nassCDS.Rd
US data, for 1997-2002, from police-reported car crashes in which there is a harmful event (people or property), and from which at least one vehicle was towed. Data are restricted to front-seat occupants, include only a subset of the variables recorded, and are restricted in other ways also.
nassCDS
A data frame with 26217 observations on the following 15 variables.
dvcat
ordered factor with levels (estimated impact
speeds) 1-9km/h
, 10-24
, 25-39
, 40-54
,
55+
weight
Observation weights, albeit of uncertain accuracy, designed to account for varying sampling probabilities.
dead
factor with levels alive
dead
airbag
a factor with levels none
airbag
seatbelt
a factor with levels none
belted
frontal
a numeric vector; 0 = non-frontal, 1=frontal impact
sex
a factor with levels f
m
ageOFocc
age of occupant in years
yearacc
year of accident
yearVeh
Year of model of vehicle; a numeric vector
abcat
Did one or more (driver or passenger) airbag(s)
deploy? This factor has levels deploy
nodeploy
unavail
occRole
a factor with levels driver
pass
deploy
a numeric vector: 0 if an airbag was unavailable or did not deploy; 1 if one or more bags deployed.
injSeverity
a numeric vector; 0:none, 1:possible injury, 2:no incapacity, 3:incapacity, 4:killed; 5:unknown, 6:prior death
caseid
character, created by pasting together the populations sampling unit, the case number, and the vehicle number. Within each year, use this to uniquely identify the vehicle.
Data collection used a multi-stage probabilistic sampling scheme.
The observation weight, called national inflation factor
(national
) in the data from NASS, is the inverse
of an estimate of the selection probability. These data
include a subset of the variables from the NASS dataset. Variables
that are coded here as factors are coded as numeric values in that
dataset.
http://www.stat.colostate.edu/~meyer/airbags.htm\ ftp://ftp.nhtsa.dot.gov/nass/
See also\ http://www.maths.anu.edu.au/~johnm/datasets/airbags
Meyer, M.C. and Finney, T. (2005): Who wants airbags?. Chance 18:3-16.
Farmer, C.H. 2006. Another look at Meyer and Finney's ‘Who wants airbags?’. Chance 19:15-22.
Meyer, M.C. 2006. Commentary on "Another look at Meyer and Finney's ‘Who wants airbags?’. Chance 19:23-24.
For analyses based on the alternative FARS (Fatal Accident Recording System) data, and associated commentary, see:
Cummings, P; McKnight, B, 2010. Accounting for vehicle, crash, and occupant characteristics in traffic crash studies. Injury Prevention 16: 363-366. [The relatively definitive analyses in this paper use a matched cohort design,
Olson, CM; Cummings, P, Rivara, FP, 2006. Association of first- and second-generation air bags with front occupant death in car crashes: a matched cohort study. Am J Epidemiol 164:161-169. [The relatively definitive analyses in this paper use a matched cohort design, using data taken from the FARS (Fatal Accident Recording System) database.]
Braver, ER; Shardell, M; Teoh, ER, 2010. How have changes in air bag designs affected frontal crash mortality? Ann Epidemiol 20:499-510.
The web page http://www-fars.nhtsa.dot.gov/Main/index.aspx has a menu-based interface into the FARS (Fatality Analysis Recording System) data. The FARS database aims to include every accident in which there was at least one fatality.
#> airbag #> dead none airbag #> alive 5445245.90 6622690.98 #> dead 39676.02 25919.11#> , , seatbelt = none, dvcat = 1-9km/h #> #> airbag #> dead none airbag #> alive 33102.044 34635.862 #> dead 0.000 139.917 #> #> , , seatbelt = belted, dvcat = 1-9km/h #> #> airbag #> dead none airbag #> alive 225699.184 402638.618 #> dead 0.000 0.000 #> #> , , seatbelt = none, dvcat = 10-24 #> #> airbag #> dead none airbag #> alive 668347.763 534335.459 #> dead 1665.872 2616.616 #> #> , , seatbelt = belted, dvcat = 10-24 #> #> airbag #> dead none airbag #> alive 2669758.307 4259342.701 #> dead 903.015 829.261 #> #> , , seatbelt = none, dvcat = 25-39 #> #> airbag #> dead none airbag #> alive 479690.599 243298.297 #> dead 9543.724 2701.504 #> #> , , seatbelt = belted, dvcat = 25-39 #> #> airbag #> dead none airbag #> alive 1006202.599 925521.610 #> dead 5601.044 3092.792 #> #> , , seatbelt = none, dvcat = 40-54 #> #> airbag #> dead none airbag #> alive 126044.688 47781.653 #> dead 4766.922 3929.506 #> #> , , seatbelt = belted, dvcat = 40-54 #> #> airbag #> dead none airbag #> alive 169175.173 136114.972 #> dead 4704.878 3156.745 #> #> , , seatbelt = none, dvcat = 55+ #> #> airbag #> dead none airbag #> alive 34836.806 11824.117 #> dead 8090.136 4372.400 #> #> , , seatbelt = belted, dvcat = 55+ #> #> airbag #> dead none airbag #> alive 32388.734 27197.692 #> dead 4400.426 5080.368 #>tab <- xtabs(weight ~ dead + abcat, data=nassCDS, subset=dvcat=="25-39"&frontal==0)[, c(3,1,2)] round(tab[2, ]/apply(tab,2,sum)*100,2)#> unavail deploy nodeploy #> 2.36 2.10 0.58