Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals

Clinical characteristics and outcomes of critically ill mechanically ventilated COVID-19 patients receiving interleukin-6 receptor antagonists and corticosteroid therapy: a preliminary report from a multinational registry

BACKGROUND

Interleukin-6 receptor antagonists (IL-6RAs) and steroids are emerging immunomodulatory therapies for severe and critical coronavirus disease (COVID-19). In this preliminary report, we aim to describe the epidemiology, clinical characteristics, and outcomes of adult critically ill COVID-19 patients, requiring invasive mechanical ventilation (iMV), and receiving IL-6RA and steroids therapy over the last 11 months.

MATERIALS AND METHODS

International, multicenter, cohort study derived from Viral Infection and Respiratory Illness University Study registry and conducted through Discovery Network, Society of Critical Care Medicine. Data were collected between March 01, 2020, and January 10, 2021.

RESULTS

Of 860 patients who met eligibility criteria, 589 received steroids, 170 IL-6RAs, and 101 combinations. Patients who received IL-6RAs were younger (median age of 57.5 years vs. 61.1 and 61.8 years in the steroids and combination groups, respectively). The median C-reactive protein level was > 75 mg/L, indicating a hyperinflammatory phenotype. The median daily steroid dose was 7.5 mg dexamethasone or equivalent (interquartile range: 6-14 mg); 80.8% and 19.2% received low-dose and high-dose steroids, respectively. Of the patients who received IL-6RAs, the majority received one dose of tocilizumab and sarilumab (dose range of 600-800 mg for tocilizumab and 200-400 mg for sarilumab). Regarding the timing of administration, we observed that steroid and IL-6RA administration on day 0 of ICU admission was only 55.6% and 39.5%, respectively. By day 28, when compared with steroid use alone, IL-6RA use was associated with an adjusted incidence rate ratio (aIRR) of 1.12 (95% confidence interval [CI] 0.88, 1.4) for ventilator-free days, while combination therapy was associated with an aIRR of 0.83 (95% CI 0.6, 1.14). IL-6RA use was associated with an adjusted odds ratio (aOR) of 0.68 (95% CI 0.44, 1.07) for the 28-day mortality rate, while combination therapy was associated with an aOR of 1.07 (95% CI 0.67, 1.70). Liver dysfunction was higher in IL-6RA group (p = 0.04), while the bacteremia rate did not differ among groups.

CONCLUSIONS

Discordance was observed between the registry utilization patterns (i.e., timing of steroids and IL-6RA administration) and new evidence from the recent randomized controlled trials and guideline recommendations. These data will help us to identify areas of improvement in prescribing patterns and enhance our understanding of IL-6RA safety with different steroid regimens. Further studies are needed to evaluate the drivers of hospital-level variation and their impact on clinical outcomes. Trial registration ClinicalTrials.gov: NCT04486521. Registered on July 2020.

Estimating Moderated Causal Effects with Time-varying Treatments and Time-varying Moderators: Structural Nested Mean Models and Regression with Residuals

Individuals differ in how they respond to a particular treatment or exposure, and social scientists are often interested in understanding how treatment effects are moderated by observed characteristics of individuals. Effect moderation occurs when individual covariates dampen or amplify the effect of some exposure. This article focuses on estimating moderated causal effects in longitudinal settings where both the treatment and effect moderator vary over time. Effect moderation is typically examined using covariate by treatment interactions in regression analyses, but in the longitudinal setting, this approach may be problematic because time-varying moderators of future treatment may be affected by prior treatment-for example, moderators may also be mediators-and naively conditioning on an outcome of treatment in a conventional regression model can lead to bias. This article introduces to sociology moderated intermediate causal effects and the structural nested mean model for analyzing effect moderation in the longitudinal setting. It discusses problems with conventional regression and presents a new approach to estimation that avoids these problems (regression-with-residuals). The method is illustrated using longitudinal data from the PSID to examine whether the effects of time-varying exposures to poor neighborhoods on the risk of adolescent childbearing are moderated by time-varying family income.

Propensity score weighting for a continuous exposure with multilevel data

Propensity score methods (e.g., matching, weighting, subclassification) provide a statistical approach for balancing dissimilar exposure groups on baseline covariates. These methods were developed in the context of data with no hierarchical structure or clustering. Yet in many applications the data have a clustered structure that is of substantive importance, such as when individuals are nested within healthcare providers or within schools. Recent work has extended propensity score methods to a multilevel setting, primarily focusing on binary exposures. In this paper, we focus on propensity score weighting for a continuous, rather than binary, exposure in a multilevel setting. Using simulations, we compare several specifications of the propensity score: a random effects model, a fixed effects model, and a single-level model. Additionally, our simulations compare the performance of marginal versus cluster-mean stabilized propensity score weights. In our results, regression specifications that accounted for the multilevel structure reduced bias, particularly when cluster-level confounders were omitted. Furthermore, cluster mean weights outperformed marginal weights.

Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals

This article considers the problem of examining time-varying causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and possible confounders are time varying. The structural nested mean model (SNMM) is used to specify the moderated time-varying causal effects of interest in a conditional mean model for a continuous response given time-varying treatments and moderators. We present an easy-to-use estimator of the SNMM that combines an existing regression-with-residuals (RR) approach with an inverse-probability-of-treatment weighting (IPTW) strategy. The RR approach has been shown to identify the moderated time-varying causal effects if the time-varying moderators are also the sole time-varying confounders. The proposed IPTW+RR approach provides estimators of the moderated time-varying causal effects in the SNMM in the presence of an additional, auxiliary set of known and measured time-varying confounders. We use a small simulation experiment to compare IPTW+RR versus the traditional regression approach and to compare small and large sample properties of asymptotic versus bootstrap estimators of the standard errors for the IPTW+RR approach. This article clarifies the distinction between time-varying moderators and time-varying confounders. We illustrate the methodology in a case study to assess if time-varying substance use moderates treatment effects on future substance use.

Estimating the causal effects of cumulative treatment episodes for adolescents using marginal structural models and inverse probability of treatment weighting

BACKGROUND

Substance use treatment is rarely a one-time event for individuals with substance use disorders. Sustained reductions in substance use and its related symptoms may result from multiple treatment episodes.

METHODS

We use a marginal structural model with inverse-probability-of-treatment weighting to estimate the causal effects of cumulative treatment experiences over a period of 9 months on drug use at the end of 1-year among 2870 adolescents receiving care in community-based treatment settings. During the 9 months, adolescents move in and out of outpatient and residential treatment with periods where they only receive biological drug screening (BDS) or no treatment at all. The use of inverse-probability-of-treatment weighting reduces confounding bias due to observed baseline and time-varying measures over the course of follow-up; weights were estimated using generalized boosted models.

RESULTS

Each additional period of treatment (representing at least one day, 1 session, or 1 BDS during the 90 day period between follow-up visits) yielded reductions in average substance use frequency at 1-year relative to no treatment during the 90-day period. For residential treatment it was a 16% decrease (95% CI=-27%, -7%), for outpatient treatment it was a 9% decrease (95% CI=-18%, -0%), and for BDS (with no additional outpatient or residential treatment) it was an 11% decrease (95% CI=-20%, -3%).

CONCLUSIONS

Using robust statistical methods, we find promising (albeit preliminary) evidence that additional periods of outpatient and residential treatment, as well as biological drug screening, lead to reductions in substance use outcomes at one year.