3.1 Dose-response meta-analysis
In order to analyze the correlation between BW and the risk of cancers,
we used the dose-response meta-analysis to reflect the overall trend
change of exposure (BW) level and the risk of outcome (cancer risk)
indicators 46. Firstly, for all collected studies, we
chose the most adjusted risk estimates and 95% confidence interval for
the highest BW group versus the lowest group (reference). And the
reported HRs and ORs were approximately considered RRs47. Then, we used both “Random-effects models” and
“Fixed-effects models” to calculate the summarized RR estimates. If
the heterogeneity was low (I2<50%), we used
the value of fixed-effects models. Otherwise, we preferred to used
random-effects models.
To estimate study‐specific dose‐response curves between BW and different
types of cancer risk, we chose three models for fitting. The generalized
least squares (GLS) model estimated the linear dose-response calculating
the study‐specific RR of per 500g BW increment. The restricted cubic
spline model was used to estimate the nonlinear trend of the
dose-response relation 48. In the dose distribution,
three knots were set to fit the model adjusting appropriately according
to different cancer data. The accuracy of nonlinear fitting was assessed
by the Wald test to determine whether the combined dose-response
relationship is nonlinear. In addition, the quadratic model was also
applied to estimate the nonlinear relationship between exposure and
outcome which using the maximum likelihood estimation method as
parameter estimation method. The heterogeneity across included studies
was tested by the Q test and I2 test. We also tested
the sensitivity by excluding one study at a time. The Egger’s test and
the symmetry of the funnel plot were used to evaluate potential
publication bias 49. All analyses were performed R
(version 4.0.5) software with the packages of “dosresmeta”,
“metaphor”, “mvmeta”, “rms” and “metafor”.