The influence of using propensity score matching method to analyze data on the effect evaluation of interventions in the parallel-group controlled trial

Objective To explore the influence on the intervention effect evaluation when applying propensity score matching (PSM) to process the cross-sectional survey data in the parallel-group controlled trial. Methods Data collected from the study of "intervention study on STD/AIDS infection reduction of low-fee female sex workers (FSWs)", a sub-project of a 12th five-year national science and technology major projects, were used as an example. PSM method was applied to match the two cross-sectional surveys data of low-fee FSWs before and after the intervention. The Chi-square test of outcome variables was carried out for the matched samples and the generalized linear mixed model (GLMM) was fitted. The influence of PSM on evaluation results was discussed. Results The sample size was 537 after PSM when using the key characteristic variables with significant difference as the matching factors. The two populations before and after intervention were completely comparable. The results of the GLMM analysis showed that intervention was the major factor for the reduction of syphilis infection. Compared with the OR value (0.51) obtained from the original data, the OR value (0.33) was 0.18 lower when using the data processed by PSM to fit the model. The confidence interval obtained from PSM data was (0.16-0.70), also narrower than original results (0.27-0.96), and farther from the number of 1. The results showed that PSM improved the accuracy of the evaluation and increased the syphilis infection reduction rate caused by intervention from 49% to 67%. Conclusions When applied in the series cross-sectional survey of parallel-group controlled trial,PSM method can effectively improve the comparability of different study populations and reduce the influence of population difference on effect evaluation, so as to improve the accuracy of the research results.