Reliability of a Meta-analysis of Air Quality−Asthma Cohort Studies

S. Stanley Young, Kai-Chieh Cheng, Jin Hua Chen, Shu-Chuan Chen and Warren B. Kindzierski

International Journal of Statistics and Probability, 2022, vol. 11, issue 2, 61

Abstract: What may be a contributing cause of the replication problem in science – multiple testing bias – was examined in this study. Independent analysis was performed on a meta-analysis of cohort studies associating ambient exposure to nitrogen dioxide (NO2) and fine particulate matter (PM2.5) with development of asthma. Statistical tests used in 19 base papers from the meta-analysis were counted. Test statistics and confidence intervals from the base papers used for meta-analysis were converted to p-values. A combined p-value plot for NO2 and PM2.5 was constructed to evaluate the effect heterogeneity of the p-values. Large numbers of statistical tests were estimated in the 19 base papers – median 13,824 (interquartile range 1,536−221,184). Given these numbers, there is little assurance that test statistics used from the base papers for meta-analysis are unbiased. The p-value plot of test statistics showed a two-component mixture. The shape of the p-value plot for NO2 suggests the use of questionable research practices related to small p-values in some of the cohort studies. All p-values for PM2.5 fall on a 45-degree line in the p-value plot indicating randomness. The claim that ambient exposure to NO2 and PM2.5 is associated with development of asthma is not supported by our analysis.

Date: 2022
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