A rank-based approach to evaluate a surrogate marker in a small sample setting.

In clinical studies of chronic diseases, the effectiveness of an intervention is often assessed using "high cost" outcomes that require long-term patient follow-up and/or are invasive to obtain. While much progress has been made in the development of statistical methods to identify surrogate markers, that is, measurements that could replace such costly outcomes, they are generally not applicable to studies with a small sample size. These methods either rely on nonparametric smoothing which requires a relatively large sample size or rely on strict model assumptions that are unlikely to hold in practice and empirically difficult to verify with a small sample size. In this paper, we develop a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. The method developed in this paper is motivated by a small study of children with nonalcoholic fatty liver disease (NAFLD), a diagnosis for a range of liver conditions in individuals without significant history of alcohol intake. Specifically, we examine whether change in alanine aminotransferase (ALT; measured in blood) is a surrogate marker for change in NAFLD activity score (obtained by biopsy) in a trial, which compared Vitamin E ($n=50$) versus placebo ($n=46$) among children with NAFLD.

Statistical Methods to Evaluate Surrogate Markers.

BACKGROUND: There is tremendous interest in evaluating surrogate markers given their potential to decrease study time, costs, and patient burden. OBJECTIVES: The purpose of this statistical workshop article is to describe and illustrate how to evaluate a surrogate marker of interest using the proportion of treatment effect (PTE) explained as a measure of the quality of the surrogate marker for: (1) a setting with a general fully observed primary outcome (eg, biopsy score); and (2) a setting with a time-to-event primary outcome which may be censored due to study termination or early drop out (eg, time to diabetes). METHODS: The methods are motivated by 2 randomized trials, one among children with nonalcoholic fatty liver disease where the primary outcome was a change in biopsy score (general outcome) and another study among adults at high risk for Type 2 diabetes where the primary outcome was time to diabetes (time-to-event outcome). The methods are illustrated using the Rsurrogate package with a detailed R code provided. RESULTS: In the biopsy score outcome setting, the estimated PTE of the examined surrogate marker was 0.182 (95% confidence interval [CI]: 0.121, 0.240), that is, the surrogate explained only 18.2% of the treatment effect on the biopsy score. In the diabetes setting, the estimated PTE of the surrogate marker was 0.596 (95% CI: 0.404, 0.760), that is, the surrogate explained 59.6% of the treatment effect on diabetes incidence. CONCLUSIONS: This statistical workshop provides tools that will support future researchers in the evaluation of surrogate markers.

Semi-supervised calibration of noisy event risk (SCANER) with electronic health records.

OBJECTIVE: Electronic health records (EHR), containing detailed longitudinal clinical information on a large number of patients and covering broad patient populations, open opportunities for comprehensive predictive modeling of disease progression and treatment response. However, since EHRs were originally constructed for administrative purposes not for research, in the EHR-linked studies, it is often not feasible to capture reliable information for analytical variables, especially in the survival setting, when both accurate event status and event times are needed for model building. For example, progression-free survival (PFS), a commonly used survival outcome for cancer patients, often involves complex information embedded in free-text clinical notes and cannot be extracted reliably. Proxies of PFS time such as time to the first mention of progression in the notes are at best good approximations to the true event time. This leads to difficulty in efficiently estimating event rates for an EHR patient cohort. Estimating survival rates based on error-prone outcome definitions can lead to biased results and hamper the power in the downstream analysis. On the other hand, extracting accurate event time information via manual annotation is time and resource intensive. The objective of this study is to develop a calibrated survival rate estimator using noisy outcomes from EHR data. MATERIALS AND METHODS: In this paper, we propose a two-stage semi-supervised calibration of noisy event rate (SCANER) estimator that can effectively overcome censoring induced dependency and attains more robust performance (i.e., not sensitive to misspecification of the imputation model) by fully utilizing both a small-labeled set of gold-standard survival outcomes annotated via manual chart review and a set of proxy features automatically captured via EHR in the unlabeled set. We validate the SCANER estimator by estimating the PFS rates for a virtual cohort of lung cancer patients from one large tertiary care center and the ICU-free survival rates for COVID patients from two large tertiary care centers. RESULTS: In terms of survival rate estimates, the SCANER had very similar point estimates compared to the complete-case Kaplan Meier estimator. On the other hand, other benchmark methods for comparison, which fail to account for the induced dependency between event time and the censoring time conditioning on surrogate outcomes, produced biased results across all three case studies. In terms of standard errors, the SCANER estimator was more efficient than the KM estimator, with up to 50% efficiency gain. CONCLUSION: The SCANER estimator achieves more efficient, robust, and accurate survival rate estimates compared to existing approaches. This promising new approach can also improve the resolution (i.e., granularity of event time) by using labels conditioning on multiple surrogates, particularly among less common or poorly coded conditions.

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

Identifying shared genetic architecture between rheumatoid arthritis and other conditions: a phenome-wide association study with genetic risk scores.

BACKGROUND: Rheumatoid arthritis (RA) shares genetic variants with other autoimmune conditions, but existing studies test the association between RA variants with a pre-defined set of phenotypes. The objective of this study was to perform a large-scale, systemic screen to determine phenotypes that share genetic architecture with RA to inform our understanding of shared pathways. METHODS: In the UK Biobank (UKB), we constructed RA genetic risk scores (GRS) incorporating human leukocyte antigen (HLA) and non-HLA risk alleles. Phenotypes were defined using groupings of International Classification of Diseases (ICD) codes. Patients with an RA code were excluded to mitigate the possibility of associations being driven by the diagnosis or management of RA. We performed a phenome-wide association study, testing the association between the RA GRS with phenotypes using multivariate generalized estimating equations that adjusted for age, sex, and first five principal components. Statistical significance was defined using Bonferroni correction. Results were replicated in an independent cohort and replicated phenotypes were validated using medical record review of patients. FINDINGS: We studied n = 316,166 subjects from UKB without evidence of RA and screened for association between the RA GRS and n = 1317 phenotypes. In the UKB, 20 phenotypes were significantly associated with the RA GRS, of which 13 (65%) were immune mediated conditions including polymyalgia rheumatica, granulomatosis with polyangiitis (GPA), type 1 diabetes, and multiple sclerosis. We further identified a novel association in Celiac disease where the HLA and non-HLA alleles had strong associations in opposite directions. Strikingly, we observed that the non-HLA GRS was exclusively associated with greater risk of the validated conditions, suggesting shared underlying pathways outside the HLA region. INTERPRETATION: This study replicated and identified novel autoimmune phenotypes verified by medical record review that share immune pathways with RA and may inform opportunities for shared treatment targets, as well as risk assessment for conditions with a paucity of genomic data, such as GPA. FUNDING: This research was funded by the US National Institutes of Health (P30AR072577, R21AR078339, R35GM142879, T32AR007530) and the Harold and DuVal Bowen Fund.

Comorbidity Influences the Comparative Safety of Biologic Therapy in Older Adults With Inflammatory Bowel Diseases.

INTRODUCTION: There are limited data on comparative risk of infections with various biologic agents in older adults with inflammatory bowel diseases (IBDs). We aimed to assess the comparative safety of biologic agents in older IBD patients with varying comorbidity burden. METHODS: We used data from a large, national commercial insurance plan in the United States to identify patients 60 years and older with IBD who newly initiated tumor necrosis factor-α antagonists (anti-TNF), vedolizumab, or ustekinumab. Comorbidity was defined using the Charlson Comorbidity Index (CCI). Our primary outcome was infection-related hospitalizations. Cox proportional hazards models were fitted in propensity score-weighted cohorts to compare the risk of infections between the different therapeutic classes. RESULTS: The anti-TNF, vedolizumab, and ustekinumab cohorts included 2,369, 972, and 352 patients, respectively, with a mean age of 67 years. The overall rate of infection-related hospitalizations was similar to that of anti-TNF agents for patients initiating vedolizumab (hazard ratio [HR] 0.94, 95% confidence interval [CI] 0.84-1.04) and ustekinumab (0.92, 95% CI 0.74-1.16). Among patients with a CCI of >1, both ustekinumab (HR: 0.66, 95% CI: 0.46-0.91, p-interaction <0.01) and vedolizumab (HR: 0.78, 95% CI: 0.65-0.94, p-interaction: 0.02) were associated with a significantly lower rate of infection-related hospitalizations compared with anti-TNFs. No difference was found among patients with a CCI of ≤1. DISCUSSION: Among adults 60 years and older with IBD initiating biologic therapy, both vedolizumab and ustekinumab were associated with lower rates of infection-related hospitalizations than anti-TNF therapy for those with high comorbidity burden.

Semi-supervised approach to event time annotation using longitudinal electronic health records.

Large clinical datasets derived from insurance claims and electronic health record (EHR) systems are valuable sources for precision medicine research. These datasets can be used to develop models for personalized prediction of risk or treatment response. Efficiently deriving prediction models using real world data, however, faces practical and methodological challenges. Precise information on important clinical outcomes such as time to cancer progression are not readily available in these databases. The true clinical event times typically cannot be approximated well based on simple extracts of billing or procedure codes. Whereas, annotating event times manually is time and resource prohibitive. In this paper, we propose a two-step semi-supervised multi-modal automated time annotation (MATA) method leveraging multi-dimensional longitudinal EHR encounter records. In step I, we employ a functional principal component analysis approach to estimate the underlying intensity functions based on observed point processes from the unlabeled patients. In step II, we fit a penalized proportional odds model to the event time outcomes with features derived in step I in the labeled data where the non-parametric baseline function is approximated using B-splines. Under regularity conditions, the resulting estimator of the feature effect vector is shown as root-n consistent. We demonstrate the superiority of our approach relative to existing approaches through simulations and a real data example on annotating lung cancer recurrence in an EHR cohort of lung cancer patients from Veteran Health Administration.

Temporal Trends in Clinical Evidence of 5-Year Survival Within Electronic Health Records Among Patients With Early-Stage Colon Cancer Managed With Laparoscopy-Assisted Colectomy vs Open Colectomy.

Importance: Temporal shifts in clinical knowledge and practice need to be adjusted for in treatment outcome assessment in clinical evidence. Objective: To use electronic health record (EHR) data to (1) assess the temporal trends in treatment decisions and patient outcomes and (2) emulate a randomized clinical trial (RCT) using EHR data with proper adjustment for temporal trends. Design, Setting, and Participants: The Clinical Outcomes of Surgical Therapy (COST) Study Group Trial assessing overall survival of patients with stages I to III early-stage colon cancer was chosen as the target trial. The RCT was emulated using EHR data of patients from a single health care system cohort who underwent colectomy for early-stage colon cancer from January 1, 2006, to December 31, 2017, and were followed up to January 1, 2020, from Mass General Brigham. Analyses were conducted from December 2, 2019, to January 24, 2022. Exposures: Laparoscopy-assisted colectomy (LAC) vs open colectomy (OC). Main Outcomes and Measures: The primary outcome was 5-year overall survival. To address confounding in the emulation, pretreatment variables were selected and adjusted. The temporal trends were adjusted by stratification of the calendar year when the colectomies were performed with cotraining across strata. Results: A total of 943 patients met key RCT eligibility criteria in the EHR emulation cohort, including 518 undergoing LAC (median age, 63 [range, 20-95] years; 268 [52%] women; 121 [23%] with stage I, 165 [32%] with stage II, and 232 [45%] with stage III cancer; 32 [6%] with colon adhesion; 278 [54%] with right-sided colon cancer; 18 [3%] with left-sided colon cancer; and 222 [43%] with sigmoid colon cancer) and 425 undergoing OC (median age, 65 [range, 28-99] years; 223 [52%] women; 61 [14%] with stage I, 153 [36%] with stage II, and 211 [50%] with stage III cancer; 39 [9%] with colon adhesion; 202 [47%] with right-sided colon cancer; 39 [9%] with left-sided colon cancer; and 201 [47%] with sigmoid colon cancer). Tests for temporal trends in treatment assignment (χ2 = 60.3; P < .001) and overall survival (χ2 = 137.2; P < .001) were significant. The adjusted EHR emulation reached the same conclusion as the RCT: LAC is not inferior to OC in overall survival rate with risk difference at 5 years of -0.007 (95% CI, -0.070 to 0.057). The results were consistent for stratified analysis within each temporal period. Conclusions and Relevance: These findings suggest that confounding bias from temporal trends should be considered when conducting clinical evidence studies with long time spans. Stratification of calendar time and cotraining of models is one solution. With proper adjustment, clinical evidence may supplement RCTs in the assessment of treatment outcome over time.

Comparative Risk of Thrombotic and Cardiovascular Events with Tofacitinib and Anti-TNF Agents in Patients with Inflammatory Bowel Diseases.

BACKGROUND: Tofacitinib and inflammatory bowel disease (IBD) have been associated with increased risks for thromboembolic and cardiovascular events, but drug attributable risk is unknown. METHODS: We conducted a retrospective cohort study in a US claims database. We identified patients with IBD by International Classification of Disease (ICD) codes, stipulated 180 days of continuous enrollment prior to tofacitinib or anti-tumor necrosis factor (TNF) initiation to determine new users. Primary outcomes were ICD codes for venous thromboembolism (VTE) and cardiovascular (CV) events. We constructed propensity score (PS)-weighted Cox proportional hazard models to estimate hazard ratios (HRs) and time-to-event outcomes comparing tofacitinib and anti-TNF. We conducted a subgroup analysis of patients ≥ 50 years. RESULTS: We identified 305 patients with IBD initiating tofacitinib and compared them with 19,096 initiating anti-TNFs. After weighting, balance was achieved across all demographic covariates. VTE occurred in 5% of patients treated with tofacitinib and 4% of anti-TNF users; in a PS-weighted cohort, tofacitinib did not confer a significantly elevated VTE risk compared with anti-TNF therapy (HR: 1.72, 95% CI: 0.74-3.01). A major CV event (MACE) occurred in 2% of tofacitinib users and 1% of anti-TNF users; tofacitinib also did not confer a significantly elevated risk for MACE (HR: 2.50, 95% CI: 0.37-6.18). Those with a Charlson comorbidity index ≥ 2 had greater risks for thromboembolic and cardiovascular events. Similar findings were noted in patients ≥ 50 years. CONCLUSIONS: In this large, active comparator, study, we demonstrate that tofacitinib was not associated with a higher risk of adverse thrombotic events compared with anti-TNFs in patients with IBD.

Risk of Infections With Ustekinumab and Tofacitinib Compared to Tumor Necrosis Factor α Antagonists in Inflammatory Bowel Diseases.

BACKGROUND & AIMS: The comparative safety of therapies is important to inform relative positioning within the therapeutic algorithm. Tumor necrosis factor α antagonists (anti-TNF) are associated with an increased risk of infections. Whether there is a similar increase with ustekinumab (UST) or tofacitinib has not been established. METHODS: We identified patients with Crohn's disease or ulcerative colitis from a national commercial health insurance plan in the United States between 2008 and 2019. Infectious outcomes were ascertained for patients newly initiating anti-TNF, UST, or tofacitinib therapy. Cox proportional hazards models were fit in propensity score-weighted cohorts to compare rates between patients treated with UST or tofacitinib and anti-TNF therapy. RESULTS: Our study included 19,096, 2420, and 305 patients with inflammatory bowel disease initiating anti-TNF, UST, and tofacitinib therapy, respectively. Over follow-up on-treatment, 7% and 44% of anti-TNF patients had infection-related hospitalizations and developed infections, respectively, compared with 4% and 32% of UST patients and 6% and 41% of tofacitinib patients. In the weighted Cox analysis, UST was associated with a significantly lower risk of infection (hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.86-0.99) compared with anti-TNF therapy. There was a trend towards a reduction in infection-related hospitalizations (HR, 0.84; 95% CI, 0.66-1.03). The risk of infections (HR, 0.97; 95% CI, 0.75-1.24) or infection-related hospitalizations (HR, 0.59; 95% CI, 0.27-1.05) were similar between patients on tofacitinib and anti-TNF. CONCLUSIONS: UST is associated with reduced risk of infections compared to anti-TNF biologics in inflammatory bowel disease, whereas no difference was observed between tofacitinib and anti-TNF therapy.

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer.

Importance: Electronic health records (EHRs) provide a low-cost means of accessing detailed longitudinal clinical data for large populations. A lung cancer cohort assembled from EHR data would be a powerful platform for clinical outcome studies. Objective: To investigate whether a clinical cohort assembled from EHRs could be used in a lung cancer prognosis study. Design, Setting, and Participants: In this cohort study, patients with lung cancer were identified among 76 643 patients with at least 1 lung cancer diagnostic code deposited in an EHR in Mass General Brigham health care system from July 1988 to October 2018. Patients were identified via a semisupervised machine learning algorithm, for which clinical information was extracted from structured and unstructured data via natural language processing tools. Data completeness and accuracy were assessed by comparing with the Boston Lung Cancer Study and against criterion standard EHR review results. A prognostic model for non-small cell lung cancer (NSCLC) overall survival was further developed for clinical application. Data were analyzed from March 2019 through July 2020. Exposures: Clinical data deposited in EHRs for cohort construction and variables of interest for the prognostic model were collected. Main Outcomes and Measures: The primary outcomes were the performance of the lung cancer classification model and the quality of the extracted variables; the secondary outcome was the performance of the prognostic model. Results: Among 76 643 patients with at least 1 lung cancer diagnostic code, 42 069 patients were identified as having lung cancer, with a positive predictive value of 94.4%. The study cohort consisted of 35 375 patients (16 613 men [47.0%] and 18 756 women [53.0%]; 30 140 White individuals [85.2%], 1040 Black individuals [2.9%], and 857 Asian individuals [2.4%]) after excluding patients with lung cancer history and less than 14 days of follow-up after initial diagnosis. The median (interquartile range) age at diagnosis was 66.7 (58.4-74.1) years. The area under the receiver operating characteristic curves of the prognostic model for overall survival with NSCLC were 0.828 (95% CI, 0.815-0.842) for 1-year prediction, 0.825 (95% CI, 0.812-0.836) for 2-year prediction, 0.814 (95% CI, 0.800-0.826) for 3-year prediction, 0.814 (95% CI, 0.799-0.828) for 4-year prediction, and 0.812 (95% CI, 0.798-0.825) for 5-year prediction. Conclusions and Relevance: These findings suggest the feasibility of assembling a large-scale EHR-based lung cancer cohort with detailed longitudinal clinical measurements and that EHR data may be applied in cancer progression with a set of generalizable approaches.

Tobacco use and age are associated with different morphologic features of anterior communicating artery aneurysms.

We present a cohort of patients with anterior communicating artery (ACoA) aneurysms to investigate morphological characteristics and clinical factors associated with rupture of the aneurysms. 505 patients with ACoA aneurysms were identified at the Brigham and Women's Hospital and Massachusetts General Hospital between 1990 and 2016, with available CT angiography (CTA). Three-dimensional (3D) reconstructions were performed to evaluate aneurysmal morphologic features, including location, projection, irregularity, the presence of daughter dome, height, height/width ratio, and relationships between surrounding vessels. Patient risk factors assessed included patient age, sex, tobacco use, alcohol use, and family history of aneurysms and aneurysmal subarachnoid hemorrhage. Logistic regression was used to build a predictive ACoA score for rupture. Morphologic features associated with ruptured ACoA aneurysms were the presence of a daughter dome (OR 21.4, 95% CI 10.6-43.1), smaller neck diameter (OR 0.55, 95% CI 0.42-0.71), larger aspect ratio (OR 3.57, 95% CI 2.05-6.24), larger flow angle (OR 1.03, 95% CI 1.02-1.05), and smaller ipsilateral A2-ACoA angle (OR 0.98, 95% CI 0.97-1.00). Tobacco use was predominantly associated with morphological factors intrinsic to the aneurysm that were associated with rupture while younger age was also associated with morphologic features extrinsic to the aneurysm that were associated with rupture. The ACoA score had good predictive capacity for rupture with AUC = 0.92 using the 0.632 bootstrap cross-validation for correction of overfitting bias. Ruptured ACoA aneurysms were associated with morphological features that are simple to assess using a simple scoring system. Tobacco use and younger age were predominantly associated with intrinsic and extrinsic morphological features characteristic of rupture, respectively.

Geometric variations associated with posterior communicating artery aneurysms.

BACKGROUND: Hemodynamic stress, conditioned by the morphology of the surrounding vasculature, plays an important role in aneurysm formation. Our goal was to identify image-based location-specific parameters that are associated with posterior communicating artery (PCoA) aneurysms. METHODS: Three-dimensional morphological parameters obtained from CT angiography or digital subtraction angiography from 187 patients with unilateral PCoA aneurysms, diagnosed at the Brigham and Women's Hospital and Massachusetts General Hospital between 1990 and 2016, were evaluated. In order to control for genetic and clinical risk factors, we chose the contralateral unaffected PCoA as a control group. We examined diameters and angles of the surrounding parent and daughter vessels. Univariable and multivariable statistical analyses were performed to determine statistical significance. Sensitivity analyses with small aneurysms (≤5 mm) only and an unmatched analysis of 432 PCoA aneurysms and 197 control patients without PCoA aneurysms were also performed. RESULTS: In a multivariable conditional logistic regression model we showed that smaller diameter size ratio (OR 1.45×10-5, 95% CI 1.12×10-7 to 1.88×10-3) and larger daughter-daughter angle (OR 1.04, 95% CI 1.02 to 1.07) were significantly associated with PCoA aneurysm presence after correcting for other variables. In subgroup analyses of small aneurysms (≤5 mm) and in an unmatched analysis the significance and direction of these results were preserved. CONCLUSIONS: Larger daughter-daughter angles and smaller diameter size ratio are significantly associated with the presence of PCoA aneurysms. These simple parameters can be utilized to guide the risk assessment for the formation of PCoA aneurysms in high risk patients.

Safety and Efficacy of Tumor Necrosis Factor Antagonists in Older Patients With Ulcerative Colitis: Patient-Level Pooled Analysis of Data From Randomized Trials.

BACKGROUND & AIMS: Treatment of older patients (more than 60 years) with ulcerative colitis (UC) can be a challenge, because they might be more vulnerable to adverse events (AEs). We determined the effects of age on the safety and efficacy of anti-tumor necrosis factor (TNF) therapy in a pooled analysis of data from randomized trials. METHODS: We obtained individual patient-level data from 4 trials of anti-TNF therapy for patients with UC from the Yale Open Data Access Project. Participants were assigned to groups of older age (60 years or older) and younger age (younger than 60 years). The primary outcome was difference in serious AEs (SAEs), defined as death, life-threatening event, hospitalization, and/or significant disability. Secondary outcomes were severe infections, non-severe infections, neoplasms, and achievement of clinical remission, defined by trial investigators as Mayo score ≤ 2 with no sub-score >1 at the end of induction or maintenance therapy. A random effects logistic regression model was fitted to estimate the effect of anti-TNF therapy on safety and efficacy by age, adjusting for confounders and trial-level effects. RESULTS: The study cohort included 2257 patients (231 60 years or older). Higher proportions of older patients receiving anti-TNF therapy had SAEs (20%) and hospitalizations (14.4%), compared with younger patients (10.2% had SAEs and 5.2% were hospitalized); there were no significant differences between groups in proportions with severe or non-severe infections. Compared with placebo, there was no significant difference in safety risks associated with anti-TNF therapy (SAEs reduced by 5.4% in older patients vs reduction of 2.4% in younger patients; hospitalizations reduced by 6.7% in older patients vs reduction of 2.5% in younger patients; severe infections reduced by 3.1% vs increase of 0.7% in younger patients). There was no significant difference in between older vs younger patients in efficacy of anti-TNF therapy in inducing remission (odds risk ratio, 1.05, 95% CI, 0.33-3.39) or in maintaining remission (odds risk ratio, 0.49; 95% CI, 0.18-1.33). CONCLUSIONS: In a pooled analysis of data from randomized trials, we found that older patients with UC have an increased baseline increased risk of SAEs, but no increase in risk can be attributed to anti-TNF therapy in older vs younger patients.

Phenome-wide examination of comorbidity burden and multiple sclerosis disease severity.

OBJECTIVE: We assessed the comorbidity burden associated with multiple sclerosis (MS) severity by performing a phenome-wide association study (PheWAS). METHODS: We conducted a PheWAS in 2 independent cohorts: a discovery (Boston, United States; 1993-2014) and extension cohort (British Columbia, Canada; 1991-2008). We included adults with MS, ≥1 Expanded Disability Status Scale (EDSS) score, and ≥1 International Classification of Diseases (ICD) code other than MS. We calculated the Multiple Sclerosis Severity Score (MSSS) using the EDSS. We mapped ICD codes into PheCodes (phenotypes), using a published system with each PheCode representing a unique medical condition. Association between the MSSS and the presence of each condition was assessed using logistic regression adjusted for covariates. RESULTS: The discovery and extension cohorts included 3,439 and 4,876 participants, respectively. After Bonferroni correction and covariate adjustments, a higher MSSS was associated with 37 coexisting conditions in the discovery cohort. Subsequently, 16 conditions, including genitourinary, infectious, metabolic, epilepsy, and movement disorders, met the reporting criteria, reaching the Bonferroni threshold of significance with the same direction of effect in the discovery and extension cohort. Notably, benign neoplasm of the skin was inversely associated with the MSSS. CONCLUSION: The phenome-wide approach enabled a systematic interrogation of the comorbidity burden and highlighted clinically relevant medical conditions associated with MS severity (beyond MS-specific consequences) and defines a roadmap for comprehensive investigation of comorbidities in chronic neurologic diseases. Further prospective investigation of the bidirectional relationship between disability and comorbidities could inform the individualized patient management.

Use of Narrative Concepts in Electronic Health Records to Validate Associations Between Genetic Factors and Response to Treatment of Inflammatory Bowel Diseases.

Crohn's disease (CD) and ulcerative colitis (UC) are heterogeneous. With availability of therapeutic classes with distinct immunologic mechanisms of action, it has become imperative to identify markers that predict likelihood of response to each drug class. However, robust development of such tools has been challenging because of need for large prospective cohorts with systematic and careful assessment of treatment response using validated indices. Most hospitals in the United States use electronic health records (EHRs) that warehouse a large amount of narrative (free-text) and codified (administrative) data generated during routine clinical care. These data have been used to construct virtual disease cohorts for epidemiologic research as well as for defining genetic basis of disease states or discrete laboratory values.1-3 Whether EHR-based data can be used to validate genetic associations for more nuanced outcomes such as treatment response has not been examined previously.

Plasma CD36 and Incident Diabetes: A Case-Cohort Study in Danish Men and Women.

BACKGROUND: Membrane CD36 is a fatty acid transporter implicated in the pathogenesis of metabolic disease. We aimed to evaluate the association between plasma CD36 levels and diabetes risk and to examine if the association was independent of adiposity among Danish population. METHODS: We conducted a case-cohort study nested within the Danish Diet, Cancer and Health study among participants free of cardiovascular disease, diabetes and cancer and with blood samples and anthropometric measurements (height, weight, waist circumference, and body fat percentage) at baseline (1993 to 1997). CD36 levels were measured in 647 incident diabetes cases that occurred before December 2011 and a total of 3,515 case-cohort participants (236 cases overlap). RESULTS: Higher plasma CD36 levels were associated with higher diabetes risk after adjusting for age, sex and other lifestyle factors. The hazard ratio (HR) comparing high versus low tertile of plasma CD36 levels was 1.36 (95% confidence interval [CI], 1.00 to 1.86). However, the association lost its significance after further adjustment for different adiposity indices such as body mass index (HR, 1.23; 95% CI, 0.87 to 1.73), waist circumference (HR, 1.21; 95% CI, 0.88 to 1.68) or body fat percentage (HR, 1.20; 95% CI, 0.86 to 1.66). Moreover, raised plasma CD36 levels were moderately associated with diabetes risk among lean participants, but the association was not present among overweight/obese individuals. CONCLUSION: Higher plasma CD36 levels were associated with higher diabetes risk, but the association was not independent of adiposity. In this Danish population, the association of CD36 with diabetes risk could be either mediated or confounded by adiposity.

Estimating average treatment effects with a double-index propensity score.

We consider estimating average treatment effects (ATE) of a binary treatment in observational data when data-driven variable selection is needed to select relevant covariates from a moderately large number of available covariates X . To leverage covariates among X predictive of the outcome for efficiency gain while using regularization to fit a parametric propensity score (PS) model, we consider a dimension reduction of X based on fitting both working PS and outcome models using adaptive LASSO. A novel PS estimator, the Double-index Propensity Score (DiPS), is proposed, in which the treatment status is smoothed over the linear predictors for X from both the initial working models. The ATE is estimated by using the DiPS in a normalized inverse probability weighting estimator, which is found to maintain double robustness and also local semiparametric efficiency with a fixed number of covariates p. Under misspecification of working models, the smoothing step leads to gains in efficiency and robustness over traditional doubly robust estimators. These results are extended to the case where p diverges with sample size and working models are sparse. Simulations show the benefits of the approach in finite samples. We illustrate the method by estimating the ATE of statins on colorectal cancer risk in an electronic medical record study and the effect of smoking on C-reactive protein in the Framingham Offspring Study.

Decreased Total Iron Binding Capacity May Correlate with Ruptured Intracranial Aneurysms.

Iron and its derivatives play a significant role in various physiological and biochemical pathways, and are influenced by a wide variety of inflammatory, infectious, and immunological disorders. We hypothesized that iron and its related factors play a role in intracranial aneurysm pathophysiology and investigated if serum iron values are associated with ruptured intracranial aneurysms. 4,701 patients with 6,411 intracranial aneurysms, including 1201 prospective patients, who were diagnosed at the Massachusetts General Hospital and Brigham and Women's Hospital between 1990 and 2016 were evaluated. A total of 366 patients with available serum iron, ferritin and total iron binding capacity (TIBC) values were ultimately included in the analysis. 89% of included patients had anemia. Patients were categorized into ruptured and non-ruptured groups. Univariable and multivariable logistic regression analyses were performed to determine the association between ruptured aneurysms and iron, ferritin, and TIBC. TIBC values (10-3 g/L) within 1 year of diagnosis (OR 0.41, 95% CI 0.28-0.59) and between 1 and 3 years from diagnosis (OR 0.52, 95% CI 0.29-0.93) were significantly and inversely associated with intracranial aneurysm rupture. In contrast, serum iron and ferritin were not significant. In this case-control study, low TIBC was significantly associated with ruptured aneurysms, both in the short- and long term. However, this association may not apply to the general population as there may be a selection bias as iron studies were done in a subset of patients only.

Evaluating longitudinal markers under two-phase study designs.

Little attention has been given to the design of efficient studies to evaluate longitudinal biomarkers. Measuring longitudinal markers on an entire cohort is cost prohibitive and, especially for rare outcomes such as cancer, may be infeasible. Thus, methods for evaluation of longitudinal biomarkers using efficient and cost-effective study designs are needed. Case cohort (CCH) and nested case-control (NCC) studies allow investigators to evaluate biomarkers rigorously and at reduced cost, with only a small loss in precision. In this article, we develop estimators of several measures to evaluate the accuracy and discrimination of predicted risk under CCH and NCC study designs. We use double inverse probability weighting (DIPW) to account for censoring and sampling bias in estimation and inference procedures. We study the asymptotic properties of the proposed estimators. To facilitate inference using two-phase longitudinal data, we develop valid resampling-based variance estimation procedures under CCH and NCC. We evaluate the performance of our estimators under CCH and NCC using simulation studies and illustrate them on a NCC study within the hepatitis C antiviral long-term treatment against cirrhosis (HALT-C) clinical trial. Our estimators and inference procedures perform well under CCH and NCC, provided that the sample size at the time of prediction (effective sample size) is reasonable. These methods are widely applicable, efficient, and cost-effective and can be easily adapted to other study designs used to evaluate prediction rules in a longitudinal setting.