Bias in trials comparing paired continuous tests can cause researchers to choose the wrong screening modality

<p>Abstract</p> <p>Background</p> <p>To compare the diagnostic accuracy of two continuous screening tests, a common approach is to test the difference between the areas under the receiver operating characteristic (ROC) curves. After study participants are screened with both screening tests, the disease status is determined as accurately as possible, either by an invasive, sensitive and specific secondary test, or by a less invasive, but less sensitive approach. For most participants, disease status is approximated through the less sensitive approach. The invasive test must be limited to the fraction of the participants whose results on either or both screening tests exceed a threshold of suspicion, or who develop signs and symptoms of the disease after the initial screening tests.</p> <p>The limitations of this study design lead to a bias in the ROC curves we call <it>paired screening trial bias</it>. This bias reflects the synergistic effects of inappropriate reference standard bias, differential verification bias, and partial verification bias. The absence of a gold reference standard leads to inappropriate reference standard bias. When different reference standards are used to ascertain disease status, it creates differential verification bias. When only suspicious screening test scores trigger a sensitive and specific secondary test, the result is a form of partial verification bias.</p> <p>Methods</p> <p>For paired screening tests with bivariate normally distributed scores, we give formulae and programs to quantify the effect of <it>paired screening trial bias </it>on a paired comparison of area under the curves. We fix the prevalence of disease, and the chance a diseased subject manifests signs and symptoms. We derive the formulas for true sensitivity and specificity, and those for the sensitivity and specificity observed by the study investigator.</p> <p>Results</p> <p>The observed area under the ROC curves is quite different from the true area under the ROC curves. The typical direction of the bias is a strong inflation in sensitivity, paired with a concomitant slight deflation of specificity.</p> <p>Conclusion</p> <p>In paired trials of screening tests, when area under the ROC curve is used as the metric, bias may lead researchers to make the wrong decision as to which screening test is better.</p

Estimates of sensitivity and specificity can be biased when reporting the results of the second test in a screening trial conducted in series

Abstract Background Cancer screening reduces cancer mortality when early detection allows successful treatment of otherwise fatal disease. There are a variety of trial designs used to find the best screening test. In a series screening trial design, the decision to conduct the second test is based on the results of the first test. Thus, the estimates of diagnostic accuracy for the second test are conditional, and may differ from unconditional estimates. The problem is further complicated when some cases are misclassified as non-cases due to incomplete disease status ascertainment. Methods For a series design, we assume that the second screening test is conducted only if the first test had negative results. We derive formulae for the conditional sensitivity and specificity of the second test in the presence of differential verification bias. For comparison, we also derive formulae for the sensitivity and specificity for a single test design, both with and without differential verification bias. Results Both the series design and differential verification bias have strong effects on estimates of sensitivity and specificity. In both the single test and series designs, differential verification bias inflates estimates of sensitivity and specificity. In general, for the series design, the inflation is smaller than that observed for a single test design. The degree of bias depends on disease prevalence, the proportion of misclassified cases, and on the correlation between the test results for cases. As disease prevalence increases, the observed conditional sensitivity is unaffected. However, there is an increasing upward bias in observed conditional specificity. As the proportion of correctly classified cases increases, the upward bias in observed conditional sensitivity and specificity decreases. As the agreement between the two screening tests becomes stronger, the upward bias in observed conditional sensitivity decreases, while the specificity bias increases. Conclusions In a series design, estimates of sensitivity and specificity for the second test are conditional estimates. These estimates must always be described in context of the design of the trial, and the study population, to prevent misleading comparisons. In addition, these estimates may be biased by incomplete disease status ascertainment.</p

Infant Feeding Practices In a Diverse Group of Women: The Healthy Start Study

Background: To describe infant feeding practices among a diverse group of mother-offspring pairs and identify factors associated with adherence to the American Academy of Pediatrics (AAP) recommendations. Methods: Data were analyzed from 835 mother-offspring dyads in The Healthy Start Study, an ongoing longitudinal prebirth cohort in Denver, Colorado. Maternal report of infant feeding practices was obtained at 4 to 6 months and 18 to 24 months postnatally. Practices were classified according to the following AAP recommendations: exclusive breastfeeding for first 6 months, continued breastfeeding through 12 months, and introduction of solid foods around 6 months of age. Participants who met all 3 recommendations were categorized as “adherent.” All others were categorized as “not adherent.” Results: About 77% of dyads did not adhere fully to the AAP recommendations. Women who worked ⩾35 hours/week or had a higher prepregnancy body mass index were more likely to be nonadherent. Women who were older, college educated, or had offspring with greater weight for gestational age at birth were less likely to be nonadherent. Conclusions: Most of the women in a large contemporary cohort are not adhering to AAP infant feeding recommendations. Our results highlight the specific subgroups of women who may need additional support to optimize infant feeding practices

GLIMMPSE: Online Power Computation for Linear Models with and without a Baseline Covariate

GLIMMPSE is a free, web-based software tool that calculates power and sample size for the general linear multivariate model with Gaussian errors (http://glimmpse.SampleSizeShop.org/). GLIMMPSE provides a user-friendly interface for the computation of power and sample size. We consider models with fixed predictors, and models with fixed predictors and a single Gaussian covariate. Validation experiments demonstrate that GLIMMPSE matches the accuracy of previously published results, and performs well against simulations. We provide several online tutorials based on research in head and neck cancer. The tutorials demonstrate the use of GLIMMPSE to calculate power and sample size

Prenatal Vitamin D Intake, Cord Blood 25-Hydroxyvitamin D, and Offspring Body Composition: The Healthy Start Study

Vitamin D deficiency in pregnancy may be associated with increased offspring adiposity, but evidence from human studies is inconclusive. We examined associations between prenatal vitamin D intake, 25-hydroxyvitamin D (25(OH)D) in cord blood, and offspring size and body composition at birth and 5 months. Participants included 605 mother-offspring dyads from the Healthy Start study, an ongoing, pre-birth prospective cohort study in Denver, Colorado, USA. Prenatal vitamin D intake was assessed with diet recalls and questionnaires, and offspring body composition was measured via air displacement plethysmography at birth and 5 months. General linear univariate models were used for analysis, adjusting for maternal age, race/ethnicity, pre-pregnancy body mass index (BMI), offspring sex, and gestational age at birth. Non-Hispanic white race, lower pre-pregnancy BMI, higher prenatal vitamin D intake, and summer births were associated with higher cord blood 25(OH)D. Higher 25(OH)D was associated with lower birthweight (β = –6.22, p = 0.02), but as maternal BMI increased, this association became increasingly positive in direction and magnitude (β = 1.05, p = 0.04). Higher 25(OH)D was also associated with lower neonatal adiposity (β = –0.02, p &lt; 0.05) but not after adjustment for maternal BMI (β = –0.01, p = 0.25). Cord blood 25(OH)D was not associated with offspring size or body composition at 5 months. Our data confirm the hypothesis that vitamin D exposure in early life is associated with neonatal body size and composition. Future research is needed to understand the implications of these associations as infants grow