Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy.

Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen measurements. When prostate-specific antigen levels rise, salvage therapies are recommended in order to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients and when to initiate these salvage therapies. In this work, we use the University of Michigan Prostatectomy Registry Data to tackle this question. Due to the observational nature of this data, we face the challenge that prostate-specific antigen is simultaneously a time-varying confounder and an intermediate variable for salvage therapy. We define different causal salvage therapy effects defined conditionally on different specifications of the longitudinal prostate-specific antigen history. We then illustrate how these effects can be estimated using the framework of joint models for longitudinal and time-to-event data. All proposed methodology is implemented in the freely-available R package JMbayes2.

Robust data integration from multiple external sources for generalized linear models with binary outcomes.

We aim to estimate parameters in a generalized linear model (GLM) for a binary outcome when, in addition to the raw data from the internal study, more than 1 external study provides summary information in the form of parameter estimates from fitting GLMs with varying subsets of the internal study covariates. We propose an adaptive penalization method that exploits the external summary information and gains efficiency for estimation, and that is both robust and computationally efficient. The robust property comes from exploiting the relationship between parameters of a GLM and parameters of a GLM with omitted covariates and from downweighting external summary information that is less compatible with the internal data through a penalization. The computational burden associated with searching for the optimal tuning parameter for the penalization is reduced by using adaptive weights and by using an information criterion when searching for the optimal tuning parameter. Simulation studies show that the proposed estimator is robust against various types of population distribution heterogeneity and also gains efficiency compared to direct maximum likelihood estimation. The method is applied to improve a logistic regression model that predicts high-grade prostate cancer making use of parameter estimates from 2 external models.

Influence of acromioclavicular joint arthritis on outcomes after reverse total shoulder.

Background: Although substantial motion at the acromioclavicular joint (ACJ) occurs during overhead shoulder motion, the influence of ACJ arthritis on postoperative outcomes of patients undergoing reverse total shoulder arthroplasty (rTSA) is unclear. We assessed the influence of ACJ arthritis, defined by degenerative radiographic changes, and its severity on clinical outcomes after primary rTSA. Methods: We conducted a retrospective review of a prospectively collected shoulder arthroplasty database of patients that underwent primary rTSA with a minimum 2-year clinical follow-up. Imaging studies of included patients were evaluated to assess ACJ arthritis classified by radiographic degenerative changes of the ACJ; severity was based upon size and location of osteophytes. Both the Petersson classification and the King classification (a modified Petersson classification addressing superior osteophytes and size of the largest osteophyte) were used to evaluate the severity of degenerative ACJ radiographic changes. Severe ACJ arthritis was characterized by large osteophytes (≥2 mm). Active range of motion (ROM) in abduction, forward elevation, and external and internal rotation as well as clinical outcome scores (American Shoulder and Elbow Surgeons Shoulder, Constant, Shoulder Pain and Disability Index, simple shoulder test, University of California, Los Angeles scores) were assessed both preoperatively and at the latest follow-up; outcomes were compared based on severity of ACJ arthritis. Multivariable linear regression models were used to determine whether increasing severity of ACJ arthritis was associated with poorer outcomes. Results: A total of 341 patients were included with a mean age of 71 ± 8 years and 55% were female. The mean follow-up was 5.1 ± 2.4 years. Preoperatively, there were no differences in outcomes based on the severity of ACJ pathology. Postoperatively, there were no differences in outcomes based upon the severity of ACJ arthritis except for greater preoperative to postoperative improvement in active internal rotation in patients with normal or grade 1 ACJ arthritis vs. grade 2 and 3 (3 ± 2 vs. 1 ± 2 and 1 ± 3, P = .029). Patients with ACJ arthritis and osteophytes ≥2 mm had less favorable Shoulder Pain and Disability Index scores, corresponding to greater pain (-49.3 ± 21.5 vs. -41.3 ± 26.8, P = .015). On multivariable linear regression, increased severity of ACJ arthritis was not independently associated with poorer postoperative ROM or outcome scores. Conclusion: Overall, our results demonstrate that greater ACJ arthritis severity score is not associated with poorer outcome scores and has minimal effect on ROM. However, patients with the largest osteophytes (≥2 mm) did have slightly worse pain postoperatively. Radiographic presence of high-stage ACJ arthritis should not alter the decision to undergo rTSA.

Optimizing dynamic predictions from joint models using super learning.

Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions are the shape of the longitudinal trajectories and the functional form linking the longitudinal outcome history to the hazard of the event. Finding a single well-specified model that produces accurate predictions for all subjects and follow-up times can be challenging, especially when considering multiple longitudinal outcomes. In this work, we use the concept of super learning and avoid selecting a single model. In particular, we specify a weighted combination of the dynamic predictions calculated from a library of joint models with different specifications. The weights are selected to optimize a predictive accuracy metric using V-fold cross-validation. We use as predictive accuracy measures the expected quadratic prediction error and the expected predictive cross-entropy. In a simulation study, we found that the super learning approach produces results very similar to the Oracle model, which was the model with the best performance in the test datasets. All proposed methodology is implemented in the freely available R package JMbayes2.

Surrogacy validation for time-to-event outcomes with illness-death frailty models.

A common practice in clinical trials is to evaluate a treatment effect on an intermediate outcome when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate outcomes in a causally-valid way when the trial outcomes are time-to-event. Using counterfactual outcomes, those that would be observed if the counterfactual treatment had been given, the causal association paradigm assesses the relationship of the treatment effect on the surrogate outcome with the treatment effect on the true, primary outcome. In particular, we propose illness-death models to accommodate the censored and semicompeting risk structure of survival data. The proposed causal version of these models involves estimable and counterfactual frailty terms. Via these multistate models, we characterize what a valid surrogate would look like using a causal effect predictiveness plot. We evaluate the estimation properties of a Bayesian method using Markov chain Monte Carlo and assess the sensitivity of our model assumptions. Our motivating data source is a localized prostate cancer clinical trial where the two survival outcomes are time to distant metastasis and time to death.

Modeling basal body temperature data using horseshoe process regression.

Biomedical data often exhibit jumps or abrupt changes. For example, women's basal body temperature may jump at ovulation, menstruation, implantation, and miscarriage. These sudden changes make these data challenging to model: many methods will oversmooth the sharp changes or overfit in response to measurement error. We develop horseshoe process regression (HPR) to address this problem. We define a horseshoe process as a stochastic process in which each increment is horseshoe-distributed. We use the horseshoe process as a nonparametric Bayesian prior for modeling a potentially nonlinear association between an outcome and its continuous predictor, which we implement via Stan and in the R package HPR. We provide guidance and extensions to advance HPR's use in applied practice: we introduce a Bayesian imputation scheme to allow for interpolation at unobserved values of the predictor within the HPR; include additional covariates via a partial linear model framework; and allow for monotonicity constraints. We find that HPR performs well when fitting functions that have sharp changes. We apply HPR to model women's basal body temperatures over the course of the menstrual cycle.

Aspirin use and head and neck cancer survival and recurrence.

BACKGROUND: Head and neck cancer (HNC) has low 5-year survival, and evidence-based recommendations for tertiary prevention are lacking. Aspirin improves outcomes for cancers at other sites, but its role in HNC tertiary prevention remains understudied. METHODS: HNC patients were recruited in the University of Michigan Head and Neck Cancer Specialized Program of Research Excellence (SPORE) from 2003 to 2014. Aspirin data were collected through medical record review; outcomes (overall mortality, HNC-specific mortality, and recurrence) were collected through medical record review, Social Security Death Index, or LexisNexis. Cox proportional hazards models were used to evaluate the associations between aspirin use at diagnosis (yes/no) and HNC outcomes. RESULTS: We observed no statistically significant associations between aspirin and cancer outcome in our HNC patient cohort (n = 1161) (HNC-specific mortality: HR = 0.91, 95% CI = 0.68-1.21; recurrence: HR = 0.94, 95% CI = 0.73-1.19). In analyses stratified by anatomic site, HPV status, and disease stage, we observed no association in any strata examined with the possible exception of a lower risk of recurrence in oropharynx patients (HR = 0.60, 95% CI 0.35-1.04). CONCLUSIONS: Our findings do not support a protective association between aspirin use and cancer-specific death or recurrence in HNC patients, with the possible exception of a lower risk of recurrence in oropharynx patients.

Incorporating Covariates into Measures of Surrogate Paradox Risk.

Clinical trials often collect intermediate or surrogate endpoints other than their true endpoint of interest. It is important that the treatment effect on the surrogate endpoint accurately predicts the treatment effect on the true endpoint. There are settings in which the proposed surrogate endpoint is positively correlated with the true endpoint, but the treatment has opposite effects on the surrogate and true endpoints, a phenomenon labeled "surrogate paradox". Covariate information may be useful in predicting an individual's risk of surrogate paradox. In this work, we propose methods for incorporating covariates into measures of assessing the risk of surrogate paradox using the meta-analytic causal association framework. The measures calculate the probability that a treatment will have opposite effects on the surrogate and true endpoints and determine the size of a positive treatment effect on the surrogate endpoint that would reduce the risk of a negative treatment effect on the true endpoint as a function of covariates, allowing the effects of covariates on the surrogate and true endpoint to vary across trials.

Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models.

Analyzing the large-scale survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program may help guide the management of cancer. Detecting and characterizing the time-varying effects of factors collected at the time of diagnosis could reveal important and useful patterns. However, fitting a time-varying effect model by maximizing the partial likelihood with such large-scale survival data is not feasible with most existing software. Moreover, estimating time-varying coefficients using spline based approaches requires a moderate number of knots, which may lead to unstable estimation and over-fitting issues. To resolve these issues, adding a penalty term greatly aids estimation. The selection of penalty smoothing parameters is difficult in this time-varying setting, as traditional ways like using Akaike information criterion do not work, while cross-validation methods have a heavy computational burden, leading to unstable selections. We propose modified information criteria to determine the smoothing parameter and a parallelized Newton-based algorithm for estimation. We conduct simulations to evaluate the performance of the proposed method. We find that penalization with the smoothing parameter chosen by a modified information criteria is effective at reducing the mean squared error of the estimated time-varying coefficients. Compared to a number of alternatives, we find that the estimates of the variance derived from Bayesian considerations have the best coverage rates of confidence intervals. We apply the method to SEER head-and-neck, colon, prostate, and pancreatic cancer data and detect the time-varying nature of various risk factors.

Comparing Individualized Survival Predictions From Random Survival Forests and Multistate Models in the Presence of Missing Data: A Case Study of Patients With Oropharyngeal Cancer.

Background: In recent years, interest in prognostic calculators for predicting patient health outcomes has grown with the popularity of personalized medicine. These calculators, which can inform treatment decisions, employ many different methods, each of which has advantages and disadvantages. Methods: We present a comparison of a multistate model (MSM) and a random survival forest (RSF) through a case study of prognostic predictions for patients with oropharyngeal squamous cell carcinoma. The MSM is highly structured and takes into account some aspects of the clinical context and knowledge about oropharyngeal cancer, while the RSF can be thought of as a black-box non-parametric approach. Key in this comparison are the high rate of missing values within these data and the different approaches used by the MSM and RSF to handle missingness. Results: We compare the accuracy (discrimination and calibration) of survival probabilities predicted by both approaches and use simulation studies to better understand how predictive accuracy is influenced by the approach to (1) handling missing data and (2) modeling structural/disease progression information present in the data. We conclude that both approaches have similar predictive accuracy, with a slight advantage going to the MSM. Conclusions: Although the MSM shows slightly better predictive ability than the RSF, consideration of other differences are key when selecting the best approach for addressing a specific research question. These key differences include the methods' ability to incorporate domain knowledge, and their ability to handle missing data as well as their interpretability, and ease of implementation. Ultimately, selecting the statistical method that has the most potential to aid in clinical decisions requires thoughtful consideration of the specific goals.

Integrating Information from Existing Risk Prediction Models with No Model Details.

Consider the setting where (i) individual-level data are collected to build a regression model for the association between an event of interest and certain covariates, and (ii) some risk calculators predicting the risk of the event using less detailed covariates are available, possibly as algorithmic black boxes with little information available about how they were built. We propose a general empirical-likelihood-based framework to integrate the rich auxiliary information contained in the calculators into fitting the regression model, to make the estimation of regression parameters more efficient. Two methods are developed, one using working models to extract the calculator information and one making a direct use of calculator predictions without working models. Theoretical and numerical investigations show that the calculator information can substantially reduce the variance of regression parameter estimation. As an application, we study the dependence of the risk of high grade prostate cancer on both conventional risk factors and newly identified molecular biomarkers by integrating information from the Prostate Biopsy Collaborative Group (PBCG) risk calculator, which was built based on conventional risk factors alone.

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Oral Microbiome Community Composition in Head and Neck Squamous Cell Carcinoma.

The impact of the oral microbiome on head and neck cancer pathogenesis and outcomes requires further study. 16s rRNA was isolated and amplified from pre-treatment oral wash samples for 52 cases and 102 controls. The sequences were binned into operational taxonomic units (OTUs) at the genus level. Diversity metrics and significant associations between OTUs and case status were assessed. The samples were binned into community types using Dirichlet multinomial models, and survival outcomes were assessed by community type. Twelve OTUs from the phyla Firmicutes, Proteobacteria, and Acinetobacter were found to differ significantly between the cases and the controls. Beta-diversity was significantly higher between the cases than between the controls (p < 0.01). Two community types were identified based on the predominant sets of OTUs within our study population. The community type with a higher abundance of periodontitis-associated bacteria was more likely to be present in the cases (p < 0.01), in older patients (p < 0.01), and in smokers (p < 0.01). Significant differences between the cases and the controls in community type, beta-diversity, and OTUs indicate that the oral microbiome may play a role in HNSCC.

Increased Nerve Density Adversely Affects Outcome in Oral Cancer.

PURPOSE: Perineural invasion (PNI) in oral cavity squamous cell carcinoma (OSCC) is associated with poor survival. Because of the risk of recurrence, patients with PNI receive additional therapies after surgical resection. Mechanistic studies have shown that nerves in the tumor microenvironment promote aggressive tumor growth. Therefore, in this study, we evaluated whether nerve density (ND) influences tumor growth and patient survival. Moreover, we assessed the reliability of artificial intelligence (AI) in evaluating ND. EXPERIMENTAL DESIGN: To investigate whether increased ND in OSCC influences patient outcome, we performed survival analyses. Tissue sections of OSCC from 142 patients were stained with hematoxylin and eosin and IHC stains to detect nerves and tumor. ND within the tumor bulk and in the adjacent 2 mm was quantified; normalized ND (NND; bulk ND/adjacent ND) was calculated. The impact of ND on tumor growth was evaluated in chick chorioallantoic-dorsal root ganglia (CAM-DRG) and murine surgical denervation models. Cancer cells were grafted and tumor size quantified. Automated nerve detection, applying the Halo AI platform, was compared with manual assessment. RESULTS: Disease-specific survival decreased with higher intratumoral ND and NND in tongue SCC. Moreover, NND was associated with worst pattern-of-invasion and PNI. Increasing the number of DRG, in the CAM-DRG model, increased tumor size. Reduction of ND by denervation in a murine model decreased tumor growth. Automated and manual detection of nerves showed high concordance, with an F1 score of 0.977. CONCLUSIONS: High ND enhances tumor growth, and NND is an important prognostic factor that could influence treatment selection for aggressive OSCC. See related commentary by Hondermarck and Jiang, p. 2342.

Whole blood transcriptome analysis in dairy calves experimentally challenged with bovine herpesvirus 1 (BoHV-1) and comparison to a bovine respiratory syncytial virus (BRSV) challenge.

Bovine herpesvirus 1 (BoHV-1), is associated with several clinical syndromes in cattle, among which bovine respiratory disease (BRD) is of particular significance. Despite the importance of the disease, there is a lack of information on the molecular response to infection via experimental challenge with BoHV-1. The objective of this study was to investigate the whole-blood transcriptome of dairy calves experimentally challenged with BoHV-1. A secondary objective was to compare the gene expression results between two separate BRD pathogens using data from a similar challenge study with BRSV. Holstein-Friesian calves (mean age (SD) = 149.2 (23.8) days; mean weight (SD) = 174.6 (21.3) kg) were either administered BoHV-1 inoculate (1 × 107/mL × 8.5 mL) (n = 12) or were mock challenged with sterile phosphate buffered saline (n = 6). Clinical signs were recorded daily from day (d) -1 to d 6 (post-challenge), and whole blood was collected in Tempus RNA tubes on d six post-challenge for RNA-sequencing. There were 488 differentially expressed (DE) genes (p < 0.05, False Discovery rate (FDR) < 0.10, fold change ≥2) between the two treatments. Enriched KEGG pathways (p < 0.05, FDR <0.05); included Influenza A, Cytokine-cytokine receptor interaction and NOD-like receptor signalling. Significant gene ontology terms (p < 0.05, FDR <0.05) included defence response to virus and inflammatory response. Genes that are highly DE in key pathways are potential therapeutic targets for the treatment of BoHV-1 infection. A comparison to data from a similar study with BRSV identified both similarities and differences in the immune response to differing BRD pathogens.

A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.

There is a growing need for flexible general frameworks that integrate individual-level data with external summary information for improved statistical inference. External information relevant for a risk prediction model may come in multiple forms, through regression coefficient estimates or predicted values of the outcome variable. Different external models may use different sets of predictors and the algorithm they used to predict the outcome Y given these predictors may or may not be known. The underlying populations corresponding to each external model may be different from each other and from the internal study population. Motivated by a prostate cancer risk prediction problem where novel biomarkers are measured only in the internal study, this paper proposes an imputation-based methodology, where the goal is to fit a target regression model with all available predictors in the internal study while utilizing summary information from external models that may have used only a subset of the predictors. The method allows for heterogeneity of covariate effects across the external populations. The proposed approach generates synthetic outcome data in each external population, uses stacked multiple imputation to create a long dataset with complete covariate information. The final analysis of the stacked imputed data is conducted by weighted regression. This flexible and unified approach can improve statistical efficiency of the estimated coefficients in the internal study, improve predictions by utilizing even partial information available from models that use a subset of the full set of covariates used in the internal study, and provide statistical inference for the external population with potentially different covariate effects from the internal population.

Impact of monoclonal antibody therapy for head and neck cancer on end-of-life care utilization and costs.

BACKGROUND: The impact of monoclonal antibody therapy (mAB) for advanced head and neck cancer on end-of-life health care utilization and costs has yet to be adequately studied. METHODS: Retrospective cohort study of patients aged 65 and over with a diagnosis of head and neck cancer between 2007 and 2017 within the SEER-Medicare registry assessing the impact of mAB therapy (i.e., cetuximab, nivolumab, or pembrolizumab) on end-of-life health care utilization (ED visits, inpatient admissions, ICU admissions, and hospice claims) and costs. RESULTS: Of 12 544 patients with HNC, 270 (2.2%) utilized mAB therapy at the end-of-life period. On multivariable analyses adjusting for demographic and clinicopathologic characteristics, there was a significant association between mAB therapy and emergency department visits (OR: 1.38, 95% CI: 1.1-1.8, p = 0.01) and healthcare costs (ß: $9760, 95% CI: 5062-14 458, p < 0.01). CONCLUSIONS: mAB use is associated with higher emergency department utilization and health care costs potentially due to infusion-related and drug toxicity expenses.

Robotic Assisted Transplant Nephrectomy: Case Series and Training Model for Improving Adoption.

Introduction: Open transplant nephrectomy for failed renal allograft is an invasive procedure associated with significant perioperative morbidity and mortality. Minimally invasive surgical approaches have improved a variety of patient outcomes for many surgeries. Thus, robotic assisted transplant nephrectomy (RATN) potentially offers significant patient benefit. Although previously reported, there remains a paucity of data on RATN outcomes and techniques. Methods: Four perfused, high-fidelity hydrogel models were created using previously described techniques and used for simulated RATN. Subsequently performed institutional cases were included for analysis. Intra- and postoperative variables along with patient demographics were retrospectively obtained through parsing of patient records. Results: Simulated nephrectomy time was 67.33 minutes (35.75 - 98.91). Five patients underwent RATN. There were four male and one female patients. The average age was 47 years. The most common indication was abdominal pain secondary to rejection (3/5). Mean blood loss was 188 mL; mean operative time was 243 minutes, and mean length of stay was 4.5 days. Intraoperatively there were two incidences of small cystotomies. One patient was readmitted within 30 days for intraabdominal abscess. Conclusion: This study adds to the growing literature around RATN, demonstrating the feasibility of the technique and reporting good outcomes for this cohort.

Data integration: exploiting ratios of parameter estimates from a reduced external model.

We consider the situation of estimating the parameters in a generalized linear prediction model, from an internal dataset, where the outcome variable [Formula: see text] is binary and there are two sets of covariates, [Formula: see text] and [Formula: see text]. We have information from an external study that provides parameter estimates for a generalized linear model of [Formula: see text] on [Formula: see text]. We propose a method that makes limited assumptions about the similarity of the distributions in the two study populations. The method involves orthogonalizing the [Formula: see text] variables and then borrowing information about the ratio of the coefficients from the external model. The method is justified based on a new result relating the parameters in a generalized linear model to the parameters in a generalized linear model with omitted covariates. The method is applicable if the regression coefficients in the [Formula: see text] given [Formula: see text] model are similar in the two populations, up to an unknown scalar constant. This type of transportability between populations is something that can be checked from the available data. The asymptotic variance of the proposed method is derived. The method is evaluated in a simulation study and shown to gain efficiency compared to simple analysis of the internal dataset, and is robust compared to an alternative method of incorporating external information.

Solutions for surrogacy validation with longitudinal outcomes for a gene therapy.

Valid surrogate endpoints S can be used as a substitute for a true outcome of interest T to measure treatment efficacy in a clinical trial. We propose a causal inference approach to validate a surrogate by incorporating longitudinal measurements of the true outcomes using a mixed modeling approach, and we define models and quantities for validation that may vary across the study period using principal surrogacy criteria. We consider a surrogate-dependent treatment efficacy curve that allows us to validate the surrogate at different time points. We extend these methods to accommodate a delayed-start treatment design where all patients eventually receive the treatment. Not all parameters are identified in the general setting. We apply a Bayesian approach for estimation and inference, utilizing more informative prior distributions for selected parameters. We consider the sensitivity of these prior assumptions as well as assumptions of independence among certain counterfactual quantities conditional on pretreatment covariates to improve identifiability. We examine the frequentist properties (bias of point and variance estimates, credible interval coverage) of a Bayesian imputation method. Our work is motivated by a clinical trial of a gene therapy where the functional outcomes are measured repeatedly throughout the trial.