R Kumar1, A Indrayan, P Chhabra. 1. Department of Biostatistics and Medical Informatics, University College of Medical Sciences, Delhi, India.
Abstract
BACKGROUND: Use of multivariable logistic regression (MLR) modeling has steeply increased in the medical literature over the past few years. Testing of model assumptions and adequate reporting of MLR allow the reader to interpret results more accurately. AIMS: To review the fulfillment of assumptions and reporting quality of MLR in selected Indian medical journals using established criteria. SETTING AND DESIGN: Analysis of published literature. MATERIALS AND METHODS: Medknow.com publishes 68 Indian medical journals with open access. Eight of these journals had at least five articles using MLR between the years 1994 to 2008. Articles from each of these journals were evaluated according to the previously established 10-point quality criteria for reporting and to test the MLR model assumptions. STATISTICAL ANALYSIS: SPSS 17 software and non-parametric test (Kruskal-Wallis H, Mann Whitney U, Spearman Correlation). RESULTS: One hundred and nine articles were finally found using MLR for analyzing the data in the selected eight journals. The number of such articles gradually increased after year 2003, but quality score remained almost similar over time. P value, odds ratio, and 95% confidence interval for coefficients in MLR was reported in 75.2% and sufficient cases (>10) per covariate of limiting sample size were reported in the 58.7% of the articles. No article reported the test for conformity of linear gradient for continuous covariates. Total score was not significantly different across the journals. However, involvement of statistician or epidemiologist as a co-author improved the average quality score significantly (P=0.014). CONCLUSIONS: Reporting of MLR in many Indian journals is incomplete. Only one article managed to score 8 out of 10 among 109 articles under review. All others scored less. Appropriate guidelines in instructions to authors, and pre-publication review of articles using MLR by a qualified statistician may improve quality of reporting.
BACKGROUND: Use of multivariable logistic regression (MLR) modeling has steeply increased in the medical literature over the past few years. Testing of model assumptions and adequate reporting of MLR allow the reader to interpret results more accurately. AIMS: To review the fulfillment of assumptions and reporting quality of MLR in selected Indian medical journals using established criteria. SETTING AND DESIGN: Analysis of published literature. MATERIALS AND METHODS: Medknow.com publishes 68 Indian medical journals with open access. Eight of these journals had at least five articles using MLR between the years 1994 to 2008. Articles from each of these journals were evaluated according to the previously established 10-point quality criteria for reporting and to test the MLR model assumptions. STATISTICAL ANALYSIS: SPSS 17 software and non-parametric test (Kruskal-Wallis H, Mann Whitney U, Spearman Correlation). RESULTS: One hundred and nine articles were finally found using MLR for analyzing the data in the selected eight journals. The number of such articles gradually increased after year 2003, but quality score remained almost similar over time. P value, odds ratio, and 95% confidence interval for coefficients in MLR was reported in 75.2% and sufficient cases (>10) per covariate of limiting sample size were reported in the 58.7% of the articles. No article reported the test for conformity of linear gradient for continuous covariates. Total score was not significantly different across the journals. However, involvement of statistician or epidemiologist as a co-author improved the average quality score significantly (P=0.014). CONCLUSIONS: Reporting of MLR in many Indian journals is incomplete. Only one article managed to score 8 out of 10 among 109 articles under review. All others scored less. Appropriate guidelines in instructions to authors, and pre-publication review of articles using MLR by a qualified statistician may improve quality of reporting.