Other studies that have assessed associations between variants in the DNA repair genes included in the current analysis and ovarian cancer risk [34], [35], [36], [37] are summarized in Table 2. Differences in the findings between studies when the SNPs were in high LD may be explained by differences in the analytic approach and the mode of inheritance analyzed.
Hence, the difference from our study, which pooled data from randomized trials only, can likely be explained by differences in the population studied and differences in the analytic methods.
This difference can be explained by major differences in the analytic procedure: first, we considered hospital mortality not 28- and 90-days mortalities; second, we used a PS matching method [ 21] whereas Iapichino provided estimates adjusted on PS quintiles.
Although this method is not directly comparable to existing methods because of differences in definining the analytic unit (ie, the number and type of variants collapsed in the analysis), results from analysis of the same regions using existing methods (SCORE-TEST and SKAT) are provided for context.
It should be noted that a direct comparison of 11C-PBR28 and 18 F-FDG is difficult due to important technical differences (mainly related to differences in voxel resolution) between microPET and autoradiography and profound differences in the AMR analytic technique between these two modalities (forced by the different physical half-life of 11C and 18 F).
The discordance with ESEfinder may be associated with differences in the respective analytic methods, as several of the models (SF2/ASF, SC35, SRp40) used by ASSA and ESEfinder were created from the same source of experimental data.
In this way, the adult comparison group helped establish the interpretation of the difference-in-differences analytic framework.
We chose a difference-in-differences analytic framework because the study period was relatively short, and we did not have adequate data to control for temporal trends using a more refined approach like interrupted time series.
In sensitivity testing, the distinctive features of our study are its evaluations of a comprehensive indicator of serious cardiovascular (CV) thromboembolic risk, alternate treatment regimens after UGI adverse events, and differences in the lengths of the analytic horizon.
More importantly, Figure 4(b)(B), for projects started after 2011, the differences in the fields of analytics, ontologies, text-mining, and semantic analysis are staggering.
We will implement a difference-in-difference analytic approach to evaluate pre-post APM changes between intervention and control groups, including: 1) clinic-level outcomes, 2) patient-level clinical outcomes, and 3) patient-level econometric outcomes.
