The application of target trials with longitudinal targeted maximum likelihood estimation to assess the effect of alcohol consumption in adolescence on depressive symptoms in adulthood

Time-varying confounding is a common challenge for causal inference in observational studies with time-varying treatments, long follow-up periods, and participant dropout. Confounder adjustment using traditional approaches can be limited by data sparsity, weight instability, and computational issues. The Nicotine Dependence in Teens Study is a prospective cohort study, and we used data from 21 data collection cycles carried out from 1999 to 2008 among 1294 students recruited from 10 high schools in Montreal, Quebec, Canada, including follow-up into adulthood. Our aim in this study was to estimate associations of timing of alcohol initiation and cumulative duration of alcohol use with depression symptoms in adulthood. Based on the target trials framework, we defined intention-to-treat and as-treated parameters in a marginal structural model with sex as a potential effect-modifier. We then used the observational data to emulate the trials. For estimation, we used pooled longitudinal target maximum likelihood estimation, a plug-in estimator with double-robust and local efficiency properties. We describe strategies for dealing with high-dimensional potential drinking patterns and practical positivity violations due to a long follow-up time, including modifying the effect of interest by removing sparsely observed drinking patterns from the loss function and applying longitudinal modified treatment policies to represent the effect of discouraging drinking.

EFFECT OF PAXLOVID TREATMENT DURING ACUTE COVID-19 ON LONG COVID ONSET: AN EHR-BASED TARGET TRIAL EMULATION FROM THE N3C AND RECOVER CONSORTIA

Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,352 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation (TTE) framework to estimate the effect of Paxlovid treatment on PASC incidence. We estimated overall PASC incidence using a computable phenotype. We also measured the onset of novel cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment did not have a significant effect on overall PASC incidence (relative risk [RR] = 0.98, 95% confidence interval [CI] 0.95-1.01). However, it had a protective effect on cognitive (RR = 0.90, 95% CI 0.84-0.96) and fatigue (RR = 0.95, 95% CI 0.91-0.98) symptom clusters, which suggests that the etiology of these symptoms may be more closely related to viral load than that of respiratory symptoms.

Second generation of pyrimidin-quinolone hybrids obtained from virtual screening acting as sphingosine kinase 1 inhibitors and potential anticancer agents

We report here the virtual screening design, synthesis and activity of eight new inhibitors of SphK1. For this study we used a pre-trained Graph Convolutional Network (GCN) combined with docking calculations. This exploratory analysis proposed nine compounds from which eight displayed significant inhibitory effect against sphingosine kinase 1 (SphK1) demonstrating a high level of efficacy for this approach. Four of these compounds also displayed anticancer activity against different tumor cell lines, and three of them (5), (6) and (7) have shown a wide inhibitory action against many of the cancer cell line tested, with GI50 below 5 µM, being (5) the most promising with TGI below 10 µM for the half of cell lines. Our results suggest that the three most promising compounds reported here are the pyrimidine-quinolone hybrids (1) and (6) linked by p-aminophenylsulfanyl and o-aminophenol fragments respectively, and (8) without such aryl linker. We also performed an exhaustive study about the molecular interactions that stabilize the different ligands at the binding site of SphK1. This molecular modeling analysis was carried out by using combined techniques: docking calculations, MD simulations and QTAIM analysis. In this study we also included PF543, as reference compound, in order to better understand the molecular behavior of these ligands at the binding site of SphK1.These results provide useful information for the design of new inhibitors of SphK1 possessing these structural scaffolds.

Using instrumental variables to address unmeasured confounding in causal mediation analysis

Mediation analysis is a strategy for understanding the mechanisms by which interventions affect later outcomes. However, unobserved confounding concerns may be compounded in mediation analyses, as there may be unobserved exposure-outcome, exposure-mediator, and mediator-outcome confounders. Instrumental variables (IVs) are a popular identification strategy in the presence of unobserved confounding. However, in contrast to the rich literature on the use of IV methods to identify and estimate a total effect of a non-randomized exposure, there has been almost no research into using IV as an identification strategy to identify mediational indirect effects. In response, we define and nonparametrically identify novel estimands-double complier interventional direct and indirect effects-when 2, possibly related, IVs are available, one for the exposure and another for the mediator. We propose nonparametric, robust, efficient estimators for these effects and apply them to a housing voucher experiment.

Causal survival analysis under competing risks using longitudinal modified treatment policies

Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, ordinal, or continuous treatments measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to a competing event that precludes observation of the event of interest. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as [Formula: see text]-consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.

Sensitivity analysis for causality in observational studies for regulatory science

Objective

The United States Congress passed the 21st Century Cures Act mandating the development of Food and Drug Administration guidance on regulatory use of real-world evidence. The Forum on the Integration of Observational and Randomized Data conducted a meeting with various stakeholder groups to build consensus around best practices for the use of real-world data (RWD) to support regulatory science. Our companion paper describes in detail the context and discussion of the meeting, which includes a recommendation to use a causal roadmap for study designs using RWD. This article discusses one step of the roadmap: the specification of a sensitivity analysis for testing robustness to violations of causal model assumptions.

Methods

We present an example of a sensitivity analysis from a RWD study on the effectiveness of Nifurtimox in treating Chagas disease, and an overview of various methods, emphasizing practical considerations on their use for regulatory purposes.

Results

Sensitivity analyses must be accompanied by careful design of other aspects of the causal roadmap. Their prespecification is crucial to avoid wrong conclusions due to researcher degrees of freedom. Sensitivity analysis methods require auxiliary information to produce meaningful conclusions; it is important that they have at least two properties: the validity of the conclusions does not rely on unverifiable assumptions, and the auxiliary information required by the method is learnable from the corpus of current scientific knowledge.

Conclusions

Prespecified and assumption-lean sensitivity analyses are a crucial tool that can strengthen the validity and trustworthiness of effectiveness conclusions for regulatory science.

Efficient and flexible estimation of natural direct and indirect effects under intermediate confounding and monotonicity constraints

Natural direct and indirect effects are mediational estimands that decompose the average treatment effect and describe how outcomes would be affected by contrasting levels of a treatment through changes induced in mediator values (in the case of the indirect effect) or not through induced changes in the mediator values (in the case of the direct effect). Natural direct and indirect effects are not generally point-identified in the presence of a treatment-induced confounder; however, they may be identified if one is willing to assume monotonicity between the treatment and the treatment-induced confounder. We argue that this assumption may be reasonable in the relatively common encouragement-design trial setting, where the intervention is randomized treatment assignment and the treatment-induced confounder is whether or not treatment was actually taken/adhered to. We develop efficiency theory for the natural direct and indirect effects under this monotonicity assumption, and use it to propose a nonparametric, multiply robust estimator. We demonstrate the finite sample properties of this estimator using a simulation study, and apply it to data from the Moving to Opportunity Study to estimate the natural direct and indirect effects of being randomly assigned to receive a Section 8 housing voucher-the most common form of federal housing assistance-on risk developing any mood or externalizing disorder among adolescent boys, possibly operating through various school and community characteristics.

A causal roadmap for generating high-quality real-world evidence

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

Nonparametric causal mediation analysis for stochastic interventional (in)direct effects

Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary exposures and static interventions and (ii) direct and indirect effect decompositions have been pursued that are only identifiable in the absence of intermediate confounders affected by exposure. We present a theoretical study of an (in)direct effect decomposition of the population intervention effect, defined by stochastic interventions jointly applied to the exposure and mediators. In contrast to existing proposals, our causal effects can be evaluated regardless of whether an exposure is categorical or continuous and remain well-defined even in the presence of intermediate confounders affected by exposure. Our (in)direct effects are identifiable without a restrictive assumption on cross-world counterfactual independencies, allowing for substantive conclusions drawn from them to be validated in randomized controlled trials. Beyond the novel effects introduced, we provide a careful study of nonparametric efficiency theory relevant for the construction of flexible, multiply robust estimators of our (in)direct effects, while avoiding undue restrictions induced by assuming parametric models of nuisance parameter functionals. To complement our nonparametric estimation strategy, we introduce inferential techniques for constructing confidence intervals and hypothesis tests, and discuss open-source software, the $\texttt{medshift}$$\texttt{R}$ package, implementing the proposed methodology. Application of our (in)direct effects and their nonparametric estimators is illustrated using data from a comparative effectiveness trial examining the direct and indirect effects of pharmacological therapeutics on relapse to opioid use disorder.

Optimally Choosing Medication Type for Patients With Opioid Use Disorder

Patients with opioid use disorder (OUD) tend to get assigned to one of 3 medications based on the treatment program to which the patient presents (e.g., opioid treatment programs tend to treat patients with methadone, while office-based practices tend to prescribe buprenorphine). It is possible that optimally matching patients with treatment type would reduce the risk of return to regular opioid use (RROU). We analyzed data from 3 comparative effectiveness trials from the US National Institute on Drug Abuse Clinical Trials Network (CTN0027, 2006-2010; CTN0030, 2006-2009; and CTN0051 2014-2017), in which patients with OUD (n = 1,459) were assigned to treatment with either injection extended-release naltrexone (XR-NTX), sublingual buprenorphine-naloxone (BUP-NX), or oral methadone. We learned an individualized rule by which to assign medication type such that risk of RROU during 12 weeks of treatment would be minimized, and then estimated the amount by which RROU risk could be reduced if the rule were applied. Applying our estimated treatment rule would reduce risk of RROU compared with treating everyone with methadone (relative risk (RR) = 0.79, 95% confidence interval (CI): 0.60, 0.97) or treating everyone with XR-NTX (RR = 0.71, 95% CI: 0.47, 0.96). Applying the estimated treatment rule would have resulted in a similar risk of RROU to that of with treating everyone with BUP-NX (RR = 0.92, 95% CI: 0.73, 1.11).

Effect of Sepsis on Death as Modified by Solid Organ Transplantation

Background

Patients with solid organ transplants (SOT) have an increased risk for sepsis compared to the general population. Paradoxically, studies suggest that SOT patients with sepsis may experience better outcomes compared to those without a SOT. However, these analyses used previous definitions of sepsis. It remains unknown whether the more recent definitions of sepsis and modern analytic approaches demonstrate a similar relationship.

Methods

Using the Weill Cornell-Critical Care Database for Advanced Research (WC-CEDAR) we analyzed granular physiologic, microbiologic, comorbidity, and therapeutic data in patients with and without SOT admitted to intensive care units (ICU’s). We used a survival analysis with a targeted minimum loss-based estimation, adjusting for within group (SOT and non-SOT) potential confounders to ascertain whether the effect of sepsis, defined by sepsis-3, on 28-day mortality was modified by SOT status. We performed additional analyses on restricted populations.

Results

We analyzed 28,431 patients: 439 with SOT and sepsis, 281 with SOT without sepsis, 6793 with sepsis and without SOT, and 20918 with neither. The most common SOT types were kidney (475) and liver (163). Despite a higher severity of illness in both sepsis groups, the adjusted sepsis-attributable effect on 28-day mortality for non-SOT patients was 4.1% (3.8, 4.5) and -14.4% (-16.8, -12) for SOT patients. The adjusted SOT effect modification was -18.5% (-21.2, -15.9). The adjusted sepsis-attributable effect for immunocompromised controls was -3.5% (-4.5, -2.6).

Conclusions

Across a large database of patients admitted to ICU’s, the sepsis associated 28-day mortality effect was significantly lower in SOT patients compared to controls.

Validation of the International Classification of Diseases, Tenth Revision Code for the National Institutes of Health Stroke Scale Score

BACKGROUND

Administrative data can be useful for stroke research but have historically lacked data on stroke severity. Hospitals increasingly report the National Institutes of Health Stroke Scale (NIHSS) score using an International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code, but this code's validity remains unclear.

METHODS

We examined the concordance of ICD-10 NIHSS scores versus NIHSS scores recorded in CAESAR (Cornell Acute Stroke Academic Registry). We included all patients with acute ischemic stroke from October 1, 2015, when US hospitals transitioned to ICD-10, through 2018, the latest year in our registry. The NIHSS score (range, 0-42) recorded in our registry served as the reference gold standard. ICD-10 NIHSS scores were derived from hospital discharge diagnosis code R29.7xx, with the latter 2 digits representing the NIHSS score. Multiple logistic regression was used to explore factors associated with availability of ICD-10 NIHSS scores. We used ANOVA to examine the proportion of variation (R2) in the true (registry) NIHSS score that was explained by the ICD-10 NIHSS score.

RESULTS

Among 1357 patients, 395 (29.1%) had an ICD-10 NIHSS score recorded. This proportion increased from 0% in 2015 to 46.5% in 2018. In a logistic regression model, only higher registry NIHSS score (odds ratio per point, 1.05 [95% CI, 1.03-1.07]) and cardioembolic stroke (odds ratio, 1.4 [95% CI, 1.0-2.0]) were associated with availability of the ICD-10 NIHSS score. In an ANOVA model, the ICD-10 NIHSS score explained almost all the variation in the registry NIHSS score (R2=0.88). Fewer than 10% of patients had a large discordance (≥4 points) between their ICD-10 and registry NIHSS scores.

CONCLUSIONS

When present, ICD-10 codes representing NIHSS scores had excellent agreement with NIHSS scores recorded in our stroke registry. However, ICD-10 NIHSS scores were often missing, especially in less severe strokes, limiting the reliability of these codes for risk adjustment.

Influence of social deprivation index on in-hospital outcomes of COVID-19

While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospitalized with confirmed COVID-19 between March 1, 2020 and February 8, 2021 from the INSIGHT Clinical Research Network (CRN). To compute the SDI (exposure variable), we linked clinical data using patient's residential zip-code with social data at zip-code tabulation area. SDI is a composite of seven socioeconomic characteristics determinants at the zip-code level. For this analysis, we categorized SDI into quintiles. The two outcomes of interest were in-hospital intubation and mortality. For each outcome, we examined logistic regression and random forests to determine incremental value of SDI in predicting outcomes. We studied 30,016 included COVID-19 patients. In a logistic regression model for intubation, a model including demographics, comorbidity, and vitals had an Area under the receiver operating characteristic curve (AUROC) = 0.73 (95% CI 0.70-0.75); the addition of SDI did not improve prediction [AUROC = 0.73 (95% CI 0.71-0.75)]. In a logistic regression model for in-hospital mortality, demographics, comorbidity, and vitals had an AUROC = 0.80 (95% CI 0.79-0.82); the addition of SDI in Model 2 did not improve prediction [AUROC = 0.81 (95% CI 0.79-0.82)]. Random forests revealed similar findings. SDI did not provide incremental improvement in predicting in-hospital intubation or mortality. SDI plays an important role on who acquires COVID-19 and its severity; but once hospitalized, SDI appears less important.

Causal Inference for Social Network Data

We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other units as sample size increases. In addition, while previous methods have implicitly permitted only one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties and for dependence due to latent similarities among nodes sharing ties. We propose new causal effects that are specifically of interest in social network settings, such as interventions on network ties and network structure. We use our methods to reanalyze an influential and controversial study that estimated causal peer effects of obesity using social network data from the Framingham Heart Study; after accounting for network structure we find no evidence for causal peer effects.

When the Ends do not Justify the Means: Learning Who is Predicted to Have Harmful Indirect Effects

There is a growing literature on finding rules by which to assign treatment based on an individual's characteristics such that a desired outcome under the intervention is maximized. A related goal entails identifying a subpopulation of individuals predicted to have a harmful indirect effect (the effect of treatment on an outcome through mediators), perhaps even in the presence of a predicted beneficial total treatment effect. In some cases, the implications of a likely harmful indirect effect may outweigh an anticipated beneficial total treatment effect, and would motivate further discussion of whether to treat identified individuals. We build on the mediation and optimal treatment rule literatures to propose a method of identifying a subgroup for which the treatment effect through the mediator is expected to be harmful. Our approach is nonparametric, incorporates post-treatment confounders of the mediator-outcome relationship, and does not make restrictions on the distribution of baseline covariates, mediating variables, or outcomes. We apply the proposed approach to identify a subgroup of boys in the MTO housing voucher experiment who are predicted to have a harmful indirect effect of housing voucher receipt on subsequent psychiatric disorder incidence through aspects of their school and neighborhood environments.

Synthesis, and spectroscopic and structural characterization of three new styrylquinoline–benzimidazole hybrids

Three new 4-styrylquinoline–benzimidazole hybrids have been synthesized using a reaction sequence in which 2-methylquinoline precursors first undergo selective oxidation by selenium dioxide to form the corresponding 2-formylquinoline intermediates, followed by oxidative cyclocondensation reactions with benzene-1,2-diamine to yield the hybrid products. The formyl intermediates and the hybrid products have all been fully characterized using a combination of IR, 1H and 13C NMR spectroscopy, and high-resolution mass spectrometry, and the structures of the three hybrid products have been determined using single-crystal X-ray diffraction. Ethyl (E)-2-(1H-benzo[d]imidazol-2-yl)-4-(4-chlorostyryl)quinoline-3-carboxylate, C27H20ClN3O2, (IIIa), and ethyl (E)-2-(1H-benzo[d]imidazol-2-yl)-4-(2-methoxystyryl)quinoline-3-carboxylate, C28H23N3O3, (IIIb), both crystallize in the solvent-free form with Z′ = 1, but ethyl (E)-2-(1H-benzo[d]imidazol-2-yl)-4-(4-methylstyryl)quinoline-3-carboxylate, C28H23N3O2, (IIIc), crystallizes as a partial hexane solvate with Z′ = 3, and the ester group in one of the independent molecules is disordered over two sets of atomic sites having occupancies of 0.765 (7) and 0.235 (7). The molecules of (IIIc) enclose continuous channels which are occupied by disordered solvent molecules having partial occupancy. In all of the molecules of (IIIa)–(IIIc), the styrylquinoline fragment is markedly nonplanar. Different combinations of N—H...O and C—H...π hydrogen bonds generate supramolecular assemblies which are two-dimensional in (IIIb) and (IIIc), but three-dimensional in (IIIa). Comparisons are made with the structures of some related compounds.

Comparison of a Target Trial Emulation Framework vs Cox Regression to Estimate the Association of Corticosteroids With COVID-19 Mortality

IMPORTANCE

Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data.

OBJECTIVE

To compare a modern method for statistical inference, including a target trial emulation framework and doubly robust estimation, with approaches common in the clinical literature, such as Cox proportional hazards models.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used longitudinal electronic health record data for outcomes at 28-days from time of hospitalization within a multicenter New York, New York, hospital system. Participants included adult patients hospitalized between March 1 and May 15, 2020, with COVID-19 and not receiving corticosteroids for chronic use. Data were analyzed from October 2021 to March 2022.

EXPOSURES

Corticosteroid exposure was defined as more than 0.5 mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, exposures were corticosteroids for 6 days if and when a patient met criteria for severe hypoxia vs no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models varied by study design (no time frame, 1 day, and 5 days from time of severe hypoxia).

MAIN OUTCOMES AND MEASURES

The main outcome was 28-day mortality from time of hospitalization. The association of corticosteroids with mortality for patients with moderate to severe COVID-19 was assessed using the World Health Organization (WHO) meta-analysis of corticosteroid randomized clinical trials as a benchmark.

RESULTS

A total of 3298 patients (median [IQR] age, 65 [53-77] years; 1970 [60%] men) were assessed, including 423 patients who received corticosteroids at any point during hospitalization and 699 patients who died within 28 days of hospitalization. Target trial emulation analysis found corticosteroids were associated with a reduced 28-day mortality rate, from 32.2%; (95% CI, 30.9%-33.5%) to 25.7% (95% CI, 24.5%-26.9%). This estimate is qualitatively identical to the WHO meta-analysis odds ratio of 0.66 (95% CI, 0.53-0.82). Hazard ratios using methods comparable with current corticosteroid research range in size and direction, from 0.50 (95% CI, 0.41-0.62) to 1.08 (95% CI, 0.80-1.47).

CONCLUSIONS AND RELEVANCE

These findings suggest that clinical research based on observational data can be used to estimate findings similar to those from randomized clinical trials; however, the correctness of these estimates requires designing the study and analyzing the data based on principles that are different from the current standard in clinical research.

Buprenorphine & methadone dosing strategies to reduce risk of relapse in the treatment of opioid use disorder

BACKGROUND

Although there is consensus that having a "high-enough" dose of buprenorphine (BUP-NX) or methadone is important for reducing relapse to opioid use, there is debate about what this dose is and how it should be attained. We estimated the extent to which different dosing strategies would affect risk of relapse over 12 weeks of treatment, separately for BUP-NX and methadone.

METHODS

This was a secondary analysis of three comparative effectiveness trials. We examined four dosing strategies: 1) increasing dose in response to participant-specific opioid use, 2) increasing dose weekly until some minimum dose (16 mg BUP, 100 mg methadone) was reached, 3) increasing dose weekly until some minimum and increasing dose in response to opioid use thereafter (referred to as the "hybrid strategy"), and 4) keeping dose constant after the first 2 weeks of treatment. We used a longitudinal sequentially doubly robust estimator to estimate contrasts between dosing strategies on risk of relapse.

RESULTS

For BUP-NX, increasing dose following the hybrid strategy resulted in the lowest risk of relapse. For methadone, holding dose constant resulted in greatest risk of relapse; the other three strategies performed similarly. For example, the hybrid strategy reduced week 12 relapse risk by 13 % (RR: 0.87, 95 %CI: 0.83-0.95) and by 20 % (RR: 0.80, 95 %CI: 0.71-0.90) for BUP-NX and methadone respectively, as compared to holding dose constant.

CONCLUSIONS

Doses should be targeted toward minimum thresholds and, in the case of BUP-NX, raised when patients continue to use opioids.

Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19

The rapid finding of effective therapeutics requires efficient use of available resources in clinical trials. Covariate adjustment can yield statistical estimates with improved precision, resulting in a reduction in the number of participants required to draw futility or efficacy conclusions. We focus on time-to-event and ordinal outcomes. When more than a few baseline covariates are available, a key question for covariate adjustment in randomised studies is how to fit a model relating the outcome and the baseline covariates to maximise precision. We present a novel theoretical result establishing conditions for asymptotic normality of a variety of covariate-adjusted estimators that rely on machine learning (e.g.,ℓ 1 -regularisation, Random Forests, XGBoost, and Multivariate Adaptive Regression Splines [MARS]), under the assumption that outcome data are missing completely at random. We further present a consistent estimator of the asymptotic variance. Importantly, the conditions do not require the machine learning methods to converge to the true outcome distribution conditional on baseline variables, as long as they converge to some (possibly incorrect) limit. We conducted a simulation study to evaluate the performance of the aforementioned prediction methods in COVID-19 trials. Our simulation is based on resampling longitudinal data from over 1500 patients hospitalised with COVID-19 at Weill Cornell Medicine New York Presbyterian Hospital. We found that usingℓ 1 -regularisation led to estimators and corresponding hypothesis tests that control type 1 error and are more precise than an unadjusted estimator across all sample sizes tested. We also show that when covariates are not prognostic of the outcome,ℓ 1 -regularisation remains as precise as the unadjusted estimator, even at small sample sizes (n = 100 ). We give an R package adjrct that performs model-robust covariate adjustment for ordinal and time-to-event outcomes.

When Effects Cannot be Estimated: Redefining Estimands to Understand the Effects of Naloxone Access Laws

Violations of the positivity assumption (also called the common support condition) challenge health policy research and can result in significant bias, large variance, and invalid inference. We define positivity in the single- and multiple-timepoint (i.e., longitudinal) health policy evaluation setting, and discuss real-world threats to positivity. We show empirical evidence of the practical positivity violations that can result when attempting to estimate the effects of health policies (in this case, Naloxone Access Laws). In such scenarios, an alternative is to estimate the effect of a shift in law enactment (e.g., the effect if enactment had been delayed by some number of years). Such an effect corresponds to what is called a modified treatment policy, and dramatically weakens the required positivity assumption, thereby offering a means to estimate policy effects even in scenarios with serious positivity problems. We apply the approach to define and estimate the longitudinal effects of Naloxone Access Laws on opioid overdose rates.

Comparison of a Target Trial Emulation Framework to Cox Regression to Estimate the Effect of Corticosteroids on COVID-19 Mortality

Importance

Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data.

Objective

To compare (1) a modern method for causal inference including a target trial emulation framework and doubly robust estimation to (2) approaches common in the clinical literature such as Cox proportional hazards models. To do this, we estimate the effect of corticosteroids on mortality for moderate-to-severe coronavirus disease 2019 (COVID-19) patients. We use the World Health Organization's (WHO) meta-analysis of corticosteroid randomized controlled trials (RCTs) as a benchmark.

Design

Retrospective cohort study using longitudinal electronic health record data for 28 days from time of hospitalization.

Settings

Multi-center New York City hospital system.

Participants

Adult patients hospitalized between March 1-May 15, 2020 with COVID-19 and not on corticosteroids for chronic use.

Intervention

Corticosteroid exposure defined as >0.5mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, interventions are (1) corticosteroids for six days if and when patient meets criteria for severe hypoxia and (2) no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models vary by study design (no time frame, one-, and five-days from time of severe hypoxia).

Main outcome

28-day mortality from time of hospitalization.

Results

3,298 patients (median age 65 (IQR 53-77), 60% male). 423 receive corticosteroids at any point during hospitalization, 699 die within 28 days of hospitalization. Target trial emulation estimates corticosteroids to reduce 28-day mortality from 32.2% (95% CI 30.9-33.5) to 25.7% (24.5-26.9). This estimate is qualitatively identical to the WHO's RCT meta-analysis odds ratio of 0.66 (0.53-0.82)). Hazard ratios using methods comparable to current corticosteroid research range in size and direction from 0.50 (0.41-0.62) to 1.08 (0.80-1.47).

Conclusion and Relevance

Clinical research based on observational data can unveil true causal relationships; however, the correctness of these effect estimates requires designing the study and analyzing the data based on principles which are different from the current standard in clinical research.

Design and Synthesis of New Pyrimidine-Quinolone Hybrids as Novel hLDHA Inhibitors

A battery of novel pyrimidine-quinolone hybrids was designed by docking scaffold replacement as lactate dehydrogenase A (hLDHA) inhibitors. Structures with different linkers between the pyrimidine and quinolone scaffolds (10-21 and 24−31) were studied in silico, and those with the 2-aminophenylsulfide (U-shaped) and 4-aminophenylsulfide linkers (24−31) were finally selected. These new pyrimidine-quinolone hybrids (24−31)(a−c) were easily synthesized in good to excellent yields by a green catalyst-free microwave-assisted aromatic nucleophilic substitution reaction between 3-(((2/4-aminophenyl)thio)methyl)quinolin-2(1H)-ones 22/23(a−c) and 4-aryl-2-chloropyrimidines (1−4). The inhibitory activity against hLDHA of the synthesized hybrids was evaluated, resulting IC50 values of the U-shaped hybrids 24−27(a−c) much better than the ones of the 1,4-linked hybrids 28−31(a−c). From these results, a preliminary structure−activity relationship (SAR) was established, which enabled the design of novel 1,3-linked pyrimidine-quinolone hybrids (33−36)(a−c). Compounds 35(a−c), the most promising ones, were synthesized and evaluated, fitting the experimental results with the predictions from docking analysis. In this way, we obtained novel pyrimidine-quinolone hybrids (25a, 25b, and 35a) with good IC50 values (<20 μM) and developed a preliminary SAR.

A reply to "Relevant factors in the eutrophication of the Uruguay River and the Río Negro"

A recent paper by Beretta-Blanco and Carrasco-Letelier (2021) claims that agricultural eutrophication is not one of the main causes for cyanobacterial blooms in rivers and artificial reservoirs. By combining rivers of markedly different hydrological characteristics e.g., presence/absence and number of dams, river discharge and geological setting, the study speculates about the role of nutrients for modulating phytoplankton chlorophyll-a. Here, we identified serious flaws, from erratic and inaccurate data manipulation. The study did not define how erroneous original dataset values were treated, how the variables below the detection/quantification limit were numerically introduced, lack of mandatory variables for river studies such as flow and rainfall, arbitrary removal of pH > 7.5 values (which were not outliers), and finally how extreme values of other environmental variables were included. In addition, we identified conceptual and procedural mistakes such as biased construction/evaluation of model prediction capability. The study trained the model using pooled data from a short restricted lotic section of the (large) Uruguay River and from both lotic and reservoir domains of the Negro River, but then tested predictability within the (small) Cuareim River. Besides these methodological considerations, the article shows misinterpretations of the statistical correlation of cause and effect neglecting basic limnological knowledge of the ecology of harmful algal blooms (HABs) and international research on land use effects on freshwater quality. The argument that pH is a predictor variable for HABs neglects overwhelming basic paradigms of carbon fluxes and change in pH because of primary productivity. As a result, the article introduces the notion that HABs formation are not related to agricultural land use and water residence time and generate a great risk for the management of surface waterbodies. This reply also emphasizes the need for good practices of open data management, especially for public databases in view of external reproducibility.

Using Mobile Integrated Health and telehealth to support transitions of care among patients with heart failure (MIGHTy-Heart): protocol for a pragmatic randomised controlled trial

INTRODUCTION

Nearly one-quarter of patients discharged from the hospital with heart failure (HF) are readmitted within 30 days, placing a significant burden on patients, families and health systems. The objective of the 'Using Mobile Integrated Health and Telehealth to support transitions of care among patients with Heart failure' (MIGHTy-Heart) study is to compare the effectiveness of two postdischarge interventions on healthcare utilisation, patient-reported outcomes and healthcare quality among patients with HF.

METHODS AND ANALYSIS

The MIGHTy-Heart study is a pragmatic comparative effectiveness trial comparing two interventions demonstrated to improve the hospital to home transition for patients with HF: mobile integrated health (MIH) and transitions of care coordinators (TOCC). The MIH intervention bundles home visits from a community paramedic (CP) with telehealth video visits by emergency medicine physicians to support the management of acute symptoms and postdischarge care coordination. The TOCC intervention consists of follow-up phone calls from a registered nurse within 48-72 hours of discharge to assess a patient's clinical status, identify unmet clinical and social needs and reinforce patient education (eg, medication adherence and lifestyle changes). MIGHTy-Heart is enrolling and randomising (1:1) 2100 patients with HF who are discharged to home following a hospitalisation in two New York City (NY, USA) academic health systems. The coprimary study outcomes are all-cause 30-day hospital readmissions and quality of life measured with the Kansas City Cardiomyopathy Questionnaire 30 days after hospital discharge. The secondary endpoints are days at home, preventable emergency department visits, unplanned hospital admissions and patient-reported symptoms. Data sources for the study outcomes include patient surveys, electronic health records and claims submitted to Medicare and Medicaid.

ETHICS AND DISSEMINATION

All participants provide written or verbal informed consent prior to randomisation in English, Spanish, French, Mandarin or Russian. Study findings are being disseminated to scientific audiences through peer-reviewed publications and presentations at national and international conferences. This study has been approved by: Biomedical Research Alliance of New York (BRANY #20-08-329-380), Weill Cornell Medicine Institutional Review Board (20-08022605) and Mt. Sinai Institutional Review Board (20-01901).

TRIAL REGISTRATION NUMBER

Clinicaltrials.gov, NCT04662541.

Excessive release of inorganic phosphate by ALS/FTD astrocytes causes non-cell-autonomous toxicity to motoneurons

Non-cell-autonomous mechanisms contribute to neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), in which astrocytes release unidentified factors that are toxic to motoneurons (MNs). We report here that mouse and patient iPSC-derived astrocytes with diverse ALS/FTD-linked mutations (SOD1, TARDBP, and C9ORF72) display elevated levels of intracellular inorganic polyphosphate (polyP), a ubiquitous, negatively charged biopolymer. PolyP levels are also increased in astrocyte-conditioned media (ACM) from ALS/FTD astrocytes. ACM-mediated MN death is prevented by degrading or neutralizing polyP in ALS/FTD astrocytes or ACM. Studies further reveal that postmortem familial and sporadic ALS spinal cord sections display enriched polyP staining signals and that ALS cerebrospinal fluid (CSF) exhibits increased polyP concentrations. Our in vitro results establish excessive astrocyte-derived polyP as a critical factor in non-cell-autonomous MN degeneration and a potential therapeutic target for ALS/FTD. The CSF data indicate that polyP might serve as a new biomarker for ALS/FTD.