Literature DB >> 18238980

Evaluation of logistic regression reporting in current obstetrics and gynecology literature.

Rafael T Mikolajczyk1, Alexis DiSilvestro, Alexis DiSilvesto, Jun Zhang.   

Abstract

OBJECTIVE: To evaluate the quality of logistic regression reporting in the obstetrics and gynecology literature.
METHODS: All original papers published in 2005 and 2006 in four leading obstetrics and gynecology journals were manually searched for the use of logistic regression. One hundred four articles that used logistic regression were randomly selected (13 from each journal and each year) and evaluated according to previously established criteria for reporting logistic regression analyses. Rates of compliance with these criteria were calculated separately for each journal and weighted according to the number of articles using logistic regression in each of the journals in the same period to obtain an overall estimate.
RESULTS: Logistic regression was used in 34.2% of all original research articles (724 of 2,234) in the four journals for the study period. Statistical significance of estimates was reported in 96% of examined articles. Criteria of variable selection for the logistic regression model were reported in 76% of articles, and coding of variables was described in 83%. Overfitting (models with too many variables for the number of outcome events) occurred in 57% of studies. The majority of examined articles insufficiently reported information for the remaining criteria-testing for interactions (18%), conformity to a linear gradient of continuous variables (9%), goodness of fit (3.6%), assessment of multi-collinearity (0.46%), and validation of the model (0%).
CONCLUSION: Logistic regression has become a standard statistical method in obstetrics and gynecology literature. Although some standards are mostly fulfilled, there is still considerable room for improvement. LEVEL OF EVIDENCE: III.

Entities:  

Mesh:

Year:  2008        PMID: 18238980     DOI: 10.1097/AOG.0b013e318160f38e

Source DB:  PubMed          Journal:  Obstet Gynecol        ISSN: 0029-7844            Impact factor:   7.661


  14 in total

Review 1.  [Delirium in stroke patients : Critical analysis of statistical procedures for the identification of risk factors].

Authors:  P Nydahl; N G Margraf; A Ewers
Journal:  Med Klin Intensivmed Notfmed       Date:  2017-01-31       Impact factor: 0.840

2.  First-trimester placental protein 13, PAPP-A, uterine artery Doppler and maternal characteristics in the prediction of pre-eclampsia.

Authors:  A O Odibo; Y Zhong; K R Goetzinger; L Odibo; J L Bick; C R Bower; D M Nelson
Journal:  Placenta       Date:  2011-06-08       Impact factor: 3.481

Review 3.  Reporting methods in studies developing prognostic models in cancer: a review.

Authors:  Susan Mallett; Patrick Royston; Susan Dutton; Rachel Waters; Douglas G Altman
Journal:  BMC Med       Date:  2010-03-30       Impact factor: 8.775

Review 4.  Reporting performance of prognostic models in cancer: a review.

Authors:  Susan Mallett; Patrick Royston; Rachel Waters; Susan Dutton; Douglas G Altman
Journal:  BMC Med       Date:  2010-03-30       Impact factor: 8.775

5.  Methodology and analytic techniques used in clinical research: associations with journal impact factor.

Authors:  Lindsay M Kuroki; Jenifer E Allsworth; Jeffrey F Peipert
Journal:  Obstet Gynecol       Date:  2009-10       Impact factor: 7.661

6.  Obesity and the incidence of bladder injury and urinary retention following tension-free vaginal tape procedure: retrospective cohort study.

Authors:  Vladimir Revicky; Sambit Mukhopadhyay; Frances de Boer; Edward P Morris
Journal:  Obstet Gynecol Int       Date:  2011-06-22

Review 7.  Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting.

Authors:  Gary S Collins; Susan Mallett; Omar Omar; Ly-Mee Yu
Journal:  BMC Med       Date:  2011-09-08       Impact factor: 8.775

8.  Identification of lung cancer breath biomarkers based on perioperative breathomics testing: A prospective observational study.

Authors:  Peiyu Wang; Qi Huang; Shushi Meng; Teng Mu; Zheng Liu; Mengqi He; Qingyun Li; Song Zhao; Shaodong Wang; Mantang Qiu
Journal:  EClinicalMedicine       Date:  2022-04-16

9.  Determining relative importance of variables in developing and validating predictive models.

Authors:  Joseph Beyene; Eshetu G Atenafu; Jemila S Hamid; Teresa To; Lillian Sung
Journal:  BMC Med Res Methodol       Date:  2009-09-14       Impact factor: 4.615

10.  Poor quality in the reporting and use of statistical methods in public health - the case of unemployment and health.

Authors:  Fredrik Norström
Journal:  Arch Public Health       Date:  2015-11-16
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.