A sensitivity analysis was performed on the effect estimates using the limit approach of Rosenbaum. This approach explains that if there were the influence of unmeasured covariates, then a match between the two groups of covariates would not have the same probability of exposure allocation, and their probability ratio (Γ) would be different from one. By increasing the value of Γ to identify the probabilities of exposure allocation (Γ>1), the degree of influence that an unmeasured covariate must have on the impact allocation and the validity of a study can be observed. Sensitivity analyses were performed for different values of Γ>1, in increments of 0.05, to determine the extent to which the Γ value remained at a significance level of 0.05. The inferences are then considered sensitive to bias caused by non-measured covariates if the Γ values are closer to 1 by altering the results (>0.05), and are considered relatively robust if values greater than Γ are required to obtain results that affect inference. The mhbounds command of Stata was used for this sensitivity analysis [29].

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