Structural Nested Mean Models Research Articles

Estimating spatially varying health effects of wildland fire smoke using mobile health data

Abstract Wildland fire smoke exposures are an increasing threat to public health, highlighting the need for studying the effects of protective behaviours on reducing health outcomes. Emerging smartphone applications provide unprecedented opportunities to deliver health risk communication messages to a large number of individuals in real-time and subsequently study the effectiveness, but also pose methodological challenges. Smoke Sense, a citizen science project, provides an interactive smartphone app platform for participants to engage with information about air quality, and ways to record their own health symptoms and actions taken to reduce smoke exposure. We propose a doubly robust estimator of the structural nested mean model that accounts for spatially and time-varying effects via a local estimating equation approach with geographical kernel weighting. Moreover, our analytical framework also handles informative missingness by inverse probability weighting of estimating functions. We evaluate the method using extensive simulation studies and apply it to Smoke Sense data to increase the knowledge base about the relationship between health preventive measures and health-related outcomes. Our results show that the protective behaviours’ effects vary over space and time and find that protective behaviours have more significant effects on reducing health symptoms in the Southwest than the Northwest region of the U.S.

The biological embedding of social adversity: How adolescent housing insecurity impacts inflammation over time

Background & PurposeAdolescent housing insecurity is a dynamic form of social adversity that impacts child health outcomes worldwide. However, the means by which adolescent housing insecurity may become biologically embedded to influence health outcomes over the life course remain unclear. Therefore, we aimed to utilize life course perspectives and advanced causal inference methods to evaluate the potential for inflammation to contribute to the biological embedding of adolescent housing insecurity. Materials and methodsUsing prospective data from the Great Smoky Mountains Study, we investigated the relationship between adolescent housing insecurity and whole-blood spot samples assayed for C-reactive protein (CRP). Adolescent housing insecurity was created based on annual measures of frequent residential moves, reduced standard of living, forced separation from the home, and foster care. Annual measures of CRP ranged from 0.001 mg/L to 13.6 mg/L (median = 0.427 mg/L) and were log10 transformed to account for positively skewed values. We used g-estimation of structural nested mean models to estimate a series of conditional average causal effects of adolescent housing insecurity on CRP levels from ages 11 to 16 years and interpreted the results within life course frameworks of accumulation, recency, and sensitive periods. Principal ResultsOf the 1,334 participants, 427 [44.3 %] were female. Based on the conditional average causal effect, one exposure to adolescent housing insecurity from ages 11 to 16 years led to a 6.4 % (95 % CI = 0.69 – 12.4) increase in later CRP levels. Exposure at 14 years of age led to a 27.9 % increase in CRP levels at age 15 (95 % CI = 6.5 – 53.5). Recent exposures to adolescent housing insecurity (<3 years) suggested stronger associations with CRP levels than distant exposures (>3 years), but limited statistical power prevented causal conclusions regarding recency effects at the risk of a Type II Error. Major ConclusionsThese findings highlight inflammation—as indicated by increased CRP levels—as one potential mechanism for the biological embedding of adolescent housing insecurity. The results also suggest that adolescent housing insecurity—particularly recent, repeated, and mid-adolescent exposures—may increase the risk of poor health outcomes and should be considered a key intervention target.

Effects of parental job loss on psychotropic drug use in children: Long-term effects, timing, and cumulative exposure

Intra-family crossover effects triggered by job losses have received growing attention across scientific disciplines, but existing research has reached discrepant conclusions concerning if, and if so how, parental job losses affect child mental health. Drawing on sociological models of stress and life course epidemiology, we ask if parental job losses have long-term effects on child mental health, and if these effects are conditional on the timing of, or the cumulative exposure to, job losses. We use intergenerationally linked Swedish register data combined with entropy balance and structural nested mean models for the analyses. The data allow us to track 400,000 children over 14 years and thereby test different life-course models of cross-over effects. We identify involuntary job losses using information on workplace closures, thus reducing the risk of confounding. Results show that paternal but not maternal job loss significantly increases the risk of psychotropic drug use among children, that the average effects are modest in size (less than 4% in relative terms), that they may persist for up to five years, and that they are driven by children aged 6–10 years. Moreover, cumulative exposure to multiple job losses are more harmful than zero or one job loss.

Estimating curvilinear time-varying treatment effects: Combining g-estimation of structural nested mean models with time-varying effect models for longitudinal causal inference.

Longitudinal designs can fortify causal inquiries of a focal predictor (i.e., treatment) on an outcome. But valid causal inferences are complicated by causal feedback between confounders and treatment over time. G-estimation of a structural nested mean model (SNMM) is designed to handle the complexities beset by measured time-varying or treatment-dependent confounding in longitudinal data. But valid inference requires correctly specifying the functional form of the SNMM, such as how the effects stay constant or change over time. In this article, we develop a g-estimation strategy for linear structural nested mean models whose causal parameters adopt the form of time-varying coefficient functions. These time-varying coefficient functions are smooth semiparametric functions of time that permit probing how the treatment effects may change curvilinearly. Further effect modification by time-invariant and time-varying covariates can be readily postulated in the SNMM to test fine-grained effect heterogeneity. We then describe a g-estimation strategy for estimating such an SNMM. We utilize the established time-varying effect model (TVEM) approach from the prevention and psychotherapy research literature for modeling flexible changes in covariate-outcome associations over time. Moreover, we exploit a known benefit of g-estimation over routine regression methods: its double robustness conferring protection against biases induced by certain forms of model misspecification. We encourage psychology researchers seeking correct causal conclusions from longitudinal data to use an SNMM with time-varying coefficient functions to assess curvilinear causal effects over time, and to use g-estimation with TVEM to resolve measured treatment-dependent confounding. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Decomposing the Black-White Racial Disparity in Severe Maternal Morbidity Risk: The Role of Hypertensive Disorders of Pregnancy.

To our knowledge, no studies have explicitly studied the role of hypertensive disorders of pregnancy (HDP) in racial disparities in severe maternal morbidity (SMM). Using causal mediation models, we estimated the proportion of the non-Hispanic (NH) Black-White racial disparity in risk of SMM that is explained through the pathway of HDP. We linked 2006-2019 Georgia hospital discharge records with vital statistics birth and fetal death records for NH Black and NH White birthing persons. We used G-estimation of a structural nested mean model to decompose the absolute racial disparity in the incidence of SMM into pathways operating through HDP. NH Black birthing people experienced an excess 56 SMM events (95% confidence interval [CI] = 52, 59) per 10,000 delivery hospitalizations compared with NH White birthing people. If counterfactual disparity measure estimation assumptions hold, the estimated absolute disparity remaining after blocking the causal pathways through HDP was 41 SMM events per 10,000 deliveries (95% CI = 38, 44), suggesting that 26% (95% CI = 12, 40) of the absolute racial disparity would be eliminated if there was no contribution of HDP to SMM risk. Our results are consistent with the hypothesis that intervening to prevent HDP is an important (yet incomplete) pathway for reducing the excess risk among NH Black pregnancies compared with NH White pregnancies.

Can cognitive inflexibility reduce symptoms of anxiety and depression? Promoting the structural nested mean model in psychotherapy research

ABSTRACT Objective To estimate the causal effect of executive functioning on the remission of depression and anxiety symptoms in an observational dataset from a vocational rehabilitation program. It is also an aim to promote a method from the causal inference literature and to illustrate its value in this setting. Method With longitudinal (four-time points over 13 months) data from four independent sites, we compiled a dataset with 390 participants. At each time point, participants were tested on executive function and self-reported symptoms of anxiety and depression. We used g-estimation to evaluate whether objectively tested cognitive flexibility affected depressive/anxious symptoms and tested for moderation. Multiple imputations were used to handle missing data. Results The g-estimation showed a strong causal effect of cognitive inflexibility reducing depression and anxiety and modified by education level. In a counterfactual framework, a hypothetical intervention that could lower cognitive flexibility seemed to cause improvement in mental distress at the subsequent time-point (negative sign) for low education. The less flexibility, the larger improvement. For high education, the same but weaker effect was found, with a change in sign, negative during the intervention and positive during follow-up. Discussion An unexpected and strong effect was found from cognitive inflexibility on symptom improvement. This study demonstrates how to estimate causal psychological effects with standard software in an observational dataset with substantial missing and shows the value of such methods.

Time-varying treatment effect modification of oral analgesic effectiveness by depressive symptoms in knee osteoarthritis: an application of structural nested mean models in a prospective cohort.

Depressive symptoms are common in knee osteoarthritis (OA), exacerbate knee pain severity and may influence outcomes of oral analgesic treatments. The aim was to assess whether oral analgesic effectiveness in knee OA varies by fluctuations in depressive symptoms. The sample included Osteoarthritis Initiative (OAI) participants not treated with oral analgesics at enrolment (n = 1477), with radiographic disease at the first follow-up visit (defined as the index date). Oral analgesic treatment and depressive symptoms, assessed with the Center for Epidemiological Studies Depression [(CES-D) score ≥16] Scale, were measured over three annual visits. Knee pain severity was measured at visits adjacent to treatment and modifier using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale (rescaled range = 0-100). Structural nested mean models (SNMMs) estimated causal mean differences in knee pain severity comparing treatment versus no treatment. The average causal effects of treated versus not treated for observations without depressive symptoms showed negligible differences in knee pain severity. However, causal mean differences in knee pain severity comparing treatment versus no treatment among observations with depressive symptoms increased over time from -0.10 [95% confidence interval (CI): -9.94, 9.74] to -16.67 (95% CI: -26.33, -7.01). Accordingly, the difference in average causal effects regarding oral analgesic treatment for knee pain severity between person-time with and without depressive symptoms was largest (-16.53; 95% CI: -26.75, -6.31) at the last time point. Cumulative treatment for 2 or 3 years did not yield larger causal mean differences. Knee OA patients with persistent depressive symptoms and chronic pain may derive more analgesic treatment benefit than those without depressive symptoms and less pain.

Neighborhood Built Environment and Sleep Health: A Longitudinal Study in Low-Income and Predominantly African-American Neighborhoods.

In the present study, we examined the associations between physical characteristics of neighborhoods and sleep health outcomes and assessed the mediating role of physical activity in these associations. A longitudinal study (the Pittsburgh Hill/Homewood Research on Eating, Shopping, and Health (PHRESH) Zzz Study; n=1,051) was conducted in 2 low-income, predominately African-American neighborhoods in Pittsburgh, Pennsylvania, with repeated measures of neighborhood characteristics and sleep health outcomes from 2013 to 2018. Built environment measures of walkability, urban design, and neighborhood disorder were captured from systematic field observations. Sleep health outcomes included insufficient sleep, sleep duration, wakefulness after sleep onset, and sleep efficiency measured from 7-day actigraphy data. G-computations based on structural nested mean models were used to examine the total effects of each built environment feature, and causal mediation analyses were used to evaluate direct and indirect effects operating through physical activity. Urban design features were associated with decreased wakefulness after sleep onset (risk difference (RD) = -1.26, 95% confidence interval (CI): -4.31, -0.33). Neighborhood disorder (RD = -0.46, 95% CI: -0.86, -0.07) and crime rate (RD = -0.54, 95% CI: -0.93, -0.08) were negatively associated with sleep efficiency. Neighborhood walkability was not associated with sleep outcomes. We did not find a strong and consistent mediating role of physical activity. Interventions to improve sleep should target modifiable factors, including urban design and neighborhood disorder.

Does Cumulative Exposure to High-Poverty Schools Widen Test-Score Inequality?

Although there is an abundance of research on the association of school poverty (or socioeconomic status) and test score level, there is very little rigorous longitudinal evidence on the cumulative effects of exposure to differing school contexts. Drawing from methods used first in epidemiology and then in neighborhood effects research, we use population-level longitudinal data from North Carolina to estimate a structural nested mean model that permits proper adjustment for time-varying confounding. Unlike panel data studies using student fixed effects, which often report close to null findings, we find evidence of modest but significant negative effects of school poverty composition on eighth-grade reading and math test scores in models that control for third-grade test scores and baseline treatment status.

Early hypophosphatemia in critically ill children and the effect of parenteral nutrition: A secondary analysis of the PEPaNIC RCT

Hypophosphatemia during critical illness has been associated with adverse outcome. The reintroduction of enteral or parenteral nutrition, leading to refeeding hypophosphatemia (RFH), has been presented as potential risk factor. We investigated the occurrence of early RFH, its association with clinical outcome, and the impact of early parenteral nutrition (PN) on the development of early RFH in pediatric critical illness. This is a secondary analysis of the PEPaNIC randomized controlled trial (N=1440), which showed that withholding supplemental parenteral nutrition (PN) for 1 week (late-PN) in the pediatric intensive care unit (PICU) accelerated recovery and reduced new infections compared to early-PN (<24h). Patients with renal replacement therapy or unavailable phosphate concentrations were excluded from this analysis. Early RFH was defined as serum/plasma phosphate <0.65mmol/L and a drop of >0.16mmol/L within 3 days of admission to the PICU. The association between baseline characteristics and early RFH, and the association of early RFH with clinical outcome were investigated using logistic and linear regression models, both uncorrected and corrected for possible confounders. To examine the impact of nutritional intake on phosphate concentrations, structural nested mean models with propensity score and censoring models were used. A total of 1247 patients were eligible (618 early-PN, 629 late-PN). Early RFH occurred in 40 patients (3%) in total, significantly more in the early-PN group (n=31, within-group occurrence 5%) than in the late-PN-group (n=9, within-group occurrence 1%, p<0.001). Patients who were older (OR 1.14 (95% CI 1.08; 1.21) per year added, p<0.001) and who had a higher Pediatric Risk of Mortality (PIM3) score had a higher risk of developing early RFH (OR 1.36 (95% CI 1.15; 1.59) per unit added, p<0.001), whereas patients in the late-PN group had a lower risk of early RFH (OR 0.24 (95% CI 0.10; 0.49), p<0.001). Early RFH was significantly associated with a 56% longer PICU stay (p=0.003) and 42% longer hospital stay (p=0.007), but not with new infections (OR 2.01 (95% CI 0.90; 4.30), p=0.08) or length of mechanical ventilatory support (OR 1.05 (95% CI-3.92; 6.03), p=0.68), when adjusted for possible confounders. Increase of parenteral nutrition intake (in % kcal of predicted resting energy expenditure) decreased phosphate concentrations (c=-0.002 (95% CI -0.002;-0.001). Early RFH occurred in 3% of critically ill children. Patients randomized to late-PN had a lower chance of developing early RFH, which may be explained by the more gradual build-up of nutrition. As early RFH might impact recovery, it is important to closely monitor phosphate concentrations in patients, especially of those at risk for early RFH.

A multiplicative structural nested mean model for zero-inflated outcomes.

Zero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes which flexibly describes the joint effect of a sequence of treatments in the presence of time-varying confounders. The proposed estimator solves a doubly robust estimating equation, where the nuisance functions, namely the propensity score and conditional outcome means given confounders, are estimated parametrically or nonparametrically. To improve the accuracy, we leverage the characteristic of zero-inflated outcomes by estimating the conditional means in two parts, that is, separately modelling the probability of having positive outcomes given confounders, and the mean outcome conditional on its being positive and given the confounders. We show that the proposed estimator is consistent and asymptotically normal as either the sample size or the follow-up time goes to infinity. Moreover, the typical sandwich formula can be used to estimate the variance of treatment effect estimators consistently, without accounting for the variation due to estimating nuisance functions. Simulation studies and an application to a freemium mobile game dataset are presented to demonstrate the empirical performance of the proposed method and support our theoretical findings.

Within-Person Variability Score-Based Causal Inference: A Two-Step Estimation for Joint Effects of Time-Varying Treatments

Behavioral science researchers have shown strong interest in disaggregating within-person relations from between-person differences (stable traits) using longitudinal data. In this paper, we propose a method of within-person variability score-based causal inference for estimating joint effects of time-varying continuous treatments by controlling for stable traits of persons. After explaining the assumed data-generating process and providing formal definitions of stable trait factors, within-person variability scores, and joint effects of time-varying treatments at the within-person level, we introduce the proposed method, which consists of a two-step analysis. Within-person variability scores for each person, which are disaggregated from stable traits of that person, are first calculated using weights based on a best linear correlation preserving predictor through structural equation modeling (SEM). Causal parameters are then estimated via a potential outcome approach, either marginal structural models (MSMs) or structural nested mean models (SNMMs), using calculated within-person variability scores. Unlike the approach that relies entirely on SEM, the present method does not assume linearity for observed time-varying confounders at the within-person level. We emphasize the use of SNMMs with G-estimation because of its property of being doubly robust to model misspecifications in how observed time-varying confounders are functionally related to treatments/predictors and outcomes at the within-person level. Through simulation, we show that the proposed method can recover causal parameters well and that causal estimates might be severely biased if one does not properly account for stable traits. An empirical application using data regarding sleep habits and mental health status from the Tokyo Teen Cohort study is also provided.

Population-Level Effectiveness of COVID-19 Vaccination Program in the United States: Causal Analysis Based on Structural Nested Mean Model.

Though COVID-19 vaccines have shown high efficacy, real-world effectiveness at the population level remains unclear. Based on the longitudinal data on vaccination coverage and daily infection cases from fifty states in the United States from March to May 2021, causal analyses were conducted using structural nested mean models to estimate the population-level effectiveness of the COVID-19 vaccination program against infection with the original strain. We found that in the US, every 1% increase of vaccination coverage rate reduced the weekly growth rate of COVID-19 confirmed cases by 1.02% (95% CI: 0.26%, 1.69%), and the estimated population-level effectiveness of the COVID-19 program was 63.9% (95% CI: 18.0%, 87.5%). In comparison to a no-vaccination scenario, the COVID-19 vaccination campaign averted 8.05 million infections through the study period. Scenario analyses show that a vaccination program with doubled vaccination speed or with more rapid vaccination speed at the early stages of the campaign would avert more infections and increase vaccine effectiveness. The COVID-19 vaccination program demonstrated a high population-level effectiveness and significantly reduced the disease burden in the US. Accelerating vaccine rollout, especially at an early stage of the campaign, is crucial for reducing COVID-19 infections.

Coherent modeling of longitudinal causal effects on binary outcomes.

Analyses of biomedical studies often necessitate modeling longitudinal causal effects. The current focus on personalized medicine and effect heterogeneity makes this task even more challenging. Toward this end, structural nested mean models (SNMMs) are fundamental tools for studying heterogeneous treatment effects in longitudinal studies. However, when outcomes are binary, current methods for estimating multiplicative and additive SNMM parameters suffer from variation dependence between the causal parameters and the noncausal nuisance parameters. This leads to a series of difficulties in interpretation, estimation, and computation. These difficulties have hindered the uptake of SNMMs in biomedical practice, where binary outcomes are very common. We solve the variation dependence problem for the binary multiplicative SNMM via a reparameterization of the noncausal nuisance parameters. Our novel nuisance parameters are variation independent of the causal parameters, and hence allow for coherent modeling of heterogeneous effects from longitudinal studies with binary outcomes. Our parameterization also provides a key building block for flexible doubly robust estimation of the causal parameters. Along the way, we prove that an additive SNMM with binary outcomes does not admit a variation independent parameterization, thereby justifying the restriction to multiplicative SNMMs.

Mediating role of psychological distress in the associations between neighborhood social environments and sleep health.

The characteristics of neighborhood social environments, such as safety and social cohesion, have been examined as determinants of poor sleep. The current study investigates associations between neighborhood social characteristics and sleep health, as well as the mediating role of psychological distress on these possible associations. Three waves of PHRESH Zzz (n = 2699), a longitudinal study conducted in two low-income, predominately Black neighborhoods, were utilized for this analysis. The characteristics of neighborhood social environments were measured using crime rates, a neighborhood social disorder index, and self-reported social cohesion. Sleep health was measured via 7 days of wrist-worn actigraphy as insufficient sleep, sleep duration, wake after sleep onset (WASO), and sleep efficiency. G-estimations based on structural nested mean models and mediation analyses were performed to estimate the effects of neighborhood social environments on sleep as well as direct/indirect effects through psychological distress. Crime rate around residential addresses was associated with increased risk of insufficient sleep (risk ratio: 1.05 [1.02, 1.12]), increased WASO (β: 3.73 [0.26, 6.04]), and decreased sleep efficiency (β: -0.54 [-0.91, -0.09]). Perceived social cohesion was associated with decreased risk of insufficient sleep (OR: 0.93 [0.88, 0.97]). Psychological distress mediated part of the associations of crime and social cohesion with insufficient sleep. Neighborhood social environments may contribute to poor sleep health in low-income, predominantly Black neighborhoods, and psychological distress can be a salient pathway linking these neighborhood characteristics and sleep health.

Childhood police encounters, social isolation and epigenetic age acceleration among older U.S. adults

ObjectivesThis study examined associations of childhood police encounters with biological age acceleration in later life, and their mediation by subjective or objective social isolation. MethodsData were from the Health and Retirement Study, nationally representative of older U.S. adults. Age acceleration was proxied through newly available epigenetic measures. Doubly robust estimation was used to establish baseline linkages, and heterogenous treatment effect models to examine variations in effects by one's increasing propensity for early police encounters. Mediation analysis was through a recently developed regression-with-residuals approach for structural nested mean models. ResultsChildhood police encounters was prospectively associated with age acceleration. Those with such early experiences also reported more loneliness and isolation from their community, although their ties to family and friends seemed stronger. Associations did not significantly decline with increasing propensity for such childhood experiences. Treatment effects on age acceleration seemed partly mediated by loneliness and by community isolation. DiscussionFindings add to the growing evidence on the “long arm of childhood,” and highlight public health implications of policy-driven social exposures.

Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations.

Structural nested mean models (SNMMs) are useful for causal inference of treatment effects in longitudinal observational studies. Most existing works assume that the data are collected at prefixed time points for all subjects, which, however, may be restrictive in practice. To deal with irregularly spaced observations, we assume a class of continuous-time SNMMs and a martingale condition of no unmeasured confounding (NUC) to identify the causal parameters. We develop the semiparametric efficiency theory and locally efficient estimators for continuous-time SNMMs. This task is nontrivial due to the restrictions from the NUC assumption imposed on the SNMM parameter. In the presence of ignorable censoring, we show that the complete-case estimator is optimal among a class of weighting estimators including the inverse probability of censoring weighting estimator, and it achieves a double robustness feature in that it is consistent if at least one of the models for the potential outcome mean function and the treatment process is correctly specified. The new framework allows us to conduct causal analysis respecting the underlying continuous-time nature of data processes. The simulation study shows that the proposed estimator outperforms existing approaches. We estimate the effect of time to initiate highly active antiretroviral therapy on the CD4 count at year 2 from the observational Acute Infection and Early Disease Research Program database.

Commentary: Using potential outcomes causal methods to assess whether reductions in PM2.5 result in decreased mortality

Causal inference regarding exposures to ambient fine particulate matter (PM2.5) and mortality estimated from observational studies is limited by confounding, among other factors. In light of a variety of causal inference frameworks and methods that have been developed over the past century to specifically quantify causal effects, three research teams were selected in 2016 to evaluate the causality of PM2.5-mortality association among Medicare beneficiaries, using their own selections of causal inference methods and study designs but the same data sources. With a particular focus on controlling for unmeasured confounding, two research teams adopted an instrumental variables approach under a quasi-experiment or natural experiment study design, whereas one team adopted a structural nested mean model under the traditional cohort study design. All three research teams reported results supporting an estimated counterfactual causal relationship between ambient PM2.5 and all-cause mortality, and their estimated causal relationships are largely of similar magnitudes to recent epidemiological studies based on regression analyses with omitted potential confounders. The causal methods used by all three research teams were built upon the potential outcomes framework. This framework has marked conceptual advantages over regression-based methods in addressing confounding and yielding unbiased estimates of average treatment effect in observational epidemiological studies. However, potential violations of the unverifiable assumptions underlying each causal method leave the results from all three studies subject to biases. We also note that the studies are not immune to some other common sources of bias, including exposure measurement errors, ecological study design, model uncertainty and specification errors, and irrelevant exposure windows, that can undermine the validity of causal inferences in observational studies. As a result, despite some apparent consistency of study results from the three research teams with the wider epidemiological literature on PM2.5-mortality statistical associations, caution seems warranted in drawing causal conclusions from the results. A possible way forward is to improve study design and reduce dependence of conclusions on untested assumptions by complementing potential outcomes methods with structural causal modeling and information-theoretic methods that emphasize empirically tested and validated relationships.

Efficient Estimation of Optimal Regimes Under a No Direct Effect Assumption

We derive new estimators of an optimal joint testing and treatment regime under the no direct effect (NDE) assumption that a given laboratory, diagnostic, or screening test has no effect on a patient’s clinical outcomes except through the effect of the test results on the choice of treatment. We model the optimal joint strategy with an optimal structural nested mean model (opt-SNMM). The proposed estimators are more efficient than previous estimators of the parameters of an opt-SNMM because they efficiently leverage the “NDE of testing” assumption. Our methods will be of importance to decision scientists who either perform cost-benefit analyses or are tasked with the estimation of the “value of information” supplied by an expensive diagnostic test (such as an MRI to screen for lung cancer). Supplementary materials for this article are available online.

'In search of lost time': Identifying the causative role of cumulative competition load and competition time-loss in professional tennis using a structural nested mean model.

Injury prevention is critical to the achievement of peak performance in elite sport. For professional tennis players, the topic of injury prevention has gained even greater importance in recent years as multiple of the best male players have been sidelined owing to injury. Identifying potential causative factors of injury is essential for the development of effective prevention strategies, yet such research is hampered by incomplete data, the complexity of injury etiology, and observational study biases. The present study attempts to address these challenges by focusing on competition load and time-loss to competition-a completely observable risk factor and outcome-and using a structural nested mean model (SNMM) to identify the potential causal role of cumulative competition load on the risk of time-loss. Using inverse probability of treatment weights to balance exposure histories with respect to player ability, past injury, and consecutive competition weeks at each time point; the SNMM analysis of 389 professional male players and 55,773 weeks of competition found that total load significantly increases the risk of time-loss (HR = 1.05 per 1,000 games of additional load 95% CI 1.01-1.10) and this effect becomes magnified with age. Standard regression showed a protective effect of load, highlighting the value of more robust causal methods in the study of dynamic exposures and injury in sport and the need for further applications of these methods for understanding how time-loss and injuries of elite athletes might be prevented in the future.