SPINE Volume 39, Number 16, p 1313 ©2014, Lippincott Williams & Wilkins
JOURNAL CLUB
Point of View Marcia A. Ciol, PhD
T
his article brings up important issues in analysis of large databases. Although these databases provide information that is difficult to obtain otherwise, these data might have been collected for billing/administrative purposes (e.g., Medicare claims data), or were collected as part of complex surveys with a more general purpose (e.g., Medicare Current Beneficiaries Survey). The latter might offer more possibilities for studies, but it is still limited to whatever the survey originally asked. Therefore, the first rule to use a large database is to know it very well, including what data were collected and how. A researcher looking for a quick publication from an existent database will be frustrated and surprised by the time that it takes to understand the database well. An important issue not mentioned in the article is the analysis of data from weighted samples. The Kids Inpatient Database is an example of a weighted sample, and to produce
national estimates the researcher must use the sampling weights in the calculation.1 I agree with the authors that the P value is not the sole determinant of how the results of a study should be interpreted. In fact, it is sad to see how the P value is frequently misused and confused with type I error (the significance level).2 All studies, including large databases, are only as good as their design, data collection, and statistical analyses are, and they all have limitations. Researchers should make sure that they thoroughly understand the database to understand its potential use and limitations.
References
1. Ciol MA, Hoffman JM, Dudgeon BJ, et al. Understanding the use of weights in the analysis of data from multistage surveys. Arch Phys Med Rehabil 2006;87:299–303. 2. Hubbard R. The widespread misinterpretation of P values as error probabilities. J Appl Stat 2011;38:2617–26.
From the Department of Rehabilitation Medicine, University of Washington, Seattle, WA. Acknowledgment date: April 8, 2014. Acceptance date: April 11, 2014. The Manuscript submitted does not contain information about medical device(s)/drug(s). No funds were received in support of this work. No relevant financial activities outside the submitted work. Address correspondence and reprint requests to Marcia A. Ciol, PhD, 1959 NE Pacific St., UW Box 356490, Seattle WA; E-mail: [email protected] DOI: 10.1097/BRS.0000000000000385 Spine
www.spinejournal.com
1313
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited. SPINEJC1301POV_LR 1313
15/06/14 11:00 AM
JOURNAL CLUB
Point of View Marcia A. Ciol, PhD
T
his article brings up important issues in analysis of large databases. Although these databases provide information that is difficult to obtain otherwise, these data might have been collected for billing/administrative purposes (e.g., Medicare claims data), or were collected as part of complex surveys with a more general purpose (e.g., Medicare Current Beneficiaries Survey). The latter might offer more possibilities for studies, but it is still limited to whatever the survey originally asked. Therefore, the first rule to use a large database is to know it very well, including what data were collected and how. A researcher looking for a quick publication from an existent database will be frustrated and surprised by the time that it takes to understand the database well. An important issue not mentioned in the article is the analysis of data from weighted samples. The Kids Inpatient Database is an example of a weighted sample, and to produce
national estimates the researcher must use the sampling weights in the calculation.1 I agree with the authors that the P value is not the sole determinant of how the results of a study should be interpreted. In fact, it is sad to see how the P value is frequently misused and confused with type I error (the significance level).2 All studies, including large databases, are only as good as their design, data collection, and statistical analyses are, and they all have limitations. Researchers should make sure that they thoroughly understand the database to understand its potential use and limitations.
References
1. Ciol MA, Hoffman JM, Dudgeon BJ, et al. Understanding the use of weights in the analysis of data from multistage surveys. Arch Phys Med Rehabil 2006;87:299–303. 2. Hubbard R. The widespread misinterpretation of P values as error probabilities. J Appl Stat 2011;38:2617–26.
From the Department of Rehabilitation Medicine, University of Washington, Seattle, WA. Acknowledgment date: April 8, 2014. Acceptance date: April 11, 2014. The Manuscript submitted does not contain information about medical device(s)/drug(s). No funds were received in support of this work. No relevant financial activities outside the submitted work. Address correspondence and reprint requests to Marcia A. Ciol, PhD, 1959 NE Pacific St., UW Box 356490, Seattle WA; E-mail: [email protected] DOI: 10.1097/BRS.0000000000000385 Spine
www.spinejournal.com
1313
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited. SPINEJC1301POV_LR 1313
15/06/14 11:00 AM
