期刊
MEDICAL DECISION MAKING
卷 32, 期 1, 页码 83-92出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/0272989X11416512
关键词
randomized trial methodology; risk factor evaluation; population-based studies; scale development
资金
- Eunice Shriver National Institute of Child Health and Human Development [R01 HD047557]
Background. Use of instrumental variables is gaining popularity as a method of controlling for confounding by indication in observational studies of treatments. Objectives. To illustrate how unmeasured instrument-level treatment substitution can distort effect size estimates using as an example an instrumental variable analysis of phototherapy for neonatal jaundice. Design. Retrospective cohort study. Setting. Northern California Kaiser Permanente Hospitals. Patients. The authors studied 20,731 newborns >= 2000 g and >= 35 weeks' gestation born 1995-2004 with a qualifying total serum bilirubin (TSB) level within 3 mg/dL of the 2004 American Academy of Pediatrics (AAP) phototherapy threshold who did not have a positive direct antiglobulin test. Measurements. The intervention was inpatient phototherapy within 8 hours of the qualifying TSB. The outcome was a TSB level exceeding the AAP exchange transfusion threshold < 48 hours from the qualifying TSB. The instrumental variable was a measure of the frequency of phototherapy use at the newborn's birth hospital. The unmeasured substituted treatment was supplementation with infant formula, assessed by chart review in a sample from the same cohort. Results. In total, 128 infants (0.62%) exceeded the exchange transfusion threshold. Logistic and propensity analyses yielded crude odds ratios of similar to 0.5 for phototherapy efficacy, decreasing to similar to 0.2 with control for confounding by indication. Instrumental variable analyses suggested much greater phototherapy efficacy (e.g., odds ratios of 0.02-0.05). However, chart reviews revealed greater use of infant formula (which also lowers bilirubin levels) in hospitals that used more phototherapy (r = 0.56; P = 0.02), an association not present at the individual level (r = 0.13). Conclusions. Instrumental variable analyses may provide biased estimates of treatment efficacy if there are cointerventions or confounders associated with treatment at the level of the instrument, although even when these associations may not exist in individuals.
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