141st APHA Annual Meeting

In This section

286941
Handling missing values in population-based cancer registries: Application to female breast cancer with missing hormonal status

Monday, November 4, 2013 : 3:30 PM - 3:50 PM

Tulay Koru-Sengul, MHS, PhD , Department of Epidemiology and Public Health, University of Miami Miller School of Medicine, Miami, FL
Stacey L. Tannenbaum, PhD, RD, LD/N , Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL
Feng Miao, MSc , University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, FL
David J. Lee, PhD , Department of Epidemiology & Public Health, University of Miami Miller School of Medicine, Miami, FL
Inferences from population-based cancer registries are used for studying cancer epidemiology, patterns of diagnosis, treatment and clinical outcomes by providing valuable sources of data for health services research. We collaborated with the Florida Cancer Data System (FCDS) Registry and the Florida Agency for Health Care Administration (AHCA). We linked these two databases in order to enhance the information provided by FCDS. The enriched dataset provided us the means to predict survival for female breast cancer patients.

However, often registries have missing patient information on important demographics, clinical and clinicopathological characteristics. Ignoring missing information might lead to inaccurate inferences resulting in under or over estimating the real effect. Therefore, proper handling of missing values in the cancer registries during data capturing, analysis and reporting need further investigation to improve the quality of results.

Advanced statistical methods for handling missing values are available and have been used in population-based surveys such as NHANES but uptake in these methods in cancer registries has been limited and in need of improvement. Depending on the missingness mechanism several ad-hoc methods (simple and hot-deck imputation) and advanced statistical model-based approaches (regression and multiple imputation) for handling and accounting for missing values in cancer registries can be introduced. When these methods are applied to the missing hormonal status for female breast cancer patients, survival inferences can be more accurate in the FCDS and AHCA-linked dataset ; this methodology can be a model for other cancer registries.

Learning Areas:
Biostatistics, economics
Chronic disease management and prevention
Epidemiology
Other professions or practice related to public health
Public health or related research

Learning Objectives:
Identify and compare different methods for handling missing values which can be applied to the population-based cancer registries. Demonstrate how different the conclusions will be by using several different ad-hoc and advanced statistical methods for handling missing values in the population-based cancer registries. Analyze survival disparities by different hormonal status for female breast cancer patients.

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am qualified to be an abstract author because I have been the principal and co-principal investigator of a multiple federally funded grants focusing on the epidemiology of cancer in a population-based sample in the state of Florida. I have provided the statistics for this and several other grants on this topic.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.