Appropriate analysis of CIREN data: using NASS-CDS to reduce bias in estimation of injury risk factors in passenger vehicle crashes.
The Crash Injury Research Engineering Network (CIREN) database contains detailed medical and crash information on a large number of severely injured occupants in motor vehicle crashes. CIREN's major limitation for stand-alone analyses to explore injury risk factors is that control subjects without a given injury type must have another severe injury to be included in the database. This leads to bias toward the null in the estimation of risk associations. One method to cope with this limitation is to obtain information about occupants without a given injury type from the National Automotive Sampling System's Crashworthiness Data System (NASS-CDS), which is a probability sample of towaway crashes, containing similar crash information, but less medical detail. Combining CIREN and NASS-CDS in this manner takes advantage of the increased sample size when outcomes are available in both datasets; otherwise NASS-CDS can serve as a sample of controls to be combined with CIREN cases, possibly under a sensitivity analysis that includes and excludes NASS-CDS subjects whose status as a control is uncertain. Because CIREN is not a probability sample of crashes that meet its inclusion criteria, we develop a method to estimate the probability of selection for the CIREN cases using data from NASS-CDS. These estimated probabilities are then used to compute "pseudo-weights" for the CIREN cases. These pseudo-weights not only allow for reduced bias in the estimation of risk associations, they allow direct prevalence estimates to be made using medical outcome data available only in CIREN. We illustrate the use of these methods with both simulation studies and application to estimation of prevalence and predictors of AIS 3+ injury risk to head, thorax, and lower extremity regions, as well as prevalence and predictors of acetabular pelvic fractures. Results of these analyses demonstrate combining NASS and CIREN data can yield improvements in mean square error and nominal confidence interval coverage over analyses that use either the NASS-CDS or the CIREN sample alone.
Elliott
Michael R
MR
Department of Biostatistics, University of Michigan School of Public Health, 1420 Washington Heights, Ann Arbor, MI 48109, United States. mrelliot@umich.edu
Resler
Alexa
A
Flannagan
Carol A
CA
Rupp
Jonathan D
JD
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D001336:Automobiles / Q000706:statistics & numerical data*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D012307:Risk Factors; D015983:Selection Bias; D014481:United States / Q000453:epidemiology; D014947:Wounds and Injuries / Q000145:classification / Q000453:epidemiology / Q000209:etiology*; D055815:Young Adult