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Lateral lymph node recurrence after total thyroidectomy and central neck dissection in patients with papillary thyroid cancer without clinical evidence of lateral neck metastasis: Comment on data sparsity
Oral Oncology xxx (2017) xxx–xxx
Contents lists available at ScienceDirect
Oral Oncology journal homepage: www.elsevier.com/locate/oraloncology
Letter to the editor Lateral lymph node recurrence after total thyroidectomy and central neck dissection in patients with papillary thyroid cancer without clinical evidence of lateral neck metastasis: Comment on data sparsity
Dear Editor-in-Chief, We have read the paper authored by Lim and colleagues that was published in the journal of Oral Oncology in November 2016, meticulously [1]. The authors purposed to examine predictive factors for lateral lymph node (LN) recurrence in patients with papillary thyroid cancer without clinical evidence of lateral LN metastasis. They found that tumor size > 1 cm, central LN metastasis and extranodal extension of a metastatic central LN were independently associated with lateral LN recurrence [1]. It is absolutely obvious that the valuable investigation has been conducted but an important methodological point need to be taken into account. The authors found very large adjusted odds ratio (OR) with wide confidence interval (CI) on the association between extranodal extension of a metastatic central LN and lateral LN recurrence which is questionable (OR = 38.82; 95% CI: 5.71–264.10). It is stated that the large effect estimate with large CI should not be always interpreted as large effect but it is maybe representing large error [2]. In fact, large effect estimate with unnecessarily wide CI is yielded when the number of the events is rare in different strata of the exposure and outcome variables [2,3]. It can be said that the sparse data lead to a specific type of bias namely sparse data bias which is controllable. The data provided by Lim and colleagues in their Table 5 on the association between extranodal extension of a metastatic central LN and lateral LN recurrence was more clarified in our Table 1. It was obvious that the data sparsity is inevitable in their study and resulting bias should be removed/decreased appropriately. There are several methods to address this bias but one of the robust methods namely penalization through data augmentation has been proposed by Greenland et al. in 2016 which effectively address this problem [2]. We Table 1 The univariate association between extranodal extension of a metastatic central LN and lateral LN recurrence using ordinary and penalized logistic regression. Lateral recurrence
No lateral recurrence
Extranodal extension Yes No
6 4
3 97
Estimated OR (95% confidence interval) Ordinary logistic regression Penalized estimation through data augmentation
48.5 (8.78–267.88) 25.24 (5.29–120.26)
http://dx.doi.org/10.1016/j.oraloncology.2017.04.006 1368-8375/Ó 2017 Elsevier Ltd. All rights reserved.
re-re-analyzed their data using the log-f (2,2) distribution prior to obtain crude association between the two aforementioned exposure and outcome. It was found that the crude OR and its CI was improved considerably (Table 1). To analyze the adjusted associations using the penalized logistic regression in the multivariate model, the individual data should be provided [2]. Hence, we suggest Lim and colleagues to re-analyze their data using the introduced robust method to obtain more valid and precise effect estimate in their study. As take home message for the readers is that the data sparsity should be checked and addressed before the large effect estimates to be interpreted. Conflict of interest The authors indicated no potential conflicts of interest. Acknowledgments The present study was not funded by any organization. References [1] Lim YC, Liu L, Chang JW, Koo BS. Lateral lymph node recurrence after total thyroidectomy and central neck dissection in patients with papillary thyroid cancer without clinical evidence of lateral neck metastasis. Oral Oncol 2016;62:109–13. [2] Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ 2016;352:i1981. [3] Greenland S, Mansournia MA. Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions. Stat Med 2015;34:3133–43.
Erfan Ayubi Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
⇑
Saeid Safiri Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran Department of Epidemiology & Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran ⇑ Corresponding author at: Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran. E-mail address: [email protected] Available online xxxx
Contents lists available at ScienceDirect
Oral Oncology journal homepage: www.elsevier.com/locate/oraloncology
Letter to the editor Lateral lymph node recurrence after total thyroidectomy and central neck dissection in patients with papillary thyroid cancer without clinical evidence of lateral neck metastasis: Comment on data sparsity
Dear Editor-in-Chief, We have read the paper authored by Lim and colleagues that was published in the journal of Oral Oncology in November 2016, meticulously [1]. The authors purposed to examine predictive factors for lateral lymph node (LN) recurrence in patients with papillary thyroid cancer without clinical evidence of lateral LN metastasis. They found that tumor size > 1 cm, central LN metastasis and extranodal extension of a metastatic central LN were independently associated with lateral LN recurrence [1]. It is absolutely obvious that the valuable investigation has been conducted but an important methodological point need to be taken into account. The authors found very large adjusted odds ratio (OR) with wide confidence interval (CI) on the association between extranodal extension of a metastatic central LN and lateral LN recurrence which is questionable (OR = 38.82; 95% CI: 5.71–264.10). It is stated that the large effect estimate with large CI should not be always interpreted as large effect but it is maybe representing large error [2]. In fact, large effect estimate with unnecessarily wide CI is yielded when the number of the events is rare in different strata of the exposure and outcome variables [2,3]. It can be said that the sparse data lead to a specific type of bias namely sparse data bias which is controllable. The data provided by Lim and colleagues in their Table 5 on the association between extranodal extension of a metastatic central LN and lateral LN recurrence was more clarified in our Table 1. It was obvious that the data sparsity is inevitable in their study and resulting bias should be removed/decreased appropriately. There are several methods to address this bias but one of the robust methods namely penalization through data augmentation has been proposed by Greenland et al. in 2016 which effectively address this problem [2]. We Table 1 The univariate association between extranodal extension of a metastatic central LN and lateral LN recurrence using ordinary and penalized logistic regression. Lateral recurrence
No lateral recurrence
Extranodal extension Yes No
6 4
3 97
Estimated OR (95% confidence interval) Ordinary logistic regression Penalized estimation through data augmentation
48.5 (8.78–267.88) 25.24 (5.29–120.26)
http://dx.doi.org/10.1016/j.oraloncology.2017.04.006 1368-8375/Ó 2017 Elsevier Ltd. All rights reserved.
re-re-analyzed their data using the log-f (2,2) distribution prior to obtain crude association between the two aforementioned exposure and outcome. It was found that the crude OR and its CI was improved considerably (Table 1). To analyze the adjusted associations using the penalized logistic regression in the multivariate model, the individual data should be provided [2]. Hence, we suggest Lim and colleagues to re-analyze their data using the introduced robust method to obtain more valid and precise effect estimate in their study. As take home message for the readers is that the data sparsity should be checked and addressed before the large effect estimates to be interpreted. Conflict of interest The authors indicated no potential conflicts of interest. Acknowledgments The present study was not funded by any organization. References [1] Lim YC, Liu L, Chang JW, Koo BS. Lateral lymph node recurrence after total thyroidectomy and central neck dissection in patients with papillary thyroid cancer without clinical evidence of lateral neck metastasis. Oral Oncol 2016;62:109–13. [2] Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ 2016;352:i1981. [3] Greenland S, Mansournia MA. Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions. Stat Med 2015;34:3133–43.
Erfan Ayubi Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
⇑
Saeid Safiri Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran Department of Epidemiology & Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran ⇑ Corresponding author at: Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran. E-mail address: [email protected] Available online xxxx