For example, we are reviewing an article and the sensitivity of a testing modality 87% while including 50 patients. How we will be able to calculate its upper and lower limit at 95% confidence interval while making a forest-plot?
Sensitivity is, mathematically speaking, a proportion. Thus, the standard method for determining the 95% confidence interval of a proportion applies. However, there are several known nuanced ways of reaching such interval estimate, one of which is Clopper-Pearson exemplified in this link: https://www.medcalc.org/calc/diagnostic_test.php. Wikipedia has a nice overview of the different binomial proportion confidence interval estimation techniques: https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval.
You may try this by using Chart Builder.
NEW FILE.
DATASET CLOSE ALL.
GET FILE "C:\SPSSdata\bankloan.sav".
DATASET NAME raw.
* OMS.
DATASET DECLARE logistic.
OMS
/SELECT TABLES
/IF COMMANDS=['Logistic Regression'] SUBTYPES=['Variables in the Equation']
/DESTINATION FORMAT=SAV NUMBERED=TableNumber_
OUTFILE='logistic' VIEWER=YES
/TAG = 'logistic'.
LOGISTIC REGRESSION VARIABLES default
/METHOD=ENTER age employ address income debtinc
/PRINT=CI(95)
/CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).
OMSEND TAG = ["logistic"].
DATASET ACTIVATE logistic.
COMPUTE Vfilter = Var2 NE "Constant".
FILTER by Vfilter.
VARIABLE LABELS Var2 "Variable".
* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Var2 MAXIMUM(Upper)[name="MAXIMUM_Upper"]
MINIMUM(Lower)[name="MINIMUM_Lower"] MEAN(ExpB)[name="MEAN_ExpB"] MISSING=LISTWISE REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Var2=col(source(s), name("Var2"), unit.category())
DATA: MAXIMUM_Upper=col(source(s), name("MAXIMUM_Upper"))
DATA: MINIMUM_Lower=col(source(s), name("MINIMUM_Lower"))
DATA: MEAN_ExpB=col(source(s), name("MEAN_ExpB"))
COORD: rect(dim(1,2), transpose())
GUIDE: axis(dim(1), label("Variable"))
GUIDE: axis(dim(2), label("Odds Ratio & 95% CI"))
SCALE: linear(dim(2), include(0))
ELEMENT: interval(position(region.spread.range(Var2*(MINIMUM_Lower+MAXIMUM_Upper))),
shape(shape.ibeam))
ELEMENT: point(position(Var2*MEAN_ExpB), shape(shape.circle))
END GPL.
Good luck
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