Comprehensive meta analysis screen resolution
- #Comprehensive meta analysis screen resolution install#
- #Comprehensive meta analysis screen resolution software#
Attention: this attribute should same to Data().subgroup. subgroup ( attribute, array): Array to store the subgroup.studies ( attribute, array): Array of study data to meta-analysis.Attention: this attribute should same to Data().datatype. datatype ( attribute, string): set the data type:'CATE' for CATEgorical/count/binary/dichotomous data, or 'CONT' for CONTinuous data.Meta()( class): Set and perform the Meta-Analysis. Input: lines array (always from method readfile()).getdata(lines) ( method): Load data into attribute array of 'studies'.Output: lines array (always as input of method getdata()).readfile(filename) ( method): Read data file.nototal ( attribute, binary): Flag of do NOT calculate the total effect.studies ( attribute, array): Array to store the study data.datatype ( attribute, string): set the data type:'CATE' for CATEgorical/count/binary/dichotomous data, or 'CONT' for continuous data.Help()( function): Show help information of PythonMeta.ĭata()( class): Set and Load data to analysis. There are four functions/classes in PythonMeta package: Import the PythonMeta module in your code: import PythonMeta
#Comprehensive meta analysis screen resolution install#
Install and update using pip: pip install PythonMeta This is an ongoing project, so, any questions and suggestions from you are very welcome. PythonMeta, Python module of Meta-analysis, cited 20xx-xx-xx (or your time) 1 screen(s). Statistical algorithms in Review Manager 5, August 2010.ĭeng Hongyong.
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Jonathan J Deeks and Julian PT Higgins, on behalf of the Statistical Methods Group of The Cochrane Collaboration.
#Comprehensive meta analysis screen resolution software#
Statistical algorithms in this software cited from: Plots drawing: forest plot, funnel plot, etc.Combining effect measures (OR, RR, RD for count data and MD, SMD for continuous data).This module was designed to perform some Evidence-based medicine (EBM) tasks, such as: Self-HPV testing may complement current screening programs by increasing population coverage in settings that do not have easy access to comprehensive cytology-based screening.Fixed a bug while calculating random effect subgroup's weight. The sensitivity of Self-HPV testing compared favorably with that of LBC and was superior to the sensitivity of VIA. Physician-HPV testing was more sensitive for detecting CIN2+ (97.0%) and CIN3+ (97.8%) but similarly specific for detecting CIN2+ (82.7%) and CIN3+ (81.3%) (all P values <.001) than Self-HPV testing. 341), and higher specificity for detecting CIN2+ (94.0%, P <. 015), similar sensitivity for detecting CIN3+ (89.0%, P =. 001) than Self-HPV testing, LBC had lower sensitivity for detecting CIN2+ (80.7%, P =. VIA had statistically significantly lower sensitivity for detecting CIN2+ (50.3%) and CIN3+ (55.7%) and higher specificity for detecting CIN2+ (87.4%) and CIN3+ (86.9%) (all P values <.
![comprehensive meta analysis screen resolution comprehensive meta analysis screen resolution](https://www.projectguru.in/publications/wp-content/uploads/2017/10/Picture11.png)
![comprehensive meta analysis screen resolution comprehensive meta analysis screen resolution](https://training.cochrane.org/sites/training.cochrane.org/files/public/uploads/handbook/chapter-10_files/image002.png)
Self-HPV testing had 86.2% sensitivity and 80.7% specificity for detecting CIN2+ and 86.1% sensitivity and 79.5% specificity for detecting CIN3+. Of 13 004 women included in the analysis, 507 (3.9%) were diagnosed as CIN2+, 273 (2.1%) as CIN3+, and 37 (0.3%) with cervical cancer. We analyzed the accuracies of pooled Self-HPV testing, Physician-HPV testing, VIA, and LBC to detect biopsy-confirmed cervical intraepithelial neoplasia grade 2 or more severe (CIN2+) and CIN3+. Screen-positive women underwent colposcopy and confirmatory biopsy. Participants (n = 13 140) received Self-HPV testing, physician-collected cervical specimens for HPV testing (Physician-HPV testing), liquid-based cytology (LBC), and visual inspection with acetic acid (VIA). We compiled individual patient data from five population-based cervical cancer-screening studies in China. By evaluating the diagnostic accuracy of self-collected cervicovaginal specimens tested for human papillomavirus (HPV) DNA (Self-HPV testing) in China, we sought to determine whether Self-HPV testing may serve as a primary cervical cancer screening method in low-resource settings. Worldwide, one-seventh of cervical cancers occur in China, which lacks a national screening program.