Nonparametric analysis of delayed treatment effects using single-crossing constraints.

Clinical trials involving novel immuno-oncology therapies frequently exhibit survival profiles which violate the proportional hazards assumption due to a delay in treatment effect, and, in such settings, the survival curves in the two treatment arms may have a crossing before the two curves eventually separate. To flexibly model such scenarios, we describe a nonparametric approach for estimating the treatment arm-specific survival functions which constrains these two survival functions to cross at most once without making any additional assumptions about how the survival curves are related. A main advantage of our approach is that it provides an estimate of a crossing time if such a crossing exists, and, moreover, our method generates interpretable measures of treatment benefit including crossing-conditional survival probabilities and crossing-conditional estimates of restricted residual mean life. Our estimates of these measures may be used together with efficacy measures from a primary analysis to provide further insight into differences in survival across treatment arms. We demonstrate the use and effectiveness of our approach with a large simulation study and an analysis of reconstructed outcomes from a recent combination therapy trial.

Clinical implications of AR alterations in advanced prostate cancer: a multi-institutional collaboration.

BACKGROUND: AR gene alterations can develop in response to pressure of testosterone suppression and androgen receptor targeting agents (ARTA). Despite this, the relevance of these gene alterations in the context of ARTA treatment and clinical outcomes remains unclear. METHODS: Patients with castration-resistant prostate cancer (CRPC) who had undergone genomic testing and received ARTA treatment were identified in the Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) database. Patients were stratified according to the timing of genomic testing relative to the first ARTA treatment (pre-/post-ARTA). Clinical outcomes such as time to progression, PSA response, and overall survival were compared based on alteration types. RESULTS: In total, 540 CRPC patients who received ARTA and had tissue-based (n = 321) and/or blood-based (n = 244) genomic sequencing were identified. Median age was 62 years (range 39-90) at the time of the diagnosis. Majority were White (72.2%) and had metastatic disease (92.6%) at the time of the first ARTA treatment. Pre-ARTA genomic testing was available in 24.8% of the patients, and AR mutations and amplifications were observed in 8.2% and 13.1% of the patients, respectively. Further, time to progression was longer in patients with AR amplifications (25.7 months) compared to those without an AR alteration (9.6 months; p = 0.03). In the post-ARTA group (n = 406), AR mutations and AR amplifications were observed in 18.5% and 35.7% of the patients, respectively. The most common mutation in post-ARTA group was L702H (9.9%). CONCLUSION: In this real-world clinicogenomics database-driven study we explored the development of AR alterations and their association with ARTA treatment outcomes. Our study showed that AR amplifications are associated with longer time to progression on first ARTA treatment. Further prospective studies are needed to optimize therapeutic strategies for patients with AR alterations.

Biomarker-Directed Therapy in Black and White Men With Metastatic Castration-Resistant Prostate Cancer.

Importance: Black men have higher incidence and mortality from prostate cancer. Whether precision oncology disparities affect Black men with metastatic castration-resistant prostate cancer (mCRPC) is unknown. Objective: To compare precision medicine data and outcomes between Black and White men with mCRPC. Design, Setting, and Participants: This retrospective cohort study used data collected by the Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) consortium, a multi-institutional registry with linked clinicogenomic data, from April 2020 to December 2021. Participants included Black and White patients with mCRPC with molecular data. Data were analyzed from December 2021 to May 2023. Exposures: Database-reported race and ethnicity. Main Outcomes and Measures: The primary outcome was the frequency of actionable molecular data, defined as the presence of mismatch repair deficiency (MMRD) or high microsatellite instability (MSI-H), homologous recombination repair deficiency, or tumor mutational burden of 10 mutations per megabase or greater. Secondary outcomes included the frequency of other alterations, the type and timing of genomic testing performed, and use of targeted therapy. Efficacy outcomes were prostate-specific antigen response rate, site-reported radiographic response, and overall survival. Results: A total of 962 eligible patients with mCRPC were identified, including 204 Black patients (21.2%; median [IQR] age at diagnosis, 61 [55-67] years; 131 patients [64.2%] with Gleason scores 8-10; 92 patients [45.1%] with de novo metastatic disease) and 758 White patients (78.8%; median [IQR] age, 63 [57-69] years; 445 patients [58.7%] with Gleason scores 8-10; 310 patients [40.9%] with de novo metastatic disease). Median (IQR) follow-up from mCRPC was 26.6 (14.2-44.7) months. Blood-based molecular testing was more common in Black men (111 men [48.7%]) than White men (317 men [36.4%]; P < .001). Rates of actionable alterations were similar between groups (65 Black men [32.8%]; 215 White men [29.1%]; P = .35), but MMRD or MSI-H was more common in Black men (18 men [9.1]) than White men (36 men [4.9%]; P = .04). PTEN alterations were less frequent in Black men than White men (31 men [15.7%] vs 194 men [26.3%]; P = .003), as were TMPRSS alterations (14 men [7.1%] vs 155 men [21.0%]; P < .001). No other differences were seen in the 15 most frequently altered genes, including TP53, AR, CDK12, RB1, and PIK3CA. Matched targeted therapy was given less frequently in Black men than White men (22 men [33.5%] vs 115 men [53.5%]; P = .008). There were no differences in response to targeted therapy or survival between the two cohorts. Conclusions and Relevance: This cohort study of men with mCRPC found higher frequency of MMRD or MSI-H and lower frequency of PTEN and TMPRSS alterations in Black men compared with White men. Although Black men received targeted therapy less frequently than White men, no differences were observed in clinical outcomes.

Effects of CYP3A4 and CYP2C9 genotype on systemic anastrozole and fulvestrant concentrations in SWOG S0226.

Objective & methods: This study tested associations of genotype-predicted activity of CYP3A4, other pharmacogenes, SLC28A7 (rs11648166) and ALPPL2 (rs28845026) with systemic concentrations of the endocrine therapies anastrozole and fulvestrant in SWOG S0226 trial participants. Results: Participants in the anastrozole-only arm with low CYP3A4 activity (i.e. CYP3A4*22 carriers) had higher systemic anastrozole concentrations than patients with high CYP3A4 activity (ß-coefficient = 10.03; 95% CI: 1.42, 18.6; p = 0.025). In an exploratory analysis, participants with low CYP2C9 activity had lower anastrozole concentrations and higher fulvestrant concentrations than participants with high CYP2C9 activity. Conclusion: Inherited genetic variation in CYP3A4 and CYP2C9 may affect concentrations of endocrine therapy and may be useful to personalize dosing and improve treatment outcomes.

Impaired in vitro fertilization outcomes following testosterone treatment improve with washout in a mouse model of gender-affirming hormone treatment.

BACKGROUND: The impact of gender-affirming testosterone on fertility is poorly understood, with ovarian histopathologic studies showing variable results, some with a detrimental effect on reproductive capacity and uncertain reversibility. Assisted reproductive outcome data are restricted to small case series that lack the ability to inform clinical practice guidelines and limit fertility preservation counseling for transgender and nonbinary individuals. OBJECTIVE: This study aimed to determine the impact of current testosterone and testosterone washout on in vitro fertilization outcomes in a mouse model for gender-affirming hormone treatment. We hypothesized that current or previous testosterone treatment would not affect in vitro fertilization outcomes. STUDY DESIGN: C57BL/6N female mice (n=120) were assigned to 4 treatment groups: (1) current control, (2) current testosterone, (3) control washout, and (4) testosterone washout. Testosterone implants remained in situ for 6 or 12 weeks, representing the short- and long-term treatment arms, respectively. Current treatment groups underwent ovarian stimulation with implants in place, and washout treatment groups were explanted and had ovarian stimulation after 2 weeks. Oocytes were collected, fertilized, and cultured in vitro, with one arm continuing to the blastocyst stage and the other having transfer of cleavage-stage embryos. Statistical analysis was performed using GraphPad Prism, version 9.0 and R statistical software, version 4.1.2, with statistical significance defined by P<.05. RESULTS: Current long-term testosterone treatment impaired in vitro fertilization outcomes, with fewer mature oocytes retrieved (13.7±5.1 [standard deviation] vs 28.6±7.8 [standard deviation]; P<.0001) leading to fewer cleavage-stage embryos (12.1±5.1 vs 26.5±8.2; P<.0001) and blastocysts (10.0±3.2 vs 25.0±6.5; P<.0001). There was recovery of in vitro fertilization outcomes following washout in the short-term treatment cohort, with incomplete reversibility in the long-term cohort. Testosterone did not negatively affect maturity, fertilization, or blastulation rates. CONCLUSION: In a mouse model of gender-affirming hormone treatment, testosterone negatively affected oocyte yield without affecting oocyte quality. Our findings suggest that testosterone reversibility is duration-dependent. These results demonstrate the feasibility of in vitro fertilization without testosterone discontinuation while supporting a washout period for optimization of mature oocyte yield.

Computational and Experimental Evidence for Templated Macrocyclization: The Role of a Hydrogen Bond Network in the Quantitative Dimerization of 24-Atom Macrocycles.

In the absence of preorganization, macrocyclization reactions are often plagued by oligomeric and polymeric side products. Here, a network of hydrogen bonds was identified as the basis for quantitative yields of macrocycles derived from the dimerization of monomers. Oligomers and polymers were not observed. Macrocyclization, the result of the formation of two hydrazones, was hypothesized to proceed in two steps. After condensation to yield the monohydrazone, a network of hydrogen bonds formed to preorganize the terminal acetal and hydrazine groups for cyclization. Experimental evidence for preorganization derived from macrocycles and acyclic models. Solution NMR spectroscopy and single-crystal X-ray diffraction revealed that the macrocycles isolated from the cyclization reaction were protonated twice. These protons contributed to an intramolecular network of hydrogen bonds that engaged distant carbonyl groups to realize a long-range order. DFT calculations showed that this network of hydrogen bonds contributed 8.7 kcal/mol to stability. Acyclic models recapitulated this network in solution. Condensation of an acetal and a triazinyl hydrazine, which adopted a number of conformational isomers, yielded a hydrazone that adopted a favored rotamer conformation in solution. The critical hydrogen-bonded proton was also evident. DFT calculations of acyclic models showed that the rotamers were isoenergetic when deprotonated. Upon protonation, however, energies diverged with one low-energy rotamer adopting the conformation observed in the macrocycle. This conformation anchored the network of hydrogen bonds of the intermediate. Computation revealed that the hydrogen-bonded network in the acyclic intermediate contributed up to 14 kcal/mol of stability and preorganized the acetal and hydrazine for cyclization.

Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models.

Individuals often respond differently to identical treatments, and characterizing such variability in treatment response is an important aim in the practice of personalized medicine. In this article, we describe a nonparametric accelerated failure time model that can be used to analyze heterogeneous treatment effects (HTE) when patient outcomes are time-to-event. By utilizing Bayesian additive regression trees and a mean-constrained Dirichlet process mixture model, our approach offers a flexible model for the regression function while placing few restrictions on the baseline hazard. Our nonparametric method leads to natural estimates of individual treatment effect and has the flexibility to address many major goals of HTE assessment. Moreover, our method requires little user input in terms of model specification for treatment covariate interactions or for tuning parameter selection. Our procedure shows strong predictive performance while also exhibiting good frequentist properties in terms of parameter coverage and mitigation of spurious findings of HTE. We illustrate the merits of our proposed approach with a detailed analysis of two large clinical trials (N = 6769) for the prevention and treatment of congestive heart failure using an angiotensin-converting enzyme inhibitor. The analysis revealed considerable evidence for the presence of HTE in both trials as demonstrated by substantial estimated variation in treatment effect and by high proportions of patients exhibiting strong evidence of having treatment effects which differ from the overall treatment effect.

AR(1) latent class models for longitudinal count data.

In a variety of applications involving longitudinal or repeated-measurements data, it is desired to uncover natural groupings or clusters that exist among study subjects. Motivated by the need to recover clusters of longitudinal trajectories of conduct problems in the field of developmental psychopathology, we propose a method to address this goal when the response data in question are counts. We assume the subject-specific observations are generated from a first-order autoregressive process that is appropriate for count data. A key advantage of our approach is that the class-specific likelihood function arising from each subject's data can be expressed in closed form, circumventing common computational issues associated with random effects models. To further improve computational efficiency, we propose an approximate EM procedure for estimating the model parameters where, within each EM iteration, the maximization step is approximated by solving an appropriately chosen set of estimating equations. We explore the effectiveness of our procedures through simulations based on a four-class model, placing a special emphasis on recovery of the latent trajectories. Finally, we analyze data and recover trajectories of conduct problems in an important nationally representative sample. The methods discussed here are implemented in the R package inarmix, which is available from the Comprehensive R Archive Network (http://cran.r-project.org).

Considerations when assessing heterogeneity of treatment effect in patient-centered outcomes research.

When baseline risk of an outcome varies within a population, the effect of a treatment on that outcome will vary on at least one scale (e.g., additive, multiplicative). This treatment effect heterogeneity is of interest in patient-centered outcomes research. Based on a literature review and solicited expert opinion, we assert the following: (1) Treatment effect heterogeneity on the additive scale is most interpretable to health-care providers and patients using effect estimates to guide treatment decision-making; heterogeneity reported on the multiplicative scale may be misleading as to the magnitude or direction of a substantively important interaction. (2) The additive scale may give clues about sufficient-cause interaction, although such interaction is typically not relevant to patients' treatment choices. (3) Statistical modeling need not be conducted on the same scale as results are communicated. (4) Statistical testing is one tool for investigations, provided important subgroups are identified a priori, but test results should be interpreted cautiously given nonequivalence of statistical and clinical significance. (5) Qualitative interactions should be evaluated in a prespecified manner for important subgroups. Principled analytic plans that take into account the purpose of investigation of treatment effect heterogeneity are likely to yield more useful results for guiding treatment decisions.

Logistic Regression Likelihood Ratio Test Analysis for Detecting Signals of Adverse Events in Post-market Safety Surveillance.

The Vaccine Adverse Event Reporting System (VAERS) and other product surveillance systems compile reports of product-associated adverse events (AEs), and these reports may include a wide range of information including age, gender, and concomitant vaccines. Controlling for possible confounding variables such as these is an important task when utilizing surveillance systems to monitor post-market product safety. A common method for handling possible confounders is to compare observed product-AE combinations with adjusted baseline frequencies where the adjustments are made by stratifying on observable characteristics. Though approaches such as these have proven to be useful, in this article we propose a more flexible logistic regression approach which allows for covariates of all types rather than relying solely on stratification. Indeed, a main advantage of our approach is that the general regression framework provides flexibility to incorporate additional information such as demographic factors and concomitant vaccines. As part of our covariate-adjusted method, we outline a procedure for signal detection that accounts for multiple comparisons and controls the overall Type 1 error rate. To demonstrate the effectiveness of our approach, we illustrate our method with an example involving febrile convulsion, and we further evaluate its performance in a series of simulation studies.

Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research.

Evaluation of heterogeneity of treatment effect (HTE) is an essential aspect of personalized medicine and patient-centered outcomes research. Our goal in this article is to promote the use of Bayesian methods for subgroup analysis and to lower the barriers to their implementation by describing the ways in which the companion software beanz can facilitate these types of analyses. To advance this goal, we describe several key Bayesian models for investigating HTE and outline the ways in which they are well-suited to address many of the commonly cited challenges in the study of HTE. Topics highlighted include shrinkage estimation, model choice, sensitivity analysis, and posterior predictive checking. A case study is presented in which we demonstrate the use of the methods discussed.

Making the cut: improved ranking and selection for large-scale inference.

Identifying leading measurement units from a large collection is a common inference task in various domains of large-scale inference. Testing approaches, which measure evidence against a null hypothesis rather than effect magnitude, tend to overpopulate lists of leading units with those associated with low measurement error. By contrast, local maximum likelihood (ML) approaches tend to favor units with high measurement error. Available Bayesian and empirical Bayesian approaches rely on specialized loss functions that result in similar deficiencies. We describe and evaluate a generic empirical Bayesian ranking procedure that populates the list of top units in a way that maximizes the expected overlap between the true and reported top lists for all list sizes. The procedure relates unit-specific posterior upper tail probabilities with their empirical distribution to yield a ranking variable. It discounts high-variance units less than popular non-ML methods and thus achieves improved operating characteristics in the models considered.

Fast Pure R Implementation of GEE: Application of the Matrix Package.

Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, geeM, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the Matrix package enable a fast implementation. To gain speed, we make use of analytic inverses of the working correlation when possible and a trick to find quick numeric inverses when an analytic inverse is not available. Through three examples, we demonstrate the speed of geeM, which is not much worse than C implementations like geepack and gee on small data sets and faster on large data sets.