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P. Natesan Batley et al.
Table 3. Data and analytical reporting characteristics of the 115
studies.Methodological
characteristic
Number of phases
Minimum duration
Number of
participants
Autocorrelation
magnitude
Mean difference
Methodological
characteristic
Percentage
Type of outcome
variable
Autocorrelation
How was treatment
effect determined?
Immediacy
Level change
Slope change
Effect size
Consistency
Type of analysisMinimum Average Maximum
1
4 min
1
-0.925
-8
Subtype
Count
Duration
Frequency
Rate
Qualitative
Rating Scale
Other
Included
Not Included
Immediacy
Level
Change
Slope
Change
Effect Size
None
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Multi-Level
modeling
Regression
modeling
Paired t-testMixed
effects
model
Meta-
analysis
Analysis of
variance
(ANOVA)
Bayesian
hierarchical
modeling
Logistic
regression4.31
82.79 days
12.5
NAb
NAb
35
1
3
7
2
63
4
18
97
7
1050
1
2
7
108
113
2
24
91
70
45
30
85
85
23
87
7
62
75 months
200
2a
100
Frequency
31.60%
0.90%
2.60%
6.00%
1.70%
53.80%
3.40%
16.20%
83.80%
6.00%
91.50%0.00%
0.90%
1.70%
6.00%
94.00%
98.30%
1.70%
20.50%
79.50%
61.50%
38.50%
27.40%
72.60%
6.80%
4.30%
19.70%
6.80%
0.90%
6.00%
6.00%
0.90%Table 3. continued
Methodological
characteristicCorrect
distributional
assumptions
Type of effect size
reported
Were the entire
data reported in a
way that they could
be replicated for
further analysis
(e.g., plots or raw
data)?
Did the study
account for
carryover effect
(effect of the
treatment into the
next phase after
removal of
treatment)?
Period effects
Intra-subject
correlation
Confidence/
credible interval for
effect sizeMinimum Average Maximum
Linear
modeling
Visual
Other
None
Yes
No
R6
22
8
19
39
76
9Cohen's d 8
G
A
None
Mean
difference
Yes
No1
0
1
45
52
59
56Yes
No47
68Yes
No
Yes
No
Yes
No47
68
17
98
45
70Sensitivity analysis Yes 14
No 101
Subgroup analysis Yes 13
No 102
aThis is not statistically possible.
bMeans could not be computed due to missing
providing ranges of the statistic.6.00%
19.70%
6.80%
16.20%
34.20%
65.80%
7.70%
7.70%
0.90%
0.00%
0.90%
38.50%
44.40%
52.10%
47.90%
40.20%
59.80%
40.20%
59.80%
15.40%
84.60%
39.30%
60.70%
12.80%
87.20%
11.10%
88.90%information or articles
comparable effect size estimate or report the complete dataset
which is common practice in SCEDs using a data plot. Only one
study (0.9%) included autocorrelation and corrected for small
sample size in their computations by reporting a design-
comparable effect size, i.e., Hedges' g [10, 11]. 47.9% of the
studies did not report raw data to be considered for future meta-
analysis. Although several studies included more than one
participant, only 15.4% computed and reported intraclass correla-
tion. Intraclass correlation is necessary to be computed because it
is the correlation among the scores within the individuals or the
ratio of between cluster variance (i.e., the variability between
people) to the total variance. That way we know how much of the
variance in the outcome variable is due to differences betweenTranslational Psychiatry (2023)13:263
1
1
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Batley, Prathiba Natesan; McClure, Erica B.; Brewer, Brandy; Contractor, Ateka A.; Batley, Nicholas John; Hedges, Larry Vernon et al. Evidence and reporting standards in N-of-1 medical studies: a systematic review, article, July 18, 2023; (https://digital.library.unt.edu/ark:/67531/metadc2288932/m1/4/: accessed July 16, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Liberal Arts & Social Sciences.