Abstract
Introduction: To support claims that RCTs can reduce health disparities (i.e., are translational), it is imperative that methodologies exist to evaluate the tenability of external validity in RCTs when probabilistic sampling of participants is not employed. Typically, attempts at establishing post hoc external validity are limited to a few comparisons across convenience variables, which must be available in both sample and population. A Type 2 diabetes RCT was used as an example of a method that uses a geographic information system to assess external validity in the absence of a priori probabilistic community-wide diabetes risk sampling strategy. Methods: A geographic information system, 2009-2013 county death certificate records, and 2013-2014 electronic medical records were used to identify community-wide diabetes prevalence. Color-coded diabetes density maps provided visual representation of these densities. Chi-square goodness of fit statistic/analysis tested the degree to which distribution of RCT participants varied across density classes compared to what would be expected, given simple random sampling of the county population. Analyses were conducted in 2016. Results: Diabetes prevalence areas as represented by death certificate and electronic medical records were distributed similarly. The simple random sample model was not a good fit for death certificate record (chi-square, 17.63; p=0.0001) and electronic medical record data (chi-square, 28.92; p<0.0001). Generally, RCT participants were oversampled in high-diabetes density areas. Conclusions: Location is a highly reliable "principal variable" associated with health disparities. It serves as a directly measurable proxy for high-risk underserved communities, thus offering an effective and practical approach for examining external validity of RCTs.
| Original language | English (US) |
|---|---|
| Journal | American Journal of Preventive Medicine |
| DOIs | |
| State | Accepted/In press - 2017 |
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ASJC Scopus subject areas
- Epidemiology
- Public Health, Environmental and Occupational Health
Cite this
Geographic Information Systems to Assess External Validity in Randomized Trials. / Savoca, Margaret R.; Ludwig, David; Jones, Stedman T.; Jason Clodfelter, K.; Sloop, Joseph B.; Bollhalter, Linda Y.; Bertoni, Alain G.
In: American Journal of Preventive Medicine, 2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Geographic Information Systems to Assess External Validity in Randomized Trials
AU - Savoca, Margaret R.
AU - Ludwig, David
AU - Jones, Stedman T.
AU - Jason Clodfelter, K.
AU - Sloop, Joseph B.
AU - Bollhalter, Linda Y.
AU - Bertoni, Alain G.
PY - 2017
Y1 - 2017
N2 - Introduction: To support claims that RCTs can reduce health disparities (i.e., are translational), it is imperative that methodologies exist to evaluate the tenability of external validity in RCTs when probabilistic sampling of participants is not employed. Typically, attempts at establishing post hoc external validity are limited to a few comparisons across convenience variables, which must be available in both sample and population. A Type 2 diabetes RCT was used as an example of a method that uses a geographic information system to assess external validity in the absence of a priori probabilistic community-wide diabetes risk sampling strategy. Methods: A geographic information system, 2009-2013 county death certificate records, and 2013-2014 electronic medical records were used to identify community-wide diabetes prevalence. Color-coded diabetes density maps provided visual representation of these densities. Chi-square goodness of fit statistic/analysis tested the degree to which distribution of RCT participants varied across density classes compared to what would be expected, given simple random sampling of the county population. Analyses were conducted in 2016. Results: Diabetes prevalence areas as represented by death certificate and electronic medical records were distributed similarly. The simple random sample model was not a good fit for death certificate record (chi-square, 17.63; p=0.0001) and electronic medical record data (chi-square, 28.92; p<0.0001). Generally, RCT participants were oversampled in high-diabetes density areas. Conclusions: Location is a highly reliable "principal variable" associated with health disparities. It serves as a directly measurable proxy for high-risk underserved communities, thus offering an effective and practical approach for examining external validity of RCTs.
AB - Introduction: To support claims that RCTs can reduce health disparities (i.e., are translational), it is imperative that methodologies exist to evaluate the tenability of external validity in RCTs when probabilistic sampling of participants is not employed. Typically, attempts at establishing post hoc external validity are limited to a few comparisons across convenience variables, which must be available in both sample and population. A Type 2 diabetes RCT was used as an example of a method that uses a geographic information system to assess external validity in the absence of a priori probabilistic community-wide diabetes risk sampling strategy. Methods: A geographic information system, 2009-2013 county death certificate records, and 2013-2014 electronic medical records were used to identify community-wide diabetes prevalence. Color-coded diabetes density maps provided visual representation of these densities. Chi-square goodness of fit statistic/analysis tested the degree to which distribution of RCT participants varied across density classes compared to what would be expected, given simple random sampling of the county population. Analyses were conducted in 2016. Results: Diabetes prevalence areas as represented by death certificate and electronic medical records were distributed similarly. The simple random sample model was not a good fit for death certificate record (chi-square, 17.63; p=0.0001) and electronic medical record data (chi-square, 28.92; p<0.0001). Generally, RCT participants were oversampled in high-diabetes density areas. Conclusions: Location is a highly reliable "principal variable" associated with health disparities. It serves as a directly measurable proxy for high-risk underserved communities, thus offering an effective and practical approach for examining external validity of RCTs.
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U2 - 10.1016/j.amepre.2017.01.001
DO - 10.1016/j.amepre.2017.01.001
M3 - Article
JO - American Journal of Preventive Medicine
JF - American Journal of Preventive Medicine
SN - 0749-3797
ER -