Biomaterial Database

The handling of missing data in molecular epidemiology studies. 2011

Manisha Desai, and Jessica Kubo, and Denise Esserman, and Mary Beth Terry

Quantitative Sciences Unit, Department of Medicine, Stanford University, Palo Alto, CA 94304, USA. manishad@stanford.edu

Molecular epidemiology studies face a missing data problem, as biospecimen or imaging data are often collected on only a proportion of subjects eligible for study. We investigated all molecular epidemiology studies published as Research Articles, Short Communications, or Null Results in Brief in Cancer Epidemiology, Biomarkers & Prevention from January 1, 2009, to March 31, 2010, to characterize the extent that missing data were present and to elucidate how the issue was addressed. Of 278 molecular epidemiology studies assessed, most (95%) had missing data on a key variable (66%) and/or used availability of data (often, but not always the biomarker data) as inclusion criterion for study entry (45%). Despite this, only 10% compared subjects included in the analysis with those excluded from the analysis and 88% with missing data conducted a complete-case analysis, a method known to yield biased and inefficient estimates when the data are not missing completely at random. Our findings provide evidence that missing data methods are underutilized in molecular epidemiology studies, which may deleteriously affect the interpretation of results. We provide practical guidelines for the analysis and interpretation of molecular epidemiology studies with missing data.

UI	MeSH Term	Description	Entries
D008297	Male		Males
D012107	Research Design	A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly.	Experimental Design,Data Adjustment,Data Reporting,Design, Experimental,Designs, Experimental,Error Sources,Experimental Designs,Matched Groups,Methodology, Research,Problem Formulation,Research Methodology,Research Proposal,Research Strategy,Research Technics,Research Techniques,Scoring Methods,Adjustment, Data,Adjustments, Data,Data Adjustments,Design, Research,Designs, Research,Error Source,Formulation, Problem,Formulations, Problem,Group, Matched,Groups, Matched,Matched Group,Method, Scoring,Methods, Scoring,Problem Formulations,Proposal, Research,Proposals, Research,Reporting, Data,Research Designs,Research Proposals,Research Strategies,Research Technic,Research Technique,Scoring Method,Source, Error,Sources, Error,Strategies, Research,Strategy, Research,Technic, Research,Technics, Research,Technique, Research,Techniques, Research
D003625	Data Collection	Systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data.	Data Collection Methods,Dual Data Collection,Collection Method, Data,Collection Methods, Data,Collection, Data,Collection, Dual Data,Data Collection Method,Method, Data Collection,Methods, Data Collection
D003627	Data Interpretation, Statistical	Application of statistical procedures to analyze specific observed or assumed facts from a particular study.	Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D004812	Epidemiologic Methods	Research techniques that focus on study designs and data gathering methods in human and animal populations.	Epidemiologic Method,Epidemiological Methods,Methods, Epidemiologic,Epidemiological Method,Method, Epidemiologic,Method, Epidemiological,Methods, Epidemiological
D005260	Female		Females
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D015982	Bias	Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions.	Aggregation Bias,Bias, Aggregation,Bias, Ecological,Bias, Statistical,Bias, Systematic,Ecological Bias,Outcome Measurement Errors,Statistical Bias,Systematic Bias,Bias, Epidemiologic,Biases,Biases, Ecological,Biases, Statistical,Ecological Biases,Ecological Fallacies,Ecological Fallacy,Epidemiologic Biases,Experimental Bias,Fallacies, Ecological,Fallacy, Ecological,Scientific Bias,Statistical Biases,Truncation Bias,Truncation Biases,Bias, Experimental,Bias, Scientific,Bias, Truncation,Biase, Epidemiologic,Biases, Epidemiologic,Biases, Truncation,Epidemiologic Biase,Error, Outcome Measurement,Errors, Outcome Measurement,Outcome Measurement Error
D017720	Molecular Epidemiology	The application of molecular biology to the answering of epidemiological questions. The examination of patterns of changes in DNA to implicate particular carcinogens and the use of molecular markers to predict which individuals are at highest risk for a disease are common examples.	Epidemiology, Molecular,Genetic Epidemiology,Epidemiologies, Genetic,Epidemiologies, Molecular,Epidemiology, Genetic,Genetic Epidemiologies,Molecular Epidemiologies