- Email: [email protected]
Multivariate Ordered Logistic Regression Models: Dealing with the Model-Building Strategy
Accepted Manuscript Multivariate ordered logistic regression models: dealing with the model-building strategy Antonio Palazón-Bru, PhD, María I. Tomás-Rodríguez, PhD, María Teresa LópezCascales, MSc, David M. Folgado-de la Rosa, MSc, Vicente F. Gil-Guillén, MD, PhD PII:
S1083-3188(17)30423-0
DOI:
10.1016/j.jpag.2017.06.006
Reference:
PEDADO 2134
To appear in:
Journal of Pediatric and Adolescent Gynecology
Please cite this article as: Palazón-Bru A, Tomás-Rodríguez MI, López-Cascales MT, Folgadode la Rosa DM, Gil-Guillén VF, Multivariate ordered logistic regression models: dealing with the model-building strategy, Journal of Pediatric and Adolescent Gynecology (2017), doi: 10.1016/ j.jpag.2017.06.006. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1
ACCEPTED MANUSCRIPT TITLE PAGE Title: Multivariate ordered logistic regression models: dealing with the model-building strategy.
RI PT
Authors: Antonio Palazón-Bru, PhD1, María I Tomás-Rodríguez, PhD2, María Teresa LópezCascales, MSc3, David M Folgado-de la Rosa, MSc1, Vicente F Gil-Guillén, MD, PhD1.
1: Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante,
SC
Alicante, Spain.
M AN U
2: Department of Pathology and Surgery, Miguel Hernández University, San Juan de Alicante, Alicante, Spain.
3: Department of Molecular Neurobiology, Neurosciences Institute (Miguel Hernández University and Consejo Superior de Investigaciones Científicas), San Juan de Alicante,
TE D
Alicante, Spain.
Corresponding author: Prof. Antonio Palazón-Bru, PhD. Department of Clinical Medicine,
EP
Miguel Hernández University, Carretera de Valencia - Alicante S/N, 03550 San Juan de Alicante, Alicante, Spain. Telephone: +34 965919449. Fax: +34 965919450. E-mail:
AC C
[email protected].
Conflict of interest statement: Nothing to declare.
2
ACCEPTED MANUSCRIPT To the editor-in-chief, Khazaei et al criticise the mathematical model recently described by us.
1,2
Their first
comment concerns the construction of the multivariate model, which they suggest should
RI PT
follow a strategy based solely on statistical tests as the criterion for whether or not a variable should be introduced. However, this technique is not wholly correct as, although they may be non-significant predictors, when making a prediction it is possible that, overall, they act
SC
differently after adjustment for the other factors and the model therefore would have less precisión.3-5 Accordingly, all the factors were introduced and their number does not violate
M AN U
the ratio of events per variable, which should always be greater than or equal to 10.3,6,7 The second point raised concerns whether two odds ratio (OR) values should appear for the variables menstrual cycle length, duration and flow. It should be noted that these variables were evaluated quantitatively (“In the multivariate model, menstrual cycle length,
TE D
duration, and flow were quantified as 0 (lowest category), 1 (medium category), and 2 (highest category).”), so that only one OR value is applied, which quantifies the increase in
EP
risk when passing from one category to the next within each factor.
ACKNOWLEDGEMENTS
AC C
The authors thank Ian Johnstone for their help with the English version of this work.
3
ACCEPTED MANUSCRIPT REFERENCES 1. Tomás-Rodríguez MI, Palazón-Bru A, Martínez-St John DR, Navarro-Cremades F, Toledo-Marhuenda JV, Gil-Guillén VF. Factors Associated with Increased Pain in Primary
RI PT
Dysmenorrhea: Analysis Using a Multivariate Ordered Logistic Regression Model. J Pediatr Adolesc Gynecol 2017; 30: 199-202.
2. Khazaei S, Hanis SM, Mansori K. Comment on: Factors associated with increased pain in
M AN U
Pediatr Adolesc Gynecol 2017; XX: PP.
SC
primary dysmenorrhoea: analysis through a multivariate ordered logistic regression model. J
3. Collins GS, Mallett S, Omar O, Yu LM. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med 2011; 9: 103. 4. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing
1996; 15: 361-387.
TE D
models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med
EP
5. Sun GW, Shook TL, Kay GL: Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 1996; 49: 907-16.
AC C
6. Collins GS, Omar O, Shanyinde M, Yu LM. A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods. J Clin Epidemiol 2013; 66: 268-77. 7. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ 2016; 353: i2416.
S1083-3188(17)30423-0
DOI:
10.1016/j.jpag.2017.06.006
Reference:
PEDADO 2134
To appear in:
Journal of Pediatric and Adolescent Gynecology
Please cite this article as: Palazón-Bru A, Tomás-Rodríguez MI, López-Cascales MT, Folgadode la Rosa DM, Gil-Guillén VF, Multivariate ordered logistic regression models: dealing with the model-building strategy, Journal of Pediatric and Adolescent Gynecology (2017), doi: 10.1016/ j.jpag.2017.06.006. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1
ACCEPTED MANUSCRIPT TITLE PAGE Title: Multivariate ordered logistic regression models: dealing with the model-building strategy.
RI PT
Authors: Antonio Palazón-Bru, PhD1, María I Tomás-Rodríguez, PhD2, María Teresa LópezCascales, MSc3, David M Folgado-de la Rosa, MSc1, Vicente F Gil-Guillén, MD, PhD1.
1: Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante,
SC
Alicante, Spain.
M AN U
2: Department of Pathology and Surgery, Miguel Hernández University, San Juan de Alicante, Alicante, Spain.
3: Department of Molecular Neurobiology, Neurosciences Institute (Miguel Hernández University and Consejo Superior de Investigaciones Científicas), San Juan de Alicante,
TE D
Alicante, Spain.
Corresponding author: Prof. Antonio Palazón-Bru, PhD. Department of Clinical Medicine,
EP
Miguel Hernández University, Carretera de Valencia - Alicante S/N, 03550 San Juan de Alicante, Alicante, Spain. Telephone: +34 965919449. Fax: +34 965919450. E-mail:
AC C
[email protected].
Conflict of interest statement: Nothing to declare.
2
ACCEPTED MANUSCRIPT To the editor-in-chief, Khazaei et al criticise the mathematical model recently described by us.
1,2
Their first
comment concerns the construction of the multivariate model, which they suggest should
RI PT
follow a strategy based solely on statistical tests as the criterion for whether or not a variable should be introduced. However, this technique is not wholly correct as, although they may be non-significant predictors, when making a prediction it is possible that, overall, they act
SC
differently after adjustment for the other factors and the model therefore would have less precisión.3-5 Accordingly, all the factors were introduced and their number does not violate
M AN U
the ratio of events per variable, which should always be greater than or equal to 10.3,6,7 The second point raised concerns whether two odds ratio (OR) values should appear for the variables menstrual cycle length, duration and flow. It should be noted that these variables were evaluated quantitatively (“In the multivariate model, menstrual cycle length,
TE D
duration, and flow were quantified as 0 (lowest category), 1 (medium category), and 2 (highest category).”), so that only one OR value is applied, which quantifies the increase in
EP
risk when passing from one category to the next within each factor.
ACKNOWLEDGEMENTS
AC C
The authors thank Ian Johnstone for their help with the English version of this work.
3
ACCEPTED MANUSCRIPT REFERENCES 1. Tomás-Rodríguez MI, Palazón-Bru A, Martínez-St John DR, Navarro-Cremades F, Toledo-Marhuenda JV, Gil-Guillén VF. Factors Associated with Increased Pain in Primary
RI PT
Dysmenorrhea: Analysis Using a Multivariate Ordered Logistic Regression Model. J Pediatr Adolesc Gynecol 2017; 30: 199-202.
2. Khazaei S, Hanis SM, Mansori K. Comment on: Factors associated with increased pain in
M AN U
Pediatr Adolesc Gynecol 2017; XX: PP.
SC
primary dysmenorrhoea: analysis through a multivariate ordered logistic regression model. J
3. Collins GS, Mallett S, Omar O, Yu LM. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med 2011; 9: 103. 4. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing
1996; 15: 361-387.
TE D
models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med
EP
5. Sun GW, Shook TL, Kay GL: Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 1996; 49: 907-16.
AC C
6. Collins GS, Omar O, Shanyinde M, Yu LM. A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods. J Clin Epidemiol 2013; 66: 268-77. 7. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ 2016; 353: i2416.