One metric derived from the parent report is based on comparison with outcomes for a non-treated group to control for fluctuations in psychopathology and regression to the mean.
The objective of this study was to conduct a meta-analysis of population based, multicentre, and single centre studies, to provide a reliable assessment of risks of pregnancy in women with chronic hypertension, drawing comparison with outcomes available from US studies and the US general population (2006) of pregnant women.
As methylation varied across CpG sites and between cases, unsupervised hierarchical cluster analysis was performed to identify groups of tumours with similar methylation for comparison with outcome data (Supplementary Figure 4b and c).
Similar Kaplan Meier plots were obtained and conclusions supported when the percentage methylation for the sample with the highest methylation for each case was used for comparison with outcome data (data not given).
In cases where more than one tumour from the same individual had been analysed, with different outcome with respect to the proportion of MGMT-positive tumour cells, the tumour with the highest percentage of MGMT-positive cells was used for the comparison with outcome of clinical treatment.
Pyrosequencing allows highly reproducible quantification of methylation at each CpG site within the chosen amplicon and has been found to be robust when applied to archival samples including glioblastomas (Mikeska et al, 2007), but use of pyrosequencing in analysis of extensive glioblastoma cohorts and comparison with outcome data has not been reported.
This and the difference in remission criteria make comparisons with outcomes in other studies such as STAR-D difficult.
In a recent study of 677 patients with metastatic or recurrent KRAS mutant lung cancers, no difference in median OS was observed for those patients harbouring G12C/V mutations compared with others (Yu et al, 2015); however, owing to the observational nature of the Yu et al (2015) study, it is difficult to draw comparisons with outcomes from clinical studies, such as ours.
Investigating the importance of non-health outcomes in comparison with health outcomes and disentangling the existing relationships between the different possible non-health outcomes are interesting directions for further research in health promotion.
It further makes a comparison possible with outcomes of earlier publications (Spijkervet et al, 1989b; Parulekar et al, 1998).
Observational studies in epidemiology often involve several sources of uncertainty, such as measurement error, proxy variables of unknown validity, confounder adjustment, and multiple comparisons with outcome variables.
