In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverse-probability-of-treatment weighted estimators.

Standard methods for survival analysis, such as the time-dependent Cox model, may produce biased effect estimates when there exist time-dependent confounders that are themselves affected by previous treatment or exposure. Marginal structural models are a new class of causal models the parameters of which are estimated through inverse-probability-of-treatment weighting; these models allow for appropriate adjustment for confounding. We describe the marginal structural Cox proportional hazards model and use it to estimate the causal effect of zidovudine on the survival of human immunodeficiency virus-positive men participating in the Multicenter AIDS Cohort Study. In this study, CD4 lymphocyte count is both a time-dependent confounder of the causal effect of zidovudine on survival and is affected by past zidovudine treatment. The crude mortality rate ratio (95% confidence interval) for zidovudine was 3.6 (3.0-4.3), which reflects the presence of confounding. After controlling for baseline CD4 count and other baseline covariates using standard methods, the mortality rate ratio decreased to 2.3 (1.9-2.8). Using a marginal structural Cox model to control further for time-dependent confounding due to CD4 count and other time-dependent covariates, the mortality rate ratio was 0.7 (95% conservative confidence interval = 0.6-1.0). We compare marginal structural models with previously proposed causal methods.

Even in the absence of unmeasured confounding factors or model misspeci cation, standard methods for estimating the causal effect of time-varying treatments on survival are biased when (a) there exists a time-dependent risk factor for survival that also predicts subsequent treatment, and (b) past treatment history predicts subsequent risk factor level. In contrast, methods based on marginal structural models (MSMs) can provide consistent estimates of causal effects when unmeasured confounding and model misspeci cation are absent. MSMs are a new class of causal models whose parameters are estimated using a new class of estimators-inverse-probability-of-treatment weighted estimators. We use a marginal structural Cox proportional hazards model to estimate the joint effect of zidovudine (AZT) and prophylaxis therapy for Pneumocystis carinii pneumonia on the survival of H IV-positive men in the Multicenter A IDS Cohort Study, an observational study of homosexual men. We obtained an estimated causal mortality rate (hazard) ratio of .67 (conservative 95% con dence interval .46-.98) for AZT and of 1.14 (.79, 1.64) for prophylaxis therapy. These estimates will be consistent for the true causal rate ratios when the functional forms chosen for our models are correct and data have been obtained on all time-independent and time-dependent covariates that predict both subsequent treatment and mortality.

Even in the absence of unmeasured confounding factors or model misspecification, standard methods for estimating the causal effect of a time-varying treatment on the mean of a repeated measures outcome (for example, GEE regression) may be biased when there are time-dependent variables that are simultaneously confounders of the effect of interest and are predicted by previous treatment. In contrast, the recently developed marginal structural models (MSMs) can provide consistent estimates of causal effects when unmeasured confounding and model misspecification are absent. We describe an MSM for repeated measures that parameterizes the marginal means of counterfactual outcomes corresponding to prespecified treatment regimes. The parameters of MSMs are estimated using a new class of estimators - inverse-probability of treatment weighted estimators. We used an MSM to estimate the effect of zidovudine therapy on mean CD4 count among HIV-infected men in the Multicenter AIDS Cohort Study. We estimated a potential expected increase of 5.4 (95 per cent confidence interval -1.8,12.7) CD4 lymphocytes/l per additional study visit while on zidovudine therapy. We also explain the theory and implementation of MSMs for repeated measures data and draw upon a simple example to illustrate the basic ideas.

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Objective To assess the effectiveness of parent-only vs family-based interventions for pediatric weight management in underserved rural settings. Design A 3-arm randomized controlled clinical trial. Setting All sessions were conducted at Cooperative Extension Service offices in underserved rural counties. Participants Ninety-three overweight or obese children (8–14 years old) and their parent(s). Intervention Families were randomized to (1) a behavioral family-based intervention, (2) a behavioral parent-only intervention, or (3) a wait-list control group. Outcome Measure The primary outcome measure was change in children’s standardized body mass index (BMI). Results Seventy-one children completed posttreatment (month 4) and follow-up (month 10) assessments. At the month 4 assessment, children in the parent-only intervention demonstrated a greater decrease in BMI z score (mean difference [MD], 0.127; 95% confidence interval [CI], 0.027 to 0.226) than children in the control condition. No significant difference was found between the family-based intervention and the control condition (MD, 0.065; 95% CI, −0.027 to 0.158). At month 10 follow-up, children in the parent-only and family-based intervention groups demonstrated greater decreases in BMI z score from before treatment compared with those in the control group (MD, 0.115; 95% CI, 0.003 to 0.220; and MD, 0.136; 95% CI, 0.018 to 0.254, respectively). No difference was found in weight status change between the parent-only and family-based interventions at either assessment. Conclusions A parent-only intervention may be a viable and effective alternative to family-based treatment of childhood overweight. Cooperative Extension Service offices have the potential to serve as effective venues for the dissemination of obesity-related health promotion programs.

BACKGROUND Cancer and sepsis have surprisingly similar immunologic responses and equally dismal long term consequences. In cancer, increased myeloid-derived suppressor cells (MDSCs) induce detrimental immunosuppression, but little is known about the role of MDSCs after sepsis. Based on our chronic sepsis animal models, we hypothesized that after sepsis in humans, MDSCs will be persistently increased, functionally immunosuppressive, and associated with adverse clinical outcomes. METHODS Blood was obtained from 74 patients within 12 hours of severe sepsis/septic shock (SS/SS), and at set intervals out to 28 days, as well as in 18 healthy controls. MDSCs were phenotyped for cell surface receptor expression and enriched by cell sorting. Functional and genome-wide expression analyses were performed. Multiple logistic regression analysis was conducted to determine if increased MDSC appearance was associated with in-hospital and long-term outcomes. RESULTS After SS/SS, CD33+CD11b+HLA-DR−/low MDSCs were dramatically increased out to 28 days (p<0.05). When co-cultured with MDSCs from SS/SS patients, antigen-driven T-cell proliferation and TH1/TH2 cytokine production were suppressed (p<0.05). Additionally, septic MDSCs had suppressed HLA gene expression and upregulated ARG1 expression (p<0.05). Finally, SS/SS patients with persistent increased percentages of blood MDSCs had increased nosocomial infections, prolonged ICU stays, and poor functional status at discharge (p<0.05). CONCLUSION After SS/SS in humans, circulating MDSCs are persistently increased, functionally immunosuppressive, and associated with adverse outcomes. This novel observation warrants further studies. As observed in cancer immunotherapy, MDSCs could be a novel component in multimodality immunotherapy targeting detrimental inflammation and immunosuppression after SS/SS to improve currently observed dismal long-term outcomes.

This paper provides a brief overview to four major types of causal models for health-sciences research: Graphical models (causal diagrams), potential-outcome (counterfactual) models, sufficient-component cause models, and structural-equations models. The paper focuses on the logical connections among the different types of models and on the different strengths of each approach. Graphical models can illustrate qualitative population assumptions and sources of bias not easily seen with other approaches; sufficient-component cause models can illustrate specific hypotheses about mechanisms of action; and potential-outcome and structural-equations models provide a basis for quantitative analysis of effects. The different approaches provide complementary perspectives, and can be employed together to improve causal interpretations of conventional statistical results.