by IQ and CAARMS, which is patient-centered (Fowler et al., 2012). The mean clinical difference in incidence is needed to estimate how many additional patients in the treatment group achieved this difference. Different approaches, such as clinical consensus, distribution, and anchor-based procedures, can be used to calculate an individual’s MCID. In contrast to the previous methods, however, this final one truly puts the patient first by tying gains in results to how the patient sees their improvements. One commonly used tool to gauge depressed symptoms is the Beck Depression Inventory, 2nd edition (the Beck et al. 1996 version; BDI-II). On the other hand, the level of MCID on the BDI-II is still uncertain.
Report Findings
Comparisons between UHR participants and controls are summarized in the coefficients table. The patients’ demographic, clinical, and neuropsychological data are compared statistically. As predicted, the differences between IQ and CAARMS groups were considered when the effects of aging were taken into account. The controls and the UHR had no significant differences in age, gender, or handedness. However, even though UHR were significantly (FDR0.05) less likely to be. They had 2 years of minimal education than controls, were more likely to have a relative with mental illnesses, had poor general functioning, and had significantly higher rates of previous mental health issues, specifically depression (0.72), anxiety (0.56), alcohol-related problems (0.35), and self-harm (0.35). (0.37). In a survey, 61 UHR admitted using one or more illegal drugs, compared to 31 of the control group and 8 of the research subjects. According to the study, UHR acknowledged CAARMS the week before testing, whereas only seven controls reported doing the same. Statistics show that cognitive symptoms were the most rampant effects, with half of all recorded cases including it. For example, the SPQ and RSES differed significantly between UHR and controls on clinical evaluations such as the BAI and EPQ-R. The results of neuropsychological testing were inconsistent. Working memory was considerably lower in UHR than in the control sample (. The D-KEFS executive function test’s verbal fluency, color-word interference, and trail-making tasks were markedly impaired in UHR, while the tower test was unchanged. A decrease in performance on the Hinting Exercise, but not on the False-Belief Picture Sequencing or the Reading of Mind in the Eyes tasks, was found in sociological cognition assessments. The final results showed that the above condition did not affect UHR’s performance.
Interpretation of Findings
Clinically relevant therapeutic effects for patients must be defined to adequately evaluate the advantages of an intervention. If the patient reports feeling better in general, this can be considered when determining whether an effect is clinically significant (Fusar-Poli et al., 2012; Salazar de Pablo et al., 2022). The Beck Depressive Inventory-II (BDI II) is a clinical research outcome measure for depressive symptoms that are widely used. According to the findings, feeling better is associated with a minor reduction in BDI II score for people who started at a higher severity level than those who started at a lower severity level. Patients who had not responded to antidepressants also required more significant gains on the BDI II before reporting an improvement in symptoms. In addition to having an impact on clinical research and practice, these findings have far-reaching implications for the future of medicine.
Strengths and Limitations
The study had many advantages, two of which are the use of sophisticated statistical techniques and the inclusion of a large sample drawn from three high-quality randomized controlled trials. The receiver operating characteristic analysis was used to determine the ideal cut-point to maximize the sum of sensitivity and specificity. Higher-scoring individuals are often regarded to be in a more financially secure situation (Yung et al., 2005). Analyses were employed to determine the minimal amount of change necessary to make a person feel better. One may examine the relationship between model fit and BDI-II improvement on the difference and ratio scales.
This study has weaknesses based on data not meant to address these problems. CoBalT’s worldwide change question could only be answered in three ways: better, the same, or worse. These trials will likely overestimate the lowest clinically relevant change in subjective well-being. In other words, in previous assessments of MCID’s impact on quality of life using global ratings of change, a global rating scale was used that included the following categories: significantly improved, somewhat improved, about the same, slightly less improved, somewhat worse, and substantially worse (Woodberry et al., 2008). Whether or if the global rating is correct is a mystery to us. This circumstance necessitates further effort. The MCID can be better informed if the patient is asked how they are feeling, and this question has evident face validity (Mollon & Reichenberg, 2018; Mollon et al., 2018). Patient and physician global evaluations of the same disease only had a moderate agreement. Patient evaluations surpass other measures when tracking the recurrence of symptoms throughout maintenance therapy. It has been advised that randomized controlled trials (RCTs) incorporate both physician and global patient assessments because of the lack of agreement between physician and international patient assessments. Patients’ self-reported outcomes, which are less expensive than expensive physician assessments, are increasingly employed in large-scale pragmatic primary care research (Petruzzelli et al., 2018). Patients’ reported results are becoming increasingly important in primary care, but there is still a lack of data to support their use in ordinary practice. MCID on the BDI-II patient-reported scale is the primary focus of our research.
Implication of Findings
The findings have crucial significance for clinical research since they concluded that clinically relevant improvement depends on the severity of initial depression. ACCORDING TO NICE GUIDELINES, THE three BDI II point shift is not clinically significant because it does not account for baseline dependency (Wechsler, 1999; Wechsler, 2011). On the BCI II test, there is a three-point difference between groups that is nonsignificant in a sample with an average score of 60 but statistically significant in a model with an average score of 14.
Conclusion
There exists a strong negative correlation between IQ and CAARMS. As a result, a ratio scale for treatment-resistant depression is the best way to measure it. Clinical investigations and clinical practice will be affected by this. In their studies of depression, researchers frequently use the Second Edition of the Beck Depression Inventory (BDI-II). However, no one knows what the MCID, or minimum clinically significant difference, means. BDI II change is linked to the patient’s comprehensive report of success in this patient-centered approach.
References
Fowler, T., Zammit, S., Owen, M. J., & Rasmussen, F. (2012). A population-based study of shared genetic variation between Premorbid IQ and psychosis among male twin pairs and sibling pairs from Sweden. Archives of General Psychiatry, 69(5), 460-466. https://doi.org/10.1001/archgenpsychiatry.2011.1370
Fusar-Poli, P., Bonoldi, I., Yung, A. R., Borgwardt, S., Kempton, M. J., Valmaggia, L., & McGuire, P. (2012). Predicting psychosis: Meta-analysis of transition outcomes in individuals at high clinical risk. Yearbook of Psychiatry and Applied Mental Health, 69(3), 220-229. https://doi.org/10.1016/j.ypsy.2012.07.042
Mollon, J., & Reichenberg, A. (2018). Cognitive development prior to onset of psychosis. Psychological Medicine, 48(3), 392-403. https://doi.org/10.1017/s0033291717001970
Mollon, J., David, A. S., Zammit, S., Lewis, G., & Reichenberg, A. (2018). Course of cognitive development from infancy to early adulthood in the psychosis spectrum. JAMA Psychiatry, 75(3), 270. https://doi.org/10.1001/jamapsychiatry.2017.4327
Petruzzelli, M. G., Margari, L., Bosco, A., Craig, F., Palumbi, R., & Margari, F. (2018). Early onset first episode psychosis: Dimensional structure of symptoms, clinical subtypes and related neurodevelopmental markers. European Child & Adolescent Psychiatry, 27(2), 171-179. https://doi.org/10.1007/s00787-017-1026-7
Salazar de Pablo, G., Soardo, L., Cabras, A., Pereira, J., Kaur, S., Besana, F., Arienti, V., Coronelli, F., Shin, J. I., Solmi, M., Petros, N., Carvalho, A. F., McGuire, P., & Fusar-Poli, P. (2022). Clinical outcomes in individuals at clinical high risk of psychosis who do not transition to psychosis: A meta-analysis. Epidemiology and Psychiatric Sciences, 31. https://doi.org/10.1017/s2045796021000639
Wechsler, D. (1999). Wechsler abbreviated scale of intelligence. PsycTESTS Dataset. https://doi.org/10.1037/t15170-000
Wechsler, D. (2011). Wechsler abbreviated scale of intelligence--second edition. PsycTESTS Dataset. https://doi.org/10.1037/t15171-000
Woodberry, K. A., Giuliano, A. J., & Seidman, L. J. (2008). Premorbid IQ in schizophrenia: A meta-analytic review. American Journal of Psychiatry, 165(5), 579-587. https://doi.org/10.1176/appi.ajp.2008.07081242
Yung, A. R., Yung, A. R., Pan Yuen, H., Mcgorry, P. D., Phillips, L. J., Kelly, D., Dell’olio, M., Francey, S. M., Cosgrave, E. M., Killackey, E., Stanford, C., Godfrey, K., & Buckby, J. (2005). Mapping the onset of psychosis: The comprehensive assessment of at-risk mental states. Australian & New Zealand Journal of Psychiatry, 39(11-12), 964-971. https://doi.org/10.1080/j.1440-1614.2005.01714.x
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