Cardiovascular and Cancer Risk with Tofacitinib in Rheumatoid Arthritis.

BACKGROUND: Increases in lipid levels and cancers with tofacitinib prompted a trial of major adverse cardiovascular events (MACE) and cancers in patients with rheumatoid arthritis receiving tofacitinib as compared with a tumor necrosis factor (TNF) inhibitor. METHODS: We conducted a randomized, open-label, noninferiority, postauthorization, safety end-point trial involving patients with active rheumatoid arthritis despite methotrexate treatment who were 50 years of age or older and had at least one additional cardiovascular risk factor. Patients were randomly assigned in a 1:1:1 ratio to receive tofacitinib at a dose of 5 mg or 10 mg twice daily or a TNF inhibitor. The coprimary end points were adjudicated MACE and cancers, excluding nonmelanoma skin cancer. The noninferiority of tofacitinib would be shown if the upper boundary of the two-sided 95% confidence interval for the hazard ratio was less than 1.8 for the combined tofacitinib doses as compared with a TNF inhibitor. RESULTS: A total of 1455 patients received tofacitinib at a dose of 5 mg twice daily, 1456 received tofacitinib at a dose of 10 mg twice daily, and 1451 received a TNF inhibitor. During a median follow-up of 4.0 years, the incidences of MACE and cancer were higher with the combined tofacitinib doses (3.4% [98 patients] and 4.2% [122 patients], respectively) than with a TNF inhibitor (2.5% [37 patients] and 2.9% [42 patients]). The hazard ratios were 1.33 (95% confidence interval [CI], 0.91 to 1.94) for MACE and 1.48 (95% CI, 1.04 to 2.09) for cancers; the noninferiority of tofacitinib was not shown. The incidences of adjudicated opportunistic infections (including herpes zoster and tuberculosis), all herpes zoster (nonserious and serious), and adjudicated nonmelanoma skin cancer were higher with tofacitinib than with a TNF inhibitor. Efficacy was similar in all three groups, with improvements from month 2 that were sustained through trial completion. CONCLUSIONS: In this trial comparing the combined tofacitinib doses with a TNF inhibitor in a cardiovascular risk-enriched population, risks of MACE and cancers were higher with tofacitinib and did not meet noninferiority criteria. Several adverse events were more common with tofacitinib. (Funded by Pfizer; ORAL Surveillance ClinicalTrials.gov number, NCT02092467.).

Risk of major adverse cardiovascular events with tofacitinib versus tumour necrosis factor inhibitors in patients with rheumatoid arthritis with or without a history of atherosclerotic cardiovascular disease: a post hoc analysis from ORAL Surveillance.

OBJECTIVES: Evaluate risk of major adverse cardiovascular events (MACE) with tofacitinib versus tumour necrosis factor inhibitors (TNFi) in patients with rheumatoid arthritis (RA) with or without a history of atherosclerotic cardiovascular disease (ASCVD) in ORAL Surveillance. METHODS: Patients with RA aged ≥50 years with ≥1 additional CV risk factor received tofacitinib 5 mg or 10 mg two times per day or TNFi. Hazard rations (HRs) were evaluated for the overall population and by history of ASCVD (exploratory analysis). RESULTS: Risk of MACE, myocardial infarction and sudden cardiac death were increased with tofacitinib versus TNFi in ORAL Surveillance. In patients with history of ASCVD (14.7%; 640/4362), MACE incidence was higher with tofacitinib 5 mg two times per day (8.3%; 17/204) and 10 mg two times per day (7.7%; 17/222) versus TNFi (4.2%; 9/214). HR (combined tofacitinib doses vs TNFi) was 1.98 (95% confidence interval (CI) 0.95 to 4.14; interaction p values: 0.196 (for HR)/0.059 (for incidence rate difference)). In patients without history of ASCVD, MACE HRs for tofacitinib 5 mg two times per day (2.4%; 30/1251) and 10 mg two times per day (2.8%; 34/1234) versus TNFi (2.3%; 28/1237) were, respectively, 1.03 (0.62 to 1.73) and 1.25 (0.76 to 2.07). CONCLUSIONS: This post hoc analysis observed higher MACE risk with tofacitinib versus TNFi in patients with RA and history of ASCVD. Among patients without history of ASCVD, all with prevalent CV risk factors, MACE risk did not appear different with tofacitinib 5 mg two times per day versus TNFi. Due to the exploratory nature of this analysis and low statistical power, we cannot exclude differential MACE risk for tofacitinib 5 mg two times per day versus TNFi among patients without history of ASCVD, but any absolute risk excess is likely low. TRIAL REGISTRATION NUMBER: NCT02092467.

Effects of dapagliflozin on prevention of major clinical events and recovery in patients with respiratory failure because of COVID-19: Design and rationale for the DARE-19 study.

AIMS: Coronavirus disease 2019 (COVID-19) is caused by a novel severe acute respiratory syndrome coronavirus 2. It can lead to multiorgan failure, including respiratory and cardiovascular decompensation, and kidney injury, with significant associated morbidity and mortality, particularly in patients with underlying metabolic, cardiovascular, respiratory or kidney disease. Dapagliflozin, a sodium-glucose cotransporter-2 inhibitor, has shown significant cardio- and renoprotective benefits in patients with type 2 diabetes (with and without atherosclerotic cardiovascular disease), heart failure and chronic kidney disease, and may provide similar organ protection in high-risk patients with COVID-19. MATERIALS AND METHODS: DARE-19 (NCT04350593) is an investigator-initiated, collaborative, international, multicentre, randomized, double-blind, placebo-controlled study testing the dual hypotheses that dapagliflozin can reduce the incidence of cardiovascular, kidney and/or respiratory complications or all-cause mortality, or improve clinical recovery, in adult patients hospitalized with COVID-19 but not critically ill on admission. Eligible patients will have ≥1 cardiometabolic risk factor for COVID-19 complications. Patients will be randomized 1:1 to dapagliflozin 10 mg or placebo. Primary efficacy endpoints are time to development of new or worsened organ dysfunction during index hospitalization, or all-cause mortality, and the hierarchical composite endpoint of change in clinical status through day 30 of treatment. Safety of dapagliflozin in individuals with COVID-19 will be assessed. CONCLUSIONS: DARE-19 will evaluate whether dapagliflozin can prevent COVID-19-related complications and all-cause mortality, or improve clinical recovery, and assess the safety profile of dapagliflozin in this patient population. Currently, DARE-19 is the first large randomized controlled trial investigating use of sodium-glucose cotransporter 2 inhibitors in patients with COVID-19.

Power and sample size calculation for the win odds test: application to an ordinal endpoint in COVID-19 trials.

The win odds is a distribution-free method of comparing locations of distributions of two independent random variables. Introduced as a method for analyzing hierarchical composite endpoints, it is well suited to be used in the analysis of ordinal scale endpoints in COVID-19 clinical trials. For a single outcome, we provide power and sample size calculation formulas for the win odds test. We also provide an implementation of the win odds analysis method for a single ordinal outcome in a commonly used statistical software to make the win odds analysis fully reproducible.

On kernel machine learning for propensity score estimation under complex confounding structures.

Post marketing data offer rich information and cost-effective resources for physicians and policy-makers to address some critical scientific questions in clinical practice. However, the complex confounding structures (e.g., nonlinear and nonadditive interactions) embedded in these observational data often pose major analytical challenges for proper analysis to draw valid conclusions. Furthermore, often made available as electronic health records (EHRs), these data are usually massive with hundreds of thousands observational records, which introduce additional computational challenges. In this paper, for comparative effectiveness analysis, we propose a statistically robust yet computationally efficient propensity score (PS) approach to adjust for the complex confounding structures. Specifically, we propose a kernel-based machine learning method for flexibly and robustly PS modeling to obtain valid PS estimation from observational data with complex confounding structures. The estimated propensity score is then used in the second stage analysis to obtain the consistent average treatment effect estimate. An empirical variance estimator based on the bootstrap is adopted. A split-and-merge algorithm is further developed to reduce the computational workload of the proposed method for big data, and to obtain a valid variance estimator of the average treatment effect estimate as a by-product. As shown by extensive numerical studies and an application to postoperative pain EHR data comparative effectiveness analysis, the proposed approach consistently outperforms other competing methods, demonstrating its practical utility.

Suboptimal reliability of liver biopsy evaluation has implications for randomized clinical trials.

BACKGROUND & AIMS: Liver biopsies are a critical component of pivotal studies in non-alcoholic steatohepatitis (NASH), constituting inclusion criteria, risk stratification factors and endpoints. We evaluated the reliability of NASH Clinical Research Network scoring of liver biopsies in a NASH clinical trial. METHODS: Digitized slides of 678 biopsies from 339 patients with paired biopsies randomized into the EMMINENCE study - examining a novel insulin sensitizer (MSDC-0602K) in NASH - were read independently by 3 hepatopathologists blinded to treatment code and scored using the NASH CRN histological scoring system. Various endpoints were computed from these scores. RESULTS: Inter-reader linearly weighted kappas were 0.609, 0.484, 0.328, and 0.517 for steatosis, fibrosis, lobular inflammation, and ballooning, respectively. Inter-reader unweighted kappas were 0.400 for the diagnosis of NASH, 0.396 for NASH resolution without worsening fibrosis, and 0.366 for fibrosis improvement without worsening NASH. In the current study, 46.3% of the patients included in the study based on 1 hepatopathologist's qualifying reading were deemed not to meet the study's histologic inclusion criteria by at least 1 of the 3 hepatopathologists. The MSDC-0602K treatment effect was lowest for those histologic features with lower inter-reader reliability. Simulations show that the lack of reliability of endpoints and inclusion criteria can drastically reduce study power - from >90% in a well-powered study to as low as 40%. CONCLUSIONS: The reliability of hepatopathologists' liver biopsy evaluation using currently accepted criteria is suboptimal. This lack of reliability may affect NASH pivotal studies by introducing patients who do not meet NASH study entry criteria, misclassifying fibrosis subgroups, and attenuating apparent treatment effects. LAY SUMMARY: Since liver biopsy analysis plays such an important role in clinical studies of non-alcoholic steatohepatitis, it is important to understand the reliability of hepato-pathologist readings. We examined both inter- and intra-reader variability in a large data set of paired liver biopsies from a clinical trial. We found very poor inter-reader and modest intra-reader variability. This result has important implications for entry criteria, fibrosis stratification, and the ability to measure a treatment effect in clinical trials.

Methods for clarifying criteria for study continuation at interim analysis.

In monitoring clinical trials, the question of futility, or whether the data thus far suggest that the results at the final analysis are unlikely to be statistically successful, is regularly of interest over the course of a study. However, the opposite viewpoint of whether the study is sufficiently demonstrating proof of concept (POC) and should continue is a valuable consideration and ultimately should be addressed with high POC power so that a promising study is not prematurely terminated. Conditional power is often used to assess futility, and this article interconnects the ideas of assessing POC for the purpose of study continuation with conditional power, while highlighting the importance of the POC type I error and the POC type II error for study continuation or not at the interim analysis. Methods for analyzing subgroups motivate the interim analyses to maintain high POC power via an adjusted interim POC significance level criterion for study continuation or testing against an inferiority margin. Furthermore, two versions of conditional power based on the assumed effect size or the observed interim effect size are considered. Graphical displays illustrate the relationship of the POC type II error for premature study termination to the POC type I error for study continuation and the associated conditional power criteria.

Randomization-based analysis of covariance for inference in the sequential parallel comparison design.

The sequential parallel comparison design (SPCD), with sequence groups P:P, P:T, and T:T, together with the exclusion of the second-period information from placebo responders in the first period, can serve usefully for studies with highly favorable placebo response, for example, psychiatric clinical trials. This paper presents a methodology for the first-period treatment difference in the overall population and the second-period treatment difference in the placebo nonresponders for the first period, as well as other available sources of information that could be of potential interest. Without any assumptions, a hypothesis testing method is proposed based on the randomization distribution of comparisons using the covariance structure for the randomized population under the null hypothesis to control type I error. Randomization-based analysis of covariance (ANCOVA) is introduced to adjust for baseline and for the observations that serve as baselines for the second period. Related methods are proposed for the study population as a simple random sample of an almost infinite population. The statistical properties of the proposed methods are described with simulation studies; and the use of the methods is illustrated for an example based on the data from the ADAPT-A clinical trial.

Cardiovascular Outcome Trials in Type 2 Diabetes: What Do They Mean for Clinical Practice?

IN BRIEF Cardiovascular disease is the leading cause of morbidity and mortality in people with diabetes, and deaths from heart disease are two to four times higher among adults with type 2 diabetes. Trials such as the U.K. Prospective Diabetes Study, ACCORD (Action to Control Cardiovascular Risk in Diabetes), ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation), and VADT (Veteran's Affairs Diabetes Trial) produced mixed findings regarding whether intensive glycemic control results in improved cardiovascular (CV) outcomes for patients with diabetes. In response to concerns, including the CV safety of the thiazolidinedione rosiglitazone, the U.S. Food and Drug Administration and subsequently the European Medicines Agency issued guidance that trials should be conducted to prove that antihyperglycemic agents have acceptable CV risk profiles. In this article, the authors review the study designs and results of CV outcomes trials conducted with sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide 1 receptor agonists and discuss how these may affect clinical practice.

Statistical planning in confirmatory clinical trials with multiple treatment groups, multiple visits, and multiple endpoints.

Multiplicity issues can be multidimensional: A confirmatory clinical trial may be designed to have efficacy assessed with two or more primary endpoints, for multiple dose groups, and at several post-baseline visits. Controlling for multiplicity in this situation is challenging because there can be a hierarchy with respect to some but not all measurements. If the higher dose is considered more efficacious, multiplicity approach may evaluate the higher dose with higher priority through a fixed sequential testing framework for dose assessments in combination with a Hochberg approach for endpoints. The lower dose is only assessed when the higher dose has significant results, which reduces the power for detecting signals in the lower dose group. However, in some instances the higher dose may associate with tolerability or safety concerns that preclude regulatory approval. A real confirmatory clinical trial with such challenges is provided as an illustrative example. We discuss closed testing procedures based on multi-way averages of comparisons for this complex multiplicity situation through illustrative case analyses and a simulation study. Such strategies manage the higher dose and the lower dose with equal priority, and they enable evaluation of the multiple endpoints at multiple visits collectively with power being reasonably high.

Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos.

The cohort design allows investigators to explore the genetic basis of a variety of diseases and traits in a single study while avoiding major weaknesses of the case-control design. Most cohort studies employ multistage cluster sampling with unequal probabilities to conveniently select participants with desired characteristics, and participants from different clusters might be genetically related. Analysis that ignores the complex sampling design can yield biased estimation of the genetic association and inflation of the type I error. Herein, we develop weighted estimators that reflect unequal selection probabilities and differential nonresponse rates, and we derive variance estimators that properly account for the sampling design and the potential relatedness of participants in different sampling units. We compare, both analytically and numerically, the performance of the proposed weighted estimators with unweighted estimators that disregard the sampling design. We demonstrate the usefulness of the proposed methods through analysis of MetaboChip data in the Hispanic Community Health Study/Study of Latinos, which is the largest health study of the Hispanic/Latino population in the United States aimed at identifying risk factors for various diseases and determining the role of genes and environment in the occurrence of diseases. We provide guidelines on the use of weighted and unweighted estimators, as well as the relevant software.

Blood pressure reactivity to psychological stress is associated with clinical outcomes in patients with heart failure.

INTRODUCTION: Cardiovascular (CV) reactivity to psychological stress has been implicated in the development and exacerbation of cardiovascular disease (CVD). Although high CV reactivity traditionally is thought to convey greater risk of CVD, the relationship between reactivity and clinical outcomes is inconsistent and may depend on the patient population under investigation. The present study examined CV reactivity in patients with heart failure (HF) and its potential association with long-term clinical outcomes. METHODS: One hundred ninety-nine outpatients diagnosed with HF, with ejection fraction ≤40%, underwent an evaluation of blood pressure (BP) and heart rate reactivity to a laboratory-based simulated public-speaking stressor. Cox proportional hazards regression models were used to examine the prospective association between BP and heart rate reactivity on a combined end point of death or CV hospitalization over a 5-year median follow-up period. RESULTS: Both systolic blood pressure (SBP) and diastolic blood pressure (DBP) reactivity, quantified as continuous variables, were inversely related to risk of death or CV hospitalization (Ps < .01) after controlling for established risk factors, including HF disease severity and etiology. In similar models, heart rate reactivity was unrelated to outcome (P = .12). In models with tertiles of reactivity, high SBP reactivity, compared with intermediate SBP reactivity, was associated with lower risk (hazard ratio [HR] = .498, 95% CI .335-.742, P =.001), whereas low SBP reactivity did not differ from intermediate reactivity. For DBP, high reactivity was marginally associated with lower risk compared with intermediate DBP reactivity (HR = .767, 95% CI .515-1.14, P =.193), whereas low DBP reactivity was associated with greater risk (HR = 1.49, 95% CI 1.027-2.155, P =.0359). No relationship of heart rate reactivity to outcome was identified. CONCLUSIONS: For HF patients with reduced ejection fraction, a robust increase in BP evoked by a laboratory-based psychological challenge was associated with lower risk for adverse CVD events and may be a novel and unique marker of left ventricular systolic reserve that is accompanied by a more favorable long-term prognosis.

A model-based conditional power assessment for decision making in randomized controlled trial studies.

Conditional power based on summary statistic by comparing outcomes (such as the sample mean) directly between 2 groups is a convenient tool for decision making in randomized controlled trial studies. In this paper, we extend the traditional summary statistic-based conditional power with a general model-based assessment strategy, where the test statistic is based on a regression model. Asymptotic relationships between parameter estimates based on the observed interim data and final unobserved data are established, from which we develop an analytic model-based conditional power assessment for both Gaussian and non-Gaussian data. The model-based strategy is not only flexible in handling baseline covariates and more powerful in detecting the treatment effects compared with the conventional method but also more robust in controlling the overall type I error under certain missing data mechanisms. The performance of the proposed method is evaluated by extensive simulation studies and illustrated with an application to a clinical study.

Statistical planning to address strongly correlated endpoints for inferential subgroups: An innovative approach for an illustrative clinical trial with complex multiplicity issues.

Multiplicity is an important statistical issue that arises in clinical trials when the efficacy of the test treatment is evaluated in multiple ways. The major concern for multiplicity is that uncontrolled multiple assessments lead to inflated family-wise Type I error, and they thereby undermine the integrity of the statistical inferences. Multiplicity comes from different sources, for example, making inferences either on the overall population or some pre-specified sub-populations, while multiple endpoints need to be evaluated for each population. Therefore, a sound statistical strategy that controls the family-wise Type I error rate in a strong sense, without excessive loss of power from over-control, is crucial for the success of the trial. For a recent phase III cardiovascular trial with such complex multiplicity, we illustrate the use of a closed testing strategy that begins with a global test; and subsequent testing only proceeds when the global test is rejected. Also, we discuss a simulation study based on this trial to compare the power of the illustrated closed testing strategy to some well-known alternative approaches. We found that this strategy can comprehensively meet most of the primary objectives of the trial effectively with reasonably high overall power.

Randomization-based adjustment of multiple treatment hazard ratios for covariates with missing data.

Clinical trials are designed to compare treatment effects when applied to samples from the same population. Randomization is used so that the samples are not biased with respect to baseline covariates that may influence the efficacy of the treatment. We develop randomization-based covariance adjustment methodology to estimate the log hazard ratios and their confidence intervals of multiple treatments in a randomized clinical trial with time-to-event outcomes and missingness among the baseline covariates. The randomization-based covariance adjustment method is a computationally straight-forward method for handling missing baseline covariate values.

Sensitivity analysis for missing dichotomous outcome data in multi-visit randomized clinical trial with randomization-based covariance adjustment.

Dichotomous endpoints in clinical trials have only two possible outcomes, either directly or via categorization of an ordinal or continuous observation. It is common to have missing data for one or more visits during a multi-visit study. This paper presents a closed form method for sensitivity analysis of a randomized multi-visit clinical trial that possibly has missing not at random (MNAR) dichotomous data. Counts of missing data are redistributed to the favorable and unfavorable outcomes mathematically to address possibly informative missing data. Adjusted proportion estimates and their closed form covariance matrix estimates are provided. Treatment comparisons over time are addressed with Mantel-Haenszel adjustment for a stratification factor and/or randomization-based adjustment for baseline covariables. The application of such sensitivity analyses is illustrated with an example. An appendix outlines an extension of the methodology to ordinal endpoints.

On variance estimate for covariate adjustment by propensity score analysis.

Propensity score (PS) methods have been used extensively to adjust for confounding factors in the statistical analysis of observational data in comparative effectiveness research. There are four major PS-based adjustment approaches: PS matching, PS stratification, covariate adjustment by PS, and PS-based inverse probability weighting. Though covariate adjustment by PS is one of the most frequently used PS-based methods in clinical research, the conventional variance estimation of the treatment effects estimate under covariate adjustment by PS is biased. As Stampf et al. have shown, this bias in variance estimation is likely to lead to invalid statistical inference and could result in erroneous public health conclusions (e.g., food and drug safety and adverse events surveillance). To address this issue, we propose a two-stage analytic procedure to develop a valid variance estimator for the covariate adjustment by PS analysis strategy. We also carry out a simple empirical bootstrap resampling scheme. Both proposed procedures are implemented in an R function for public use. Extensive simulation results demonstrate the bias in the conventional variance estimator and show that both proposed variance estimators offer valid estimates for the true variance, and they are robust to complex confounding structures. The proposed methods are illustrated for a post-surgery pain study. Copyright © 2016 John Wiley & Sons, Ltd.

Sensitivity analysis for missing outcomes in time-to-event data with covariate adjustment.

Covariate-adjusted sensitivity analyses is proposed for missing time-to-event outcomes. The method invokes multiple imputation (MI) for the missing failure times under a variety of specifications regarding the post-withdrawal tendency for having the event of interest. With a clinical trial example, we compared methods of covariance analyses for time-to-event data, i.e., the multivariable Cox proportional hazards (PH) model and nonparametric analysis of covariance, and then illustrated how to incorporate these methods into the proposed sensitivity analysis for covariate adjustment. The MI methods considered are Kaplan-Meier multiple imputation and covariate-adjusted and unadjusted PH multiple imputation. The assumptions, statistical issues, and features for these methods are discussed.