Dylan S. Small

Assessing cause-and-effect relationships is one of the most important tasks in science. The best device for this task is the randomized controlled trial. Randomization is often either infeasible or...

Observational studies are valuable tools for inferring causal effects in the absence of controlled experiments. However, these studies may be biased due to the presence of some relevant, unmeasured set of covariates. One way to mitigate this concern is to identify hypotheses likely to be more resilient to hidden biases by splitting the data into a planning sample for designing the study and an analysis sample for making inferences. We devise a powerful and flexible method for selecting hypotheses in the planning sample when an unknown number of outcomes are affected by the treatment, allowing researchers to gain the benefits of exploratory analysis and still conduct powerful inference under concerns of unmeasured confounding. We investigate the theoretical properties of our method and conduct extensive simulations that demonstrate pronounced benefits, especially at higher levels of allowance for unmeasured confounding. Finally, we demonstrate our method in an observational study of the multi-dimensional impacts of a devastating flood in Bangladesh.

The Lung Health Study was a randomized clinical trial of smoking cessation that took place between 1986 and 1988. Special intervention participants received the smoking intervention program and were compared with usual care participants. Vital status was followed up to 32.5 years. Previous work sought to assess the effect of the cessation program on all-cause and cause-specific mortality at 14.5 years. Our objective was to do so at 32.5 years. We analyzed data from 5279 participants from the United States from the Lung Health Study. The three arms were smoking intervention plus bronchodilator, smoking intervention plus placebo, or no intervention. We compared all-cause and cause-specific mortality at 32.5 years between the combined special intervention groups and usual care group. The hazard ratio for the usual care group compared with the special intervention group was 1.05 (95% CI, 0.97-1.18) at 32.5 years. The cause-specific hazard ratio for the usual care group compared with the special intervention group for death due to respiratory disease that was not lung cancer was 1.21 (95% CI, 1.04-1.42) at 32.5 years, indicating that the smoking cessation program had a protective effect against death due to non-lung cancer respiratory disease, even after a long period.

We investigated the expression and distribution of 5 cytoadhesion receptors for the Plasmodium falciparum erythrocyte membrane protein 1 in 12 regions of post-mortem brains of 50 Malawian children, that is, 27 with the clinical and pathological diagnosis of cerebral malaria (CM) and 23 with a non-malarial cause of death. We quantified the expression of each receptor by microvascular endothelium and the colocalization of receptor-expressing microvessels with sequestered infected red blood cells (iRBC) and calculated a receptor-independent sequestration ratio. There were differences in the level of expression and regional distribution of the five receptors: ICAM-1 was the most widely expressed receptor, followed by CD36, VCAM-1, E-selectin, and thrombospondin. Receptor-expressing microvessels were most numerous in the frontal lobe and least numerous in the brainstem and cerebellum. Colocalization of receptor-expressing endothelial cells with iRBC was present in all brain regions; it was highest for ICAM-1 and CD36 and greatest in the frontal lobe. The sequestration ratios were close to 100% for all receptors across all brain regions and were similar in cerebral and extracerebral microvessels. Receptor expression and colocalization ratios were greater in the brain than in the lung, heart, liver, spleen, and subcutaneous tissue. These differences in cerebral endothelial expression of cytoadhesion receptors and their preferential regional distribution may underpin differences in iRBC sequestration and lesion development in CM. Moreover, greater expression of these receptors in the brain vs peripheral organs may explain a comparatively greater degree of iRBC sequestration in the brain.

The case$^2$ study, also referred to as the case–case study design, is a valuable approach for conducting inference for treatment effects. Unlike traditional case-control studies, the case$^2$ design compares treatment in cases of concern (the first type of case) to other cases (the second type of case). One of the quantities of interest is the attributable effect for the first type of case—that is, the number of the first type of case that would not have occurred had the treatment been withheld from all units. In some case$^2$ studies, a key quantity of interest is the attributable effect for the first type of case. Two key assumptions that are usually made for making inferences about this attributable effect in case$^2$ studies are (1) treatment does not cause the second type of case, and (2) the treatment does not alter an individual’s case type. However, these assumptions are not realistic in many real-data applications. In this article, we present a sensitivity analysis framework to scrutinize the impact of deviations from these assumptions on inferences for the attributable effect. We also include sensitivity analyses related to the assumption of unmeasured confounding, recognizing the potential bias introduced by unobserved covariates. The proposed methodology is exemplified through an investigation into whether having violent behavior in the last year of life increases suicide risk using the 1993 National Mortality Followback Survey dataset.

The attributable fraction among the exposed (AFe) is the proportion of disease cases among the exposed that could be avoided by eliminating the exposure. In this article, we propose a new approach to reduce sensitivity to hidden bias for conducting statistical inference on the AFe by leveraging case description information such as subtype of cancer. The proposed method is examined through an asymptotic tool, design sensitivity, simulation studies, and case studies of alcohol consumption and the risk of postmenopausal invasive breast cancer utilizing information on the subtype of cancer using data from the Women’s Health Initiative Observational Study allowing the possibility that leveraging case definition information may introduce selection bias through an additional sensitivity parameter.

BACKGROUND: The BE ACTIVE trial (Behavioral Economic Approaches to Increase Physical Activity Among Patients with Elevated Risk for Cardiovascular Disease) documented the effectiveness, compared with an attention control arm that received daily text messages, of gamification, financial incentives, or gamification+financial incentives to increase steps/day. Increases in daily step count are associated with longer life expectancy, but understanding the cost-effectiveness of these interventions is essential for payers and other stakeholders seeking to implement findings. METHODS: We built a probabilistic Markov model to compare intervention costs with lifetime estimates of life-years and quality-adjusted life-years for 2 sets of comparisons: (1) each behavioral intervention versus attention control, and (2) each trial arm, including attention control, versus no intervention. Since the durability of changes in steps/day post-intervention is unknown, we modeled optimistic, intermediate, and pessimistic scenarios. RESULTS: Over the 12-month intervention, per-participant cost to deliver attention control was $878, gamification $938, financial incentives $1534, and gamification+financial incentives $1712. Compared with attention control, gamification’s cost-effectiveness ranged from $261 (95% CI, 259–263) per life-year gained if mean steps/day during the last 18 weeks of follow-up are maintained (optimistic), to $30 550 (95% CI, 30 503–30 597) per life-year if steps/day continue to decline at the rate observed during the full 26-week follow-up (pessimistic). Gamification+financial incentives cost <$50 000/life-year only under the optimistic and intermediate scenarios. Financial incentives was dominated by gamification and gamification+financial incentives. When all 4 trial arms, including attention control, were compared with no intervention, gamification again cost <$50 000/life-year across all durability scenarios. CONCLUSIONS: Across a range of scenarios about the durability of increases in steps/day post-intervention, gamification consistently cost <$50 000 per life-year gained, the threshold for high value interventions set by American College of Cardiology/American Heart Association guidelines. Gamification+financial incentives was high-value except in the pessimistic scenario. Financial incentives was dominated. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03911141.

The cardiovascular risks of exogenous testosterone have been a subject of controversy. In this study, our objective was to examine the association between testosterone (versus glaucoma treatments as an active comparator, with no assumed effect) and the new onset of cardiovascular, cerebrovascular and thromboembolic adverse events, in a US commercial insurance database. Data were analyzed by three complementary designs: inverse propensity score weighting (IPSW), calendar time instrumental variable and instrumented difference-in-differences. Results of these analyses suggest that there is no difference between testosterone and glaucoma treatments regarding the risk of the composite primary endpoint of acute myocardial infarction, ischemic stroke and sudden cardiac arrest/ventricular arrhythmia. In contrast, IPSW analysis identified a negative association between testosterone and the secondary endpoint of venous thromboembolism. However, this association was attenuated towards the null in the calendar time instrumental variable and difference-in-differences analysis, which suggests that there might be unmeasured confounding in the IPSW analysis. Because there is no uniquely suitable method that offers a universally optimal solution for evaluating causal relationships between exposures and outcomes from observational data, using multiple state-of-the-art methods to answer the question of interest can help in assessing the robustness of findings to various forms of unmeasured confounding, thereby aiding in causal inference.

Objectives. To test low-cost, scalable interventions designed to encourage seat belt use (primary outcome) and discourage handheld phone use while driving. Methods. A randomized controlled trial assigned 1139 consenting General Motors‒connected vehicle customers in the United States to 1 of 4 groups for a 10-week intervention: (1) control, (2) behavioral engagement, (3) behavioral engagement plus raffle, and (4) behavioral engagement plus shared pot. Behavioral engagement involved education, personalized tips, a “wish outcome obstacle plan” exercise, and weekly feedback about buckling and handheld-free streaks. Participants in the behavioral engagement plus raffle group also earned a chance at a $125 prize each week they had a buckling or handheld-free streak. Those in the behavioral engagement plus shared pot group earned an equal share of this prize for each streak. The intervention was delivered virtually in spring 2023. Results. Participants in the behavioral engagement plus shared pot group had a higher buckling rate (91.3%) than those in the behavioral engagement plus raffle (89.5%), behavioral engagement (89.4%), or control (88.3%) groups—differences that remained significant at follow-up. Handheld phone use did not differ significantly. Conclusions. A behavioral intervention with a shared pot incentive could be delivered at scale to reduce injuries and deaths associated with vehicular crashes. Trial Registration. ClinicalTrials.gov identifier: NCT05469477. ( Am J Public Health. 2025;115(5):758–768. https://doi.org/10.2105/AJPH.2024.307980 )