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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