Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial.

Importance: The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population. Objective: To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults. Design, Setting, and Participants: This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023. Interventions: Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years. Main Outcomes and Measures: For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality. Results: The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%). Conclusions and Relevance: Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.

Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population.

Most work on extending (generalizing or transporting) inferences from a randomized trial to a target population has focused on estimating average treatment effects (i.e., averaged over the target population's covariate distribution). Yet, in the presence of strong effect modification by baseline covariates, the average treatment effect in the target population may be less relevant for guiding treatment decisions. Instead, the conditional average treatment effect (CATE) as a function of key effect modifiers may be a more useful estimand. Recent work on estimating target population CATEs using baseline covariate, treatment, and outcome data from the trial and covariate data from the target population only allows for the examination of heterogeneity over distinct subgroups. We describe flexible pseudo-outcome regression modeling methods for estimating target population CATEs conditional on discrete or continuous baseline covariates when the trial is embedded in a sample from the target population (i.e., in nested trial designs). We construct pointwise confidence intervals for the CATE at a specific value of the effect modifiers and uniform confidence bands for the CATE function. Last, we illustrate the methods using data from the Coronary Artery Surgery Study (CASS) to estimate CATEs given history of myocardial infarction and baseline ejection fraction value in the target population of all trial-eligible patients with stable ischemic heart disease.

Estimating subgroup effects in generalizability and transportability analyses.

Methods for extending - generalizing or transporting - inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is sufficient for rendering the randomized and non-randomized groups exchangeable. Yet, decision-makers are often interested in examining treatment effects in subgroups of the target population defined in terms of only a few discrete covariates. Here, we propose methods for estimating subgroup-specific potential outcome means and average treatment effects in generalizability and transportability analyses, using outcome model-based (g-formula), weighting, and augmented weighting estimators. We consider estimating subgroup-specific average treatment effects in the target population and its non-randomized subset, and provide methods that are appropriate both for nested and non-nested trial designs. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study to compare the effect of surgery plus medical therapy versus medical therapy alone for chronic coronary artery disease in subgroups defined by history of myocardial infarction.

Assessing Heterogeneity of Treatment Effects in Observational Studies.

Here we describe methods for assessing heterogeneity of treatment effects over prespecified subgroups in observational studies, using outcome-model-based (g-formula), inverse probability weighting, doubly robust, and matching estimators of subgroup-specific potential outcome means, conditional average treatment effects, and measures of heterogeneity of treatment effects. We compare the finite-sample performance of different estimators in simulation studies where we vary the total sample size, the relative frequency of each subgroup, the magnitude of treatment effect in each subgroup, and the distribution of baseline covariates, for both continuous and binary outcomes. We find that the estimators' bias and variance vary substantially in finite samples, even when there is no unobserved confounding and no model misspecification. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study (August 1975-December 1996) to compare the effect of surgery plus medical therapy with that of medical therapy alone for chronic coronary artery disease in subgroups defined by previous myocardial infarction or left ventricular ejection fraction.

Extending inferences from a randomized trial to a new target population.

When treatment effect modifiers influence the decision to participate in a randomized trial, the average treatment effect in the population represented by the randomized individuals will differ from the effect in other populations. In this tutorial, we consider methods for extending causal inferences about time-fixed treatments from a trial to a new target population of nonparticipants, using data from a completed randomized trial and baseline covariate data from a sample from the target population. We examine methods based on modeling the expectation of the outcome, the probability of participation, or both (doubly robust). We compare the methods in a simulation study and show how they can be implemented in software. We apply the methods to a randomized trial nested within a cohort of trial-eligible patients to compare coronary artery surgery plus medical therapy versus medical therapy alone for patients with chronic coronary artery disease. We conclude by discussing issues that arise when using the methods in applied analyses.

Ultrasound Adds No Benefit to Clinical Exam for Predicting Dehydration in Children With Acute Diarrhea in a Resource-Limited Setting.

OBJECTIVES: Diarrhea is one of the most common and deadly conditions affecting children, causing over 525,000 deaths annually, largely in resource-limited settings. Appropriate treatment depends on accurate determination of dehydration status. This study evaluated the accuracy of a new model using clinical and ultrasound measurements for predicting dehydration status in children with acute diarrhea. METHODS: The Dehydration: Assessing Kids Accurately (DHAKA) study was a prospective cohort study of children under 5 years of age with acute diarrhea presenting to the International Centre for Diarrhoeal Disease Research in Dhaka, Bangladesh. Clinical signs and sonographic measurements of the aorta-to-inferior vena cava ratio were recorded. Percent weight change with rehydration was used to classify dehydration severity. Logistic regression was used to create a combined DHAKA-US model based on clinical and sonographic measurements. Area under the curve and calibration slope were used to assess the model's accuracy and compare it to the original DHAKA score model. RESULTS: A total of 850 children were enrolled, with 736 included in the final analysis. The combined DHAKA-US model showed equivalent discrimination with the original DHAKA score, with an area under the curve of 0.79 for both models for severe dehydration (95% confidence interval, 0.74-0.84), as well as similar classification (48% versus 50% correctly classified) and calibration (calibration slopes of 0.900 versus 0.904 for presence of any dehydration). CONCLUSION: Adding sonographic measurements to the DHAKA score had no effect on discrimination, classification, or calibration when compared to the original DHAKA score. Clinical signs alone may be the most important predictors of dehydration status in children with diarrhea in limited resource settings.

Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals.

We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite-sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial-eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.

Calcium/calmodulin regulates ubiquitination of the ubiquitin-specific protease TRE17/USP6.

The TRE17 (USP6/TRE-2) oncogene induces tumorigenesis in both humans and mice. However, little is known regarding its regulation or mechanism of transformation. TRE17 encodes a TBC (Tre-2/Bub2/Cdc16)/Rab GTPase-activating protein homology domain at its N terminus and a ubiquitin-specific protease at its C terminus. In the current study, we identified the ubiquitous calcium (Ca2+)-binding protein calmodulin (CaM) as a novel binding partner for TRE17. CaM bound directly to TRE17 in a Ca2+-dependent manner both in vitro and in vivo. The CaM-binding site was mapped to two hydrophobic motifs near the C terminus of the TBC domain. Point mutations within these motifs significantly reduced the interaction of TRE17 with CaM. We further found that TRE17 is monoubiquitinated and promotes its own deubiquitination in vivo. CaM binding-deficient mutants of TRE17 exhibited significantly reduced monoubiquitination, suggesting that binding of Ca2+/CaM to TRE17 promotes this modification. Consistent with this notion, treatment of cells with the CaM inhibitor W7 reduced levels of TRE17 monoubiquitination. Interestingly, the calcium ionophore A23187 induced accumulation of a polyubiquitinated TRE17 species. The effect of A23187 was attenuated in CaM binding-deficient mutants of TRE17. Taken together, these studies indicate a role for Ca2+/CaM in regulating ubiquitination through direct interaction with TRE17.

Hypoxia-inducible factor regulates alphavbeta3 integrin cell surface expression.

Hypoxia-inducible factor (HIF)-deficient placentas exhibit a number of defects, including changes in cell fate adoption, lack of fetal angiogenesis, hypocellularity, and poor invasion into maternal tissue. HIF is a heterodimeric transcription factor consisting of alpha and beta aryl hydrocarbon receptor nuclear translocator or ARNT) subunits. We used undifferentiated trophoblast stem (TS) cells to characterize HIF-dependent adhesion, migration, and invasion. Arnt(-/-) and Hifalpha(-/-) TS cells exhibit reduced adhesion and migration toward vitronectin compared with wild-type cells. Furthermore, this defect is associated with decreased cell surface expression of integrin alphavbeta3 and significantly decreased expression of this integrin in focal adhesions. Because of the importance of adhesion and migration in tumor progression (in addition to placental development), we examined the affect of culturing B16F0 melanoma cells in 1.5% oxygen (O(2)). Culturing B16F0 melanoma cells at 1.5% O(2) resulted in increased alphavbeta3 integrin surface expression and increased adhesion to and migration toward vitronectin. Together, these data suggest that HIF and O(2) tension influence placental invasion and tumor migration by increasing cell surface expression of alphavbeta3 integrin.

The TBC (Tre-2/Bub2/Cdc16) domain protein TRE17 regulates plasma membrane-endosomal trafficking through activation of Arf6.

TBC (Tre-2/Bub2/Cdc16) domains are predicted to encode GTPase-activating proteins (GAPs) for Rab family G proteins. While approximately 50 TBC proteins are predicted to exist in humans, little is known about their substrate specificity. Here we show that TRE17 (also called Tre-2 and USP6), a founding member of the TBC family, targets the Arf family GTPase Arf6, which regulates plasma membrane-endosome trafficking. Surprisingly, TRE17 does not function as a GAP for Arf6 but rather promotes its activation in vivo. TRE17 associates directly with Arf6 in its GDP- but not GTP-bound state. Mapping experiments pinpoint the site of interaction to the TBC domain of TRE17. Forced expression of TRE17 promotes the localization of Arf6 to the plasma membrane, leading to Arf6 activation, presumably due to facilitated access to membrane-associated guanine nucleotide exchange factors (GEFs). Furthermore, TRE17 cooperates with Arf6 GEFs to induce GTP loading of Arf6 in vivo. Finally, short interfering RNA-mediated loss of TRE17 leads to attenuated Arf6 activation. These studies identify TRE17 as a novel regulator of the Arf6-regulated plasma membrane recycling system and reveal an unexpected function for TBC domains.