Heterogeneity is usually assessed via the well known Q and

Heterogeneity is usually assessed via the well known Q and GSI-IX price I(2) statistics, along with the random effects estimate they imply. In recent years, alternative methods for quantifying heterogeneity have been proposed, that are based on a ‘generalised’ Q statistic.


We review 18 IPD meta-analyses of RCTs into treatments for cancer, in order to quantify the amount of heterogeneity present and also to discuss practical methods for explaining heterogeneity.

Results: Differing results were obtained when the standard Q and I(2) statistics were used to test for the presence of heterogeneity. The two meta-analyses with the largest amount of heterogeneity were investigated further, and on inspection the straightforward application of a random effects model was not deemed appropriate. Compared to the standard Q statistic, the generalised Q statistic provided a more accurate platform for estimating the amount of heterogeneity in the 18 meta-analyses.

Conclusions: Explaining heterogeneity via the pre-specification of trial subgroups, graphical diagnostic tools and sensitivity analyses produced a more desirable outcome than an automatic application of the random effects model. STA-9090 Generalised Q statistic methods for quantifying and adjusting for heterogeneity should be incorporated as standard into statistical software. Software

is provided to help achieve this aim.”
“The aim of this study was to determine influence of selected lifestyle factors on kidney cancer. The study brings data from two centres of international multicentric hospital-based analytical observational case-control studies. Data were obtained from a group of 300 patients newly diagnosed with kidney cancer

(ICD-O-2 code C64) and 335 controls from two centres in the Czech Republic. Results showed that smoking increased OR to 1.09 (95% CI 0.77-1.55) and 1.06 (95% CI 0.73-1.52), but the results were not statistically significant. Selleck LY3023414 Obesity (BMIa (c) 3/430) created adjusted OR 1.71 (95% CI 1.11-2.66) and 1.44 (95% CI 0.91-2.28), showing a minor, statistically insignificant, effect of obesity on the development of kidney cancer. For hypertension, adjusted OR was 1.73 (95% CI 1.25-2.40), suggesting a minor to moderate effect of hypertension on kidney cancer. The analysis results showed a positive association between hereditary predisposition and the development of kidney cancer with an OR of 1.97 (95% CI 1.41-2.76) and 1.97 (95% CI 1.40-2.77) depending on the model of adjustment. The reasons for the high incidence of kidney cancer are not fully understood. Genetic polymorphisms, together with other lifestyle and environmental factors, are likely to contribute to various rates of kidney cancer incidence throughout the world.”
“Out-of-hospital cardiac arrest (OHCA) is a major public health problem. Unfortunately, in spite of recurring updated guidelines, survival of patients with OHCA had been unchanged for decades.

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