In India, the most common cancer among men is cancer of lip-oral and its incidence is further increasing. Nodal metastasis is an important prognostic factor in oral cancer. Previous studies on factors associated with ...In India, the most common cancer among men is cancer of lip-oral and its incidence is further increasing. Nodal metastasis is an important prognostic factor in oral cancer. Previous studies on factors associated with nodal involvement are mainly focused on clinical exploration, and there is very little work in statistical modeling for nodal involvement. Also, the available studies have limited covariates and their varying forms. Further, studies available from India have mainly focused either on occult nodal metastasis only or a specific site of oral or stages. Hence, in order to identify epidemiological determinants of nodal metastasis, objective of this study was to develop a regression model to find out factors associated with nodal involvement and assess its validity. 945 histopathologically proven oral squamous cell carcinoma (OSCC) patients who went under surgery including neck dissection during 1995-2013 at the Department of Surgical Oncology, Dr. BRA-IRCH, AIIMS, New Delhi, India, were included for model building. Another data of 204 patients available during 2014-2015 was used for the temporal validation of the developed model. To assess the factors associated with nodal involvement, stepwise multivariable logistic regression procedure was used and results are presented as odds ratio and corresponding 95% confidence interval (CI). Pain at the time of presentation [1.34 (1.02 to 1.77)], sub mucous fibrosis (SMF) [0.45 (0.21 to 0.95)], palpable neck node [2.38 (1.69 to 3.35)], tongue [1.63 (1.07 to 2.46)] as compared to buccal mucosa and degree of differentiation [1.41 (1.05 to 1.89)] were found to be significantly associated with nodal involvement. Further, diagnostic performance of the developed model was found to be satisfactory on temporal validation. These data suggest that, Pain at time of presentation, presence of clinical neck node, SMF, degree of differentiation and oral site are the most probable factors associated with nodal involvement in OSCC.展开更多
Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-disp...Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-dispersion—a larger variability than expected. Until now, the negative binomial model has been used to describe this distribution assuming that over-dispersion is only due to unobserved heterogeneity. The distribution of involved nodes contains a large proportion of excess zeros (negative nodes), which can lead to over-dispersion. In this situation, alternative models may better account for over-dispersion due to excess zeros. This study examines data from 1152 patients who underwent axillary dissections in a tertiary hospital in India during January 1993-January 2005. We fit and compare various count models to test model abilities to predict the number of involved nodes. We also argue for using zero inflated models in such populations where all the excess zeros come from those who have at some risk of the outcome of interest. The negative binomial regression model fits the data better than the Poisson, zero hurdle/inflated Poisson regression models. However, zero hurdle/inflated negative binomial regression models predicted the number of involved nodes much more accurately than the negative binomial model. This suggests that the number of involved nodes displays excess variability not only due to unobserved heterogeneity but also due to excess negative nodes in the data set. In this analysis, only skin changes and primary site were associated with negative nodes whereas parity, skin changes, primary site and size of tumor were associated with a greater number of involved nodes. In case of near equal performances, the zero inflated negative binomial model should be preferred over the hurdle model in describing the nodal frequency because it provides an estimate of negative nodes that are at “high-risk” of nodal involvement.展开更多
文摘In India, the most common cancer among men is cancer of lip-oral and its incidence is further increasing. Nodal metastasis is an important prognostic factor in oral cancer. Previous studies on factors associated with nodal involvement are mainly focused on clinical exploration, and there is very little work in statistical modeling for nodal involvement. Also, the available studies have limited covariates and their varying forms. Further, studies available from India have mainly focused either on occult nodal metastasis only or a specific site of oral or stages. Hence, in order to identify epidemiological determinants of nodal metastasis, objective of this study was to develop a regression model to find out factors associated with nodal involvement and assess its validity. 945 histopathologically proven oral squamous cell carcinoma (OSCC) patients who went under surgery including neck dissection during 1995-2013 at the Department of Surgical Oncology, Dr. BRA-IRCH, AIIMS, New Delhi, India, were included for model building. Another data of 204 patients available during 2014-2015 was used for the temporal validation of the developed model. To assess the factors associated with nodal involvement, stepwise multivariable logistic regression procedure was used and results are presented as odds ratio and corresponding 95% confidence interval (CI). Pain at the time of presentation [1.34 (1.02 to 1.77)], sub mucous fibrosis (SMF) [0.45 (0.21 to 0.95)], palpable neck node [2.38 (1.69 to 3.35)], tongue [1.63 (1.07 to 2.46)] as compared to buccal mucosa and degree of differentiation [1.41 (1.05 to 1.89)] were found to be significantly associated with nodal involvement. Further, diagnostic performance of the developed model was found to be satisfactory on temporal validation. These data suggest that, Pain at time of presentation, presence of clinical neck node, SMF, degree of differentiation and oral site are the most probable factors associated with nodal involvement in OSCC.
文摘Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-dispersion—a larger variability than expected. Until now, the negative binomial model has been used to describe this distribution assuming that over-dispersion is only due to unobserved heterogeneity. The distribution of involved nodes contains a large proportion of excess zeros (negative nodes), which can lead to over-dispersion. In this situation, alternative models may better account for over-dispersion due to excess zeros. This study examines data from 1152 patients who underwent axillary dissections in a tertiary hospital in India during January 1993-January 2005. We fit and compare various count models to test model abilities to predict the number of involved nodes. We also argue for using zero inflated models in such populations where all the excess zeros come from those who have at some risk of the outcome of interest. The negative binomial regression model fits the data better than the Poisson, zero hurdle/inflated Poisson regression models. However, zero hurdle/inflated negative binomial regression models predicted the number of involved nodes much more accurately than the negative binomial model. This suggests that the number of involved nodes displays excess variability not only due to unobserved heterogeneity but also due to excess negative nodes in the data set. In this analysis, only skin changes and primary site were associated with negative nodes whereas parity, skin changes, primary site and size of tumor were associated with a greater number of involved nodes. In case of near equal performances, the zero inflated negative binomial model should be preferred over the hurdle model in describing the nodal frequency because it provides an estimate of negative nodes that are at “high-risk” of nodal involvement.
