When analyzing longitudinal data, it is essential to account both for the correlation inherent from the repeated measures of the responses as well as the correlation realized on account of the feedback created between the responses at a particular time and the predictors at other times. As such one can analyze these data using generalized estimating equation with the independent working correlation. However, because it is essential to include all the appropriate moment conditions as you solve for the regression coefficients, we explore an alternative approach using a generalized method of moments for estimating the coefficients in such data. We develop an approach that makes use of all the valid moment conditions necessary with each time-dependent and time-independent covariate. This approach does not assume that feedback is always present over time, or if present occur at the same degree. Further, we make use of continuously updating generalized method of moments in obtaining estimates. We fit the generalized method of moments logistic regression model with time-dependent covariates using SAS PROC IML and also in R. We used p-values adjusted for multiple correlated tests to determine the appropriate moment conditions for determining the regression coefficients. We examined two datasets for illustrative purposes. We looked at re-hospitalization taken from a Medicare database. We also revisited data regarding the relationship between the body mass index and future morbidity among children in the Philippines. We conducted a simulated study to compare the performances of extended classifications.

Immune checkpointty inhibitors (ICIs), particularly those targeting programmed death 1 (PD-1) and anti-programmed death ligand 1 (PD-L1), enhance the antitumor effect by restoring the function of the inhibited effector T cells and produce durable responses in a large variety of metastatic and late patients with non-small-cell lung cancer. Although often well tolerated, the activation of the immune system results in side effects known as immune-related adverse events (irAEs), which can affect multiple organ systems, including the lungs. The occurrence of severe pulmonary irAEs, especially checkpoint inhibitor pneumonitis (CIP), is rare but has extremely high mortality and often overlaps with the respiratory symptoms and imaging of primary tumors. The development of CIP may be accompanied by radiation pneumonia and infectious pneumonia, leading to the simultaneous occurrence of a mixture of several types of inflammation in the lungs. However, there is a lack of authoritative diagnosis, grading criteria and clarified mechanisms of CIP. In this article, we review the incidence and median time to onset of CIP in patients with non-small-cell lung cancer treated with PD-1/PD-L1 blockade in clinical studies. We also summarize the clinical features, potential mechanisms, management and predictive biomarkers of CIP caused by PD-1/PD-L1 blockade in non-small-cell lung cancer treatment.

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We group approaches to modeling correlated binary data according to data recorded cross-sectionally as opposed to data recorded longitudinally; according to models that are population-averaged as opposed to subject-specific; and according to data with time-dependent covariates as opposed to time-independent covariates. Standard logistic regression models are appropriate for cross-sectional data. However, for longitudinal data, methods such as generalized estimating equations (GEE) and generalized method of moments (GMM) are commonly used to fit population-averaged models, while random-effects models such as generalized linear mixed models (GLMM) are used to fit subject-specific models. Some of these methods account for time-dependence in covariates while others do not. This paper addressed these approaches with an illustration using a Medicare dataset as it relates to rehospitalization. In particular, we compared results from standard logistic models, GEE models, GMM models, and random-effects models by analyzing a binary outcome for four successive hospitalizations. We found that these procedures address differently the correlation among responses and the feedback from response to covariate. We found marginal GMM logistic regression models to be more appropriate when covariates are classified as time-dependent in comparison to GEE models. We also found conditional random-intercept models with time-dependent covariates decomposed into components to be more appropriate when time-dependent covariates are present in comparison to ordinary random-effects models. We used the SAS procedures GLIMMIX, NLMIXED, IML, GENMOD, and LOGISTIC to analyze the illustrative dataset, as well as unique programs written using the R language.

Benign prostatic hyperplasia (BPH) occurs most commonly among older men, often accompanied by chronic tissue inflammation. Although its aetiology remains unclear, autoimmune dysregulation may contribute to BPH. Regulatory T cells (Tregs) prevent autoimmune responses and maintain immune homeostasis. In this study, we aimed to investigate Tregs frequency, phenotype, and function in BPH patients and to evaluate adoptive transfer Tregs for immunotherapy in mice with BPH via CD39. Prostate specimens and peripheral blood from BPH patients were used to investigate Treg subsets, phenotype and Treg-associated cytokine production. Sorted CD39 +/− Tregs from healthy mice were adoptively transferred into mice before or after testosterone propionate administration. The Tregs percentage in peripheral blood from BPH patients was attenuated, exhibiting low Foxp3 and CD39 expression with low levels of serum IL-10, IL-35 and TGF-β. Immunohistochemistry revealed Foxp3+ cells were significantly diminished in BPH prostate with severe inflammatory. Although the Tregs subset was comprised of more effector/memory Tregs, CD39 was still downregulated on effector/memory Tregs in BPH patients. Before or after testosterone propionate administration, no alterations of BPH symptoms were observed due to CD39-Tregs in mice, however, CD39 + Tregs existed more potency than Tregs to regulate prostatic hyperplasia and inhibit inflammation by decreasing IL-1β and PSA secretion, and increasing IL-10 and TGF-β secretion. Furthermore, adoptive transfer with functional Tregs not only improved prostate hyperplasia but also regulated muscle cell proliferation in bladder. Adoptive transfer with Tregs may provide a novel method for the prevention and treatment of BPH clinically.