(82011))=/پروژه R

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شرح پروژه

سلام وقتتون به خیر

Method
Statistical analysis

To assess each ORM, the mada R package of the R program (R-4.1.1 for Windows) was employed for meta-analysis. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals for each ORM were measured by a random-effects model. We used DOR in this meta-analysis to find out how much the odds of having a poor or high ovarian response increase when the ORM test is positive . Two separate meta-analyses were performed to evaluate each ORM's efficacy in predicting poor and high ovarian responses. The meta-analysis included forest plots that visually displayed the logarithm of the relative odds ratio (OR) for ORMs in predicting poor and high ovarian response. These plots illustrated the differences in diagnostic accuracy observed across the selected studies.

Furthermore this meta-analysis aggregated the findings of several studies that evaluated the diagnostic accuracy of ORMs using Receiver Operating Characteristic (ROC) curve analysis. We calculated the average Area Under the ROC Curve (AUC) and its confidence intervals, as well as the degree of heterogeneity between trials, using metrics such as I-squared (I2) and Cochran's Q statistic. To minimize the risk of bias for overall sensitivity and specificity, each study was weighted according to its reversed variance via the fixed effect model of MantelHaenzel. The (Begg's) funnel plot is a scatterplot that was used in this meta-analysis to visually identify the publication bias. DerSimonianLaird's random effects model, which estimates the disparity across selected publications, was preferred to evaluate between-study heterogeneity since it can provide broader confidence intervals. [32].

Result

Meta-analysis was performed for each ORM for high and poor response groups. The Forest plots demonstrate the logarithm of relative OR for ORMs to predict poor (Figure 2) and high response (Figure 3) in each selected study. The ROC curve analysis in this meta-analysis demonstrated the diagnostic accuracy of ORMs in predicting ovarian response to COH. For predicting poor ovarian response, AMH had the highest DOR and AUC, indicating that it is the most accurate marker. AFC was the second-best marker, with a comparable AUC but somewhat lower DOR. The accuracy of E2 and FSH was much lower than those of AMH and AFC. AFC had the highest diagnostic accuracy for predicting a high ovarian response, with AMH coming in second. Despite its limited diagnostic accuracy, E2 exhibited the best AUC of the ORMs for detecting high responders (Table 3).

Random-effects meta-analysis using tau and tau-squared with 95% CI for each ORM was performed to evaluate between-study heterogeneity. AFC had the highest variance in poor and high responders. According to Cochran's Q and I2 tests, both poor and high responders showed the most variability in AMH findings. Also, the chi-squared test showed the greatest heterogeneity among AMH sensitivities/specificities in high and low responder categories (Table 4). The funnel plot for all ORMs showed asymmetry which indicates publication bias in both poor and high responders category.

Please report the results of all statistical analyses you performed, either in textual descriptions or figures, such as a funnel plot for identifying publication bias and other relevant analyses.

Subgroup Analysis based on study country, study design, or sample size:

Funnel plot and subgroup analysis is required

82011
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