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. 2020 Apr 28;9(4):e18333. doi: 10.2196/18333

Table 2.

Estimated effects of intervention compared to control on primary and secondary binary outcomes at end of study.

Outcomes Control, n (%) Intervention, n (%) Primary modela

RRb (95% CI) P value
Primary outcome

HbA1cc < 7.0% xxx (xx.x) xxx (xx.x) x.xx (x.xx-x.xx) .xx
Secondary outcomes

FBGd < 7.0 mmol/L xxx (xx.x) xxx (xx.x) x.xx (x.xx-x.xx) .xx

BPe < 140/80 mmHgf xxx (xx.x) xxx (xx.x) x.xx (x.xx-x.xx) .xx

LDL-Cg < 2.6 mmol/L xxx (xx.x) xxx (xx.x) x.xx (x.xx-x.xx) .xx

Composite diabetes controlh,i xxx (xx.x) xxx (xx.x) x.xx (x.xx-x.xx) .xx

aPrimary model: log-binomial regression with generalized estimating equation (GEE) with adjustment of the baseline value of the analyzed outcome and clustering. The logistic regression with GEE will be employed as the alternative method in case of non-convergence, with indirectly derived relative risk reported.

bRR: relative risk.

cHbA1c: glycated hemoglobin.

dFBG: fasting blood glucose.

eBP: blood pressure.

fOnly systolic blood pressure at baseline and clustering were adjusted in the primary model for BP control.

gLDL-C: low-density lipoprotein cholesterol.

hComposite diabetes control: defined as HbA1c level <7.0%, BP <140/80 mmHg and LDL-C <2.6 mmol/L.

iNo baseline variable was adjusted in the primary model for the composite diabetes control.