What can patients tell us in Sjögren's syndrome?

In Sjögren's Syndrome (SS), clinical heterogeneity and discordance between disease activity measures and patient experience are key obstacles to effective therapeutic development. Patient reported outcome measures (PROMs) are useful tools for understanding the unmet needs from the patients' perspective and therefore they are key for the development of patient centric healthcare systems. Initial concern about the subjectivity of PROMs has given way to methodological rigour and clear guidance for the development of PROMs. To date, several studies of patient stratification using PROMs have identified similar symptom-based subgroups. There is evidence to suggest that these subgroups may represent different disease endotypes with differing responses to therapeutic interventions. Stratified medicine approaches, alongside sensitive outcome measures, have the potential to improve our understanding of SS pathobiology and therapeutic development. The inclusion of PROMs is important for the success of such approaches. In this review we discuss the opportunities of using PROMs in understanding the pathogenesis of and therapeutic development for SS.

Win ratio analyses of piperacillin-tazobactam versus meropenem for ceftriaxone non-susceptible Escherichia coli or Klebsiella pneumoniae bloodstream infections: Post-hoc insights from the MERINO trial

BACKGROUND

Clinical trials of treatments for serious infections commonly use the primary endpoint of all-cause mortality. However, many trial participants survive their infection and this endpoint may not truly reflect important benefits and risks of therapy. The win ratio uses a hierarchical composite endpoint that can incorporate and prioritise outcome measures by relative clinical importance.

METHODS

The win ratio methodology was applied post-hoc to outcomes observed in the MERINO trial, which compared piperacillin-tazobactam with meropenem. We quantified the win ratio with a primary hierarchical composite endpoint, including all-cause mortality, microbiological relapse and secondary infection. A win ratio of one would correspond to no difference between the two antibiotics, while a ratio less than one favors meropenem. Further analyses were performed to calculate the win odds and to introduce a continuous outcome variable in order to reduce ties.

RESULTS

With the hierarchy of all-cause mortality, microbiological relapse and secondary infection, the win ratio estimate was 0.40 (95% CI: 0.22, 0.71; p=0.002), favoring meropenem over piperacillin-tazobactam. However, 73.4% of the pairs were tied due to the small proportion of events. The win odds, a modification of the win ratio accounting for ties, was 0.79 (95% CI: 0.68, 0.92). The addition of length of stay to the primary composite, greatly minimised the number of ties (4.6%) with a win ratio estimate of 0.77 (95% CI: 0.60-0.99; p=0.04).

CONCLUSIONS

The application of the win ratio methodology to the MERINO trial data illustrates its utility and feasibility for use in antimicrobial trials.

Statistical inference for time-to-event data in non-randomized cohorts with selective attrition

In multi-season clinical trials with a randomize-once strategy, patients enrolled from previous seasons who stay alive and remain in the study will be treated according to the initial randomization in subsequent seasons. To address the potentially selective attrition from earlier seasons for the non-randomized cohorts, we develop an inverse probability of treatment weighting method using season-specific propensity scores to produce unbiased estimates of survival functions or hazard ratios. Bootstrap variance estimators are used to account for the randomness in the estimated weights and the potential correlations in repeated events within each patient from season to season. Simulation studies show that the weighting procedure and bootstrap variance estimator provide unbiased estimates and valid inferences in Kaplan-Meier estimates and Cox proportional hazard models. Finally, data from the INVESTED trial are analyzed to illustrate the proposed method.

Dissecting the restricted mean time in favor of treatment

The restricted mean time in favor (RMT-IF) summarizes the treatment effect on a hierarchical composite endpoint with mortality at the top. Its crude decomposition into "stage-wise effects," i.e., the net average time gained by the treatment prior to each component event, does not reveal the patient state in which the extra time is spent. To obtain this information, we break each stage-wise effect into subcomponents according to the specific state to which the reference condition is improved. After re-expressing the subcomponents as functionals of the marginal survival functions of outcome events, we estimate them conveniently by plugging in the Kaplan -- Meier estimators. Their robust variance matrices allow us to construct joint tests on the decomposed units, which are particularly powerful against component-wise differential treatment effects. By reanalyzing a cancer trial and a cardiovascular trial, we acquire new insights into the quality and composition of the extra survival times, as well as the extra time with fewer hospitalizations, gained by the treatment in question. The proposed methods are implemented in the rmt package freely available on the Comprehensive R Archive Network (CRAN).

Study design for restricted mean time analysis of recurrent events and death

The restricted mean time in favor (RMT-IF) of treatment has just been added to the analytic toolbox for composite endpoints of recurrent events and death. To help practitioners design new trials based on this method, we develop tools to calculate the sample size and power. Specifically, we formulate the outcomes as a multistate Markov process with a sequence of transient states for recurrent events and an absorbing state for death. The transition intensities, in this case the instantaneous risks of another nonfatal event or death, are assumed to be time-homogeneous but nonetheless allowed to depend on the number of past events. Using the properties of Coxian distributions, we derive the RMT-IF effect size under the alternative hypothesis as a function of the treatment-to-control intensity ratios along with the baseline intensities, the latter of which can be easily estimated from historical data. We also reduce the variance of the nonparametric RMT-IF estimator to calculable terms under a standard set-up for censoring. Simulation studies show that the resulting formulas provide accurate approximation to the sample size and power in realistic settings. For illustration, a past cardiovascular trial with recurrent-hospitalization and mortality outcomes is analyzed to generate the parameters needed to design a future trial. The procedures are incorporated into the rmt package along with the original methodology on the Comprehensive R Archive Network (CRAN).

Generalized Pairwise Comparisons to Assess Treatment Effects: JACC Review Topic of the Week

A time-to-first-event composite endpoint analysis has well-known shortcomings in evaluating a treatment effect in cardiovascular clinical trials. It does not fully describe the clinical benefit of therapy because the severity of the events, events repeated over time, and clinically relevant nonsurvival outcomes cannot be considered. The generalized pairwise comparisons (GPC) method adds flexibility in defining the primary endpoint by including any number and type of outcomes that best capture the clinical benefit of a therapy as compared with standard of care. Clinically important outcomes, including bleeding severity, number of interventions, and quality of life, can easily be integrated in a single analysis. The treatment effect in GPC can be expressed by the net treatment benefit, the success odds, or the win ratio. This review provides guidance on the use of GPC and the choice of treatment effect measures for the analysis and reporting of cardiovascular trials.

Nonparametric inference of general while-alive estimands for recurrent events

Measuring the treatment effect on recurrent events like hospitalization in the presence of death has long challenged statisticians and clinicians alike. Traditional inference on the cumulative frequency unjustly penalizes survivorship as longer survivors also tend to experience more adverse events. Expanding a recently suggested idea of the "while-alive" event rate, we consider a general class of such estimands that adjust for the length of survival without losing causal interpretation. Given a user-specified loss function that allows for arbitrary weighting, we define as estimand the average loss experienced per unit time alive within a target period and use the ratio of this loss rate to measure the effect size. Scaling the loss rate by the width of the corresponding time window gives us an alternative, and sometimes more photogenic, way of showing the data. To make inferences, we construct a nonparametric estimator for the loss rate through the cumulative loss and the restricted mean survival time and derive its influence function in closed form for variance estimation and testing. As simulations and analysis of real data from a heart failure trial both show, the while-alive approach corrects for the false attenuation of treatment effect due to patients living longer under treatment, with increased statistical power as a result. The proposed methods are implemented in the R-package WA, which is publicly available from the Comprehensive R Archive Network (CRAN).

The Role of Sodium-Glucose Cotransporter-2 Inhibitors in Heart Failure Management: The Continuing Challenge of Clinical Outcome Endpoints in Heart Failure Trials

The introduction of sodium-glucose cotransporter-2 (SGLT2) inhibitors in the management of heart failure with preserved ejection fraction (HFpEF) may be regarded as the first effective treatment in these patients. However, this proposition must be evaluated from the perspective of the complexity of clinical outcome endpoints in heart failure. The major goals of heart failure treatment have been categorized as: (1) reduction in (cardiovascular) mortality, (2) prevention of recurrent hospitalizations due to worsening heart failure, and (3) improvement in clinical status, functional capacity, and quality of life. The use of the composite primary endpoint of cardiovascular death and hospitalization for heart failure in SGLT2 inhibitor HFpEF trials flowed from the assumption that hospitalization for heart failure is a proxy for subsequent cardiovascular death. The use of this composite endpoint was not justified since the effect of the intervention on both components was clearly distinct. Moreover, the lack of convincing and clinically meaningful effects of SGLT2 inhibitors on metrics of heart failure-related health status indicates that the effect of this class of drugs in HFpEF patients is essentially restricted to an effect on hospitalization for heart failure. In conclusion, SGLT2 inhibitors do not represent a substantial breakthrough in the management of HFpEF.

On restricted mean time in favor of treatment

The restricted mean time in favor (RMT-IF) of treatment is a nonparametric effect size for complex life history data. It is defined as the net average time the treated spend in a more favorable state than the untreated over a prespecified time window. It generalizes the familiar restricted mean survival time (RMST) from the two-state life-death model to account for intermediate stages in disease progression. The overall estimand can be additively decomposed into stage-wise effects, with the standard RMST as a component. Alternate expressions of the overall and stage-wise estimands as integrals of the marginal survival functions for a sequence of landmark transitioning events allow them to be easily estimated by plug-in Kaplan-Meier estimators. The dynamic profile of the estimated treatment effects as a function of follow-up time can be visualized using a multilayer, cone-shaped "bouquet plot." Simulation studies under realistic settings show that the RMT-IF meaningfully and accurately quantifies the treatment effect and outperforms traditional tests on time to the first event in statistical efficiency thanks to its fuller utilization of patient data. The new methods are illustrated on a colon cancer trial with relapse and death as outcomes and a cardiovascular trial with recurrent hospitalizations and death as outcomes. The R-package rmt implements the proposed methodology and is publicly available from the Comprehensive R Archive Network (CRAN).

Stratified proportional win-fractions regression analysis

The recently proposed proportional win-fractions (PW) model extends the two-sample win ratio analysis of prioritized composite endpoints to regression. Its proportionality assumption ensures that the covariate-specific win ratios are invariant to the follow-up time. However, this assumption is strong and may not be satisfied by every covariate in the model. We develop a stratified PW model that adjusts for certain prognostic factors without setting them as covariates, thus bypassing the proportionality requirement. We formulate the stratified model based on pairwise comparisons within each stratum, with a common win ratio across strata modeled as a multiplicative function of the covariates. Correspondingly, we construct an estimating function for the regression coefficients in the form of an incomplete U $$ U $$ -statistic consisting of within-stratum pairs. Two types of asymptotic variance estimators are developed depending on the number of strata relative to the sample size. This in particular allows valid inference even when the strata are extremely small, such as with matched pairs. Simulation studies in realistic settings show that the stratified model outperforms the unstratified version in robustness and efficiency. Finally, real data from a major cardiovascular trial are analyzed to illustrate the potential benefits of stratification. The proposed methods are implemented in the R package WR, publicly available on the Comprehensive R Archive Network (CRAN).

On recurrent-event win ratio

The win ratio approach proposed by Pocock et al. (2012) has become a popular tool for analyzing composite endpoints of death and non-fatal events like hospitalization. Its standard version, however, draws on the non-fatal event only through the first occurrence. For statistical efficiency and clinical interpretability, we construct and compare different win ratio variants that make fuller use of recurrent events. We pay special attention to a variant called last-event-assisted win ratio, which compares two patients on the cumulative frequency of the non-fatal event, with ties broken by the time of its latest episode. It is shown that last-event-assisted win ratio uses more data than the standard win ratio does but reduces to the latter when the non-fatal event occurs at most once. We further prove that last-event-assisted win ratio rejects the null hypothesis with large probability if the treatment stochastically delays all events. Simulations under realistic settings show that the last-event-assisted win ratio test consistently enjoys higher power than the standard win ratio and other competitors. Analysis of a real cardiovascular trial provides further evidence for the practical advantages of the last-event-assisted win ratio. Finally, we discuss future work to develop meaningful effect size estimands based on the extended rules of comparison. The R-code for the proposed methods is included in the package WR openly available on the Comprehensive R Archive Network.

Sample size formula for a win ratio endpoint

The win ratio composite endpoint, which organizes the components of the composite hierarchically, is becoming popular in late-stage clinical trials. The method involves comparing data in a pair-wise manner starting with the endpoint highest in priority (eg, cardiovascular death). If the comparison is a tie, the endpoint next highest in priority (eg, hospitalizations for heart failure) is compared, and so on. Its sample size is usually calculated through complex simulations because there does not exist in the literature a simple sample size formula. This article provides a formula that depends on the probability that a randomly selected patient from one group does better than a randomly selected patient from another group, and on the probability of a tie. We compare the published 95% confidence intervals, which require patient-level data, with that calculated from the formula, requiring only summary-level data, for 17 composite or single win ratio endpoints. The two sets of results are similar. Simulations show the sample size formula performs well. The formula provides important insights. It shows when adding an endpoint to the hierarchy can increase power even if the added endpoint has low power by itself. It provides relevant information to modify an on-going blinded trial if necessary. The formula allows a non-specialist to quickly determine the size of the trial with a win ratio endpoint whose use is expected to increase over time.

A comparison of methods for analyzing a binary composite endpoint with partially observed components in randomized controlled trials

Composite endpoints are commonly used to define primary outcomes in randomized controlled trials. A participant may be classified as meeting the endpoint if they experience an event in one or several components (eg, a favorable outcome based on a composite of being alive and attaining negative culture results in trials assessing tuberculosis treatments). Partially observed components that are not missing simultaneously complicate the analysis of the composite endpoint. An intuitive strategy frequently used in practice for handling missing values in the components is to derive the values of the composite endpoint from observed components when possible, and exclude from analysis participants whose composite endpoint cannot be derived. Alternatively, complete record analysis (CRA) (excluding participants with any missing components) or multiple imputation (MI) can be used. We compare a set of methods for analyzing a composite endpoint with partially observed components mathematically and by simulation, and apply these methods in a reanalysis of a published trial (TOPPS). We show that the derived composite endpoint can be missing not at random even when the components are missing completely at random. Consequently, the treatment effect estimated from the derived endpoint is biased while CRA results without the derived endpoint are valid. Missing at random mechanisms require MI of the components. We conclude that, although superficially attractive, deriving the composite endpoint from observed components should generally be avoided. Despite the potential risk of imputation model mis-specification, MI of missing components is the preferred approach in this study setting.

A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints

It is often of interest to explore how dose affects the toxicity and efficacy properties of a novel treatment. In oncology, efficacy is often assessed through response, which is defined by a patient having no new tumour lesions and their tumour size shrinking by 30%. Usually response and toxicity are analysed as binary outcomes in early phase trials. Methods have been proposed to improve the efficiency of analysing response by utilising the continuous tumour size information instead of dichotomising it. However, these methods do not allow for toxicity or for different doses. Motivated by a phase II trial testing multiple doses of a treatment against placebo, we propose a latent variable model that can estimate the probability of response and no toxicity (or other related outcomes) for different doses. We assess the confidence interval coverage and efficiency properties of the method, compared to methods that do not use the continuous tumour size, in a simulation study and the real study. The coverage is close to nominal when model assumptions are met, although can be below nominal when the model is misspecified. Compared to methods that treat response as binary, the method has confidence intervals with 30-50% narrower widths. The method adds considerable efficiency but care must be taken that the model assumptions are reasonable.

Analysis of ordered composite endpoints

Composite endpoints are frequently used in clinical trials, but simple approaches, such as the time to first event, do not reflect any ordering among the endpoints. However, some endpoints, such as mortality, are worse than others. A variety of procedures have been proposed to reflect the severity of the individual endpoints such as pairwise ranking approaches, the win ratio, and the desirability of outcome ranking. When patients have different lengths of follow-up, however, ranking can be difficult and proposed methods do not naturally lead to regression approaches and require specialized software. This paper defines an ordering score O to operationalize the patient ranking implied by hierarchical endpoints. We show how differential right censoring of follow-up corresponds to multiple interval censoring of the ordering score allowing standard software for survival models to be used to calculate the nonparametric maximum likelihood estimators (NPMLEs) of different measures. Additionally, if one assumes that the ordering score is transformable to an exponential random variable, a semiparametric regression is obtained, which is equivalent to the proportional hazards model subject to multiple interval censoring. Standard software can be used for estimation. We show that the NPMLE can be poorly behaved compared to the simple estimators in staggered entry trials. We also show that the semiparametric estimator can be more efficient than simple estimators and explore how standard Cox regression maneuvers can be used to assess model fit, allow for flexible generalizations, and assess interactions of covariates with treatment. We analyze a trial of short versus long-term antiplatelet therapy using our methods.

A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints

It is often of interest to explore how dose affects the toxicity and efficacy properties of a novel treatment. In oncology, efficacy is often assessed through response, which is defined by a patient having no new tumour lesions and their tumour size shrinking by 30%. Usually response and toxicity are analysed as binary outcomes in early phase trials. Methods have been proposed to improve the efficiency of analysing response by utilising the continuous tumour size information instead of dichotomising it. However, these methods do not allow for toxicity or for different doses. Motivated by a phase II trial testing multiple doses of a treatment against placebo, we propose a latent variable model that can estimate the probability of response and no toxicity (or other related outcomes) for different doses. We assess the confidence interval coverage and efficiency properties of the method, compared to methods that do not use the continuous tumour size, in a simulation study and the real study. The coverage is close to nominal when model assumptions are met, although can be below nominal when the model is misspecified. Compared to methods that treat response as binary, the method has confidence intervals with 30-50% narrower widths. The method adds considerable efficiency but care must be taken that the model assumptions are reasonable.

On weighted composite scores for early Alzheimer's trials

Recent research on finding appropriate composite endpoints for preclinical Alzheimer's disease has focused considerable effort on finding "optimized" weights in the construction of a weighted composite score. In this paper, several proposed methods are reviewed. Our results indicate no evidence that these methods will increase the power of the test statistics, and some of these weights will introduce biases to the study. Our recommendation is to focus on identifying more sensitive items from clinical practice and appropriate statistical analyses of a large Alzheimer's data set. Once a set of items has been selected, there is no evidence that adding weights will generate more sensitive composite endpoints.

Are component endpoints equal? A preference study into the practice of composite endpoints in clinical trials

Objectives

To examine patients’ perspectives regarding composite endpoints and the utility patients put on possible adverse outcomes of revascularization procedures.

Design

In the PRECORE study, a stated preference elicitation method Best‐Worst Scaling (BWS) was used to determine patient preference for 8 component endpoints (CEs): need for redo percutaneous coronary intervention (PCI) within 1 year, minor stroke with symptoms <24 hours, minor myocardial infarction (MI) with symptoms <3 months, recurrent angina pectoris, need for redo coronary artery bypass grafting (CABG) within 1 year, major MI causing permanent disability, major stroke causing permanent disability and death within 24 hours.

Setting

A tertiary PCI/CABG centre.

Participants

One hundred and sixty patients with coronary artery disease who underwent PCI or CABG.

Main outcome measures

Importance weights (IWs).

Results

Patients considered need for redo PCI within 1 year (IW: 0.008), minor stroke with symptoms <24 hours (IW: 0.017), minor MI with symptoms <3 months (IW: 0.027), need for redo CABG within 1 year (IW: 0.119), recurrent angina pectoris (IW: 0.300) and major MI causing permanent disability (IW: 0.726) less severe than death within 24 hours (IW: 1.000). Major stroke causing permanent disability was considered worse than death within 24 hours (IW: 1.209). Ranking of CEs and the relative values attributed to the CEs differed among subgroups based on gender, age and educational level.

Conclusion

Patients attribute different weight to individual CEs. This has significant implications for the interpretation of clinical trial data.

Methods for the analysis of multiple endpoints in small populations: A review

While current guidelines generally recommend single endpoints for primary analyses of confirmatory clinical trials, it is recognized that certain settings require inference on multiple endpoints for comprehensive conclusions on treatment effects. Furthermore, combining treatment effect estimates from several outcome measures can increase the statistical power of tests. Such an efficient use of resources is of special relevance for trials in small populations. This paper reviews approaches based on a combination of test statistics or measurements across endpoints as well as multiple testing procedures that allow for confirmatory conclusions on individual endpoints. We especially focus on feasibility in trials with small sample sizes and do not solely rely on asymptotic considerations. A systematic literature search in the Scopus database, supplemented by a manual search, was performed to identify research papers on analysis methods for multiple endpoints with relevance to small populations. The identified methods were grouped into approaches that combine endpoints into a single measure to increase the power of statistical tests and methods to investigate differential treatment effects in several individual endpoints by multiple testing.

An optimal Wilcoxon-Mann-Whitney test of mortality and a continuous outcome

We consider a two-group randomized clinical trial, where mortality affects the assessment of a follow-up continuous outcome. Using the worst-rank composite endpoint, we develop a weighted Wilcoxon-Mann-Whitney test statistic to analyze the data. We determine the optimal weights for the Wilcoxon-Mann-Whitney test statistic that maximize its power. We derive a formula for its power and demonstrate its accuracy in simulations. Finally, we apply the method to data from an acute ischemic stroke clinical trial of normobaric oxygen therapy.

Cholesterol trials and mortality

An overview of clinical trials can reveal a class effect on mortality that is not apparent from individual trials. Most large trials of lipid pharmacotherapy are not powered to detect differences in mortality and instead assess efficacy with composite cardiovascular endpoints. We illustrate the importance of all-cause mortality data by comparing survival in three different sets of the larger controlled lipid trials that underpin meta-analyses. These trials are for fibrates and statins. Fibrate treatment in five of the six main trials was associated with a decrease in survival, one fibrate trial showed a non-significant reduction in mortality that can be explained by a different target population. In secondary prevention, statin treatment increased survival in all five of the main trials, absolute mean increase ranged from 0.43% to 3.33%, the median change was 1.75%, which occurred in the largest trial. In primary prevention, statin treatment increased survival in six of the seven main trials, absolute mean change in survival ranged from -0.09% to 0.89%, median 0.49%. Composite safety endpoints are rare in these trials. The failure to address composite safety endpoints in most lipid trials precludes a balanced summary of risk-benefit when a composite has been used for efficacy. Class effects on survival provide informative summaries of the risk-benefit of lipid pharmacotherapy. We consider that the presentation of key mortality/survival data adds to existing meta-analyses to aid personal treatment decisions.

Heterogeneity of primary outcome measures used in clinical trials of treatments for intermediate, posterior, and panuveitis

BACKGROUND

Uveitis describes a heterogeneous group of conditions characterized by intraocular inflammation. Since most of the sight-threatening forms of uveitis are individually rare, there has been an increasing tendency for clinical trials to group distinct uveitis syndromes together despite clear variations in phenotype which may reflect real aetiological and pathogenetic differences. Furthermore this grouping of distinct syndromes, and the range of manifestations within each uveitis syndrome, leads to a wide range of possible outcome measures. In this study we wished to review the degree of consensus or otherwise in the choice of primary outcome measures for registered clinical trials related to uveitis.

METHODS

Systematic review of data provided in clinical trial registries describing clinical trials dealing with medical treatment of intermediate, posterior, or panuveitis through 01 October 2013. We reviewed 15 on-line clinical trial registries approved by the International Committee of Medical Journal Editors. We identified all that met the following inclusion criteria: prospective, interventional design; target populations with intermediate, posterior or panuveitis; and one or more pre-specified outcome measures that were related to uveitis. Primary outcome measures were classified in terms of type (efficacy or safety or both; single, composite, or multiple); dimension (disease activity, disease damage, measured or patient-reported visual function); and domain (the specific study variable being measured).

RESULTS

Of 195 registered uveitis studies, we identified 104 clinical trials that met inclusion criteria. There were 14 different domains used as primary outcome measures. Among clinical trials that utilized primary outcome measures of treatment efficacy (n = 94), 70 (74 %) used a measure of disease activity (vitreous haze in 40/70 [57 %]; macular oedema in 19/70 [27 %]) and 49 (70 %) used a measure of visual function (visual acuity in all cases). Multiple primary outcome measures were used in 23 (22 %) of 104 clinical trials. With regard to quality, in 12 (12 %) of 104 clinical trials, outcome measures were poorly defined. No clinical trial utilized a patient-reported study variable as primary outcome measure.

CONCLUSIONS

This systematic review highlights the heterogeneity of outcome measures used in recent clinical trials for intermediate, posterior, and panuveitis. Current designs prioritize clinician-observed measures of disease activity and measurement of visual function as outcome measures. This apparent lack of consensus regarding outcome measures for the study of uveitis is a concern, as it prevents comparison of studies and meta-analyses, and weakens the evidence available to stake-holders, from patients to clinicians to regulators, regarding the efficacy and value of a given treatment.

A novel test to compare two treatments based on endpoints involving both nonfatal and fatal events

In a clinical trial comparing two treatment groups, one commonly-used endpoint is time to death. Another is time until the first nonfatal event (if there is one) or until death (if not). Both endpoints have drawbacks. The wrong choice may adversely affect the value of the study by impairing power if deaths are too few (with the first endpoint) or by lessening the role of mortality if not (with the second endpoint). We propose a compromise that provides a simple test based on the time to death if the patient has died or time since randomization augmented by an increment otherwise. The test applies the ordinary two-sample Wilcoxon statistic to these values. The formula for the increment (the same for experimental and control patients) must be specified before the trial starts. In the simplest (and perhaps most useful) case, the increment assumes only two values, according to whether or not the (surviving) patient had a nonfatal event. More generally, the increment depends on the time of the first nonfatal event, if any, and the time since randomization. The test has correct Type I error even though it does not handle censoring in a customary way. For conditions where investigators would face no easy (advance) choice between the two older tests, simulation results favor the new test. An example using a renal-cancer trial is presented.

Composite endpoints in trials of type-2 diabetes

Composite endpoints (CEPs) are being used more frequently as outcomes for trials of drugs in type-2 diabetes. We reviewed the literature to determine how CEPs have been used to date in trials of drugs for type-2 diabetes. A systematic search was undertaken on Medline, Embase and Cochrane databases and Clinicaltrials.gov for randomized controlled trials of currently marketed agents including SGLT-2 inhibitors (dapagliflozin), GLP-1 agonists (exenatide, liraglutide) and DPP-4 inhibitors (linagliptin, saxagliptin, sitagliptin and vildagliptin). CEPs used were identified as well as numbers and percentages of patients achieving each. Thirty-six studies were identified that reported results on ≥1 CEP; 15 different CEPs were reported (7 with 2 components, 8 with 3 components). All CEPs addressed goals recommended by the American Diabetes Association (ADA). All included HbA1c<7%; other endpoints measured weight, blood pressure and hypoglycaemic events. Results were obtained for CEPs from 6 months to 2 years. Rates of achieving CEPs decreased with increasing numbers of components and outcomes assessed. CEPs are becoming used as indicators of clinical outcomes in type-2 diabetes trials, but are still not common. More research is required to identify optimal CEPs. Standardization of outcomes and their reporting is needed.

Opportunities and challenges of combined effect measures based on prioritized outcomes

Many authors have proposed different approaches to combine multiple endpoints in a univariate outcome measure in the literature. In case of binary or time-to-event variables, composite endpoints, which combine several event types within a single event or time-to-first-event analysis are often used to assess the overall treatment effect. A main drawback of this approach is that the interpretation of the composite effect can be difficult as a negative effect in one component can be masked by a positive effect in another. Recently, some authors proposed more general approaches based on a priority ranking of outcomes, which moreover allow to combine outcome variables of different scale levels. These new combined effect measures assign a higher impact to more important endpoints, which is meant to simplify the interpretation of results. Whereas statistical tests and models for binary and time-to-event variables are well understood, the latter methods have not been investigated in detail so far. In this paper, we will investigate the statistical properties of prioritized combined outcome measures. We will perform a systematical comparison to standard composite measures, such as the all-cause hazard ratio in case of time-to-event variables or the absolute rate difference in case of binary variables, to derive recommendations for different clinical trial scenarios. We will discuss extensions and modifications of the new effect measures, which simplify the clinical interpretation. Moreover, we propose a new method on how to combine the classical composite approach with a priority ranking of outcomes using a multiple testing strategy based on the closed test procedure.

Planning and evaluating clinical trials with composite time-to-first-event endpoints in a competing risk framework

Composite endpoints combine several events of interest within a single variable. These are often time-to-first-event data, which are analyzed via survival analysis techniques. To demonstrate the significance of an overall clinical benefit, it is sufficient to assess the test problem formulated for the composite. However, the effect observed for the composite does not necessarily reflect the effects for the components. Therefore, it would be desirable that the sample size for clinical trials using composite endpoints provides enough power not only to detect a clinically relevant superiority for the composite but also to address the components in an adequate way. The single components of a composite endpoint assessed as time-to-first-event define competing risks. We consider multiple test problems based on the cause-specific hazards of competing events to address the problem of analyzing both a composite endpoint and its components. Thereby, we use sequentially rejective test procedures to reduce the power loss to a minimum. We show how to calculate the sample size for the given multiple test problem by using a simply applicable simulation tool in SAS. Our ideas are illustrated by two clinical study examples.

The ADAS-Cog revisited: novel composite scales based on ADAS-Cog to improve efficiency in MCI and early AD trials

BACKGROUND

The Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) has been used widely as a cognitive end point in Alzheimer's Disease (AD) clinical trials. Efforts to treat AD pathology at earlier stages have also used ADAS-Cog, but failure in these trials can be difficult to interpret because the scale has well-known ceiling effects that limit its use in mild cognitive impairment (MCI) and early AD. A wealth of data exists in ADAS-Cog from both historical trials and contemporary longitudinal natural history studies that can provide insights about parts of the scale that may be better suited for MCI and early AD trials.

METHODS

Using Alzheimer's Disease Neuroimaging Initiative study data, we identified the most informative cognitive measures from the ADAS-Cog and other available scales. We used cross-sectional analyses to characterize trajectories of ADAS-Cog and its individual subscales, as well as other cognitive, functional, or global measures across disease stages. Informative measures were identified based on standardized mean of 2-year change from baseline and were combined into novel composite endpoints. We assessed performance of the novel endpoints based on sample size requirements for a 2-year clinical trial. A bootstrap validation procedure was also undertaken to assess the reproducibility of the standardized mean changes of the selected measures and the corresponding composites.

RESULTS

All proposed novel endpoints have improved standardized mean changes and thus improved statistical power compared with the ADAS-Cog 11. Further improvements were achieved by using cognitive-functional composites. Combining the novel composites with an enrichment strategy based on cerebral spinal fluid beta-amyloid (Aβ(1-42)) in a 2-year trial yielded gains in power of 20% to 40% over ADAS-Cog 11, regardless of the novel measure considered.

CONCLUSION

An empirical, data-driven approach with existing instruments was used to derive novel composite scales based on ADAS-Cog 11 with improved performance characteristics for MCI and early AD clinical trials. Together with patient enrichment based on Aβ(1-42) pathology, these modified endpoints may allow more efficient clinical trials in these populations and can be assessed without modifying current test administration procedures in ongoing trials.

Endpoint selection and relative (versus absolute) risk reporting in published medication trials

BACKGROUND

The use of surrogate and composite endpoints, disease-specific mortality as an endpoint, and relative (rather than absolute) risk reporting in clinical trials may produce results that are misleading or difficult to interpret.

OBJECTIVE

To describe the prevalence of these endpoints and of relative risk reporting in medication trials. DESIGN AND MAIN MEASURES: We analyzed all randomized medication trials published in the six highest impact general medicine journals between June 1, 2008 and September 30, 2010 and determined the percentage using these endpoints and the percentage reporting results in the abstract exclusively in relative terms.

KEY RESULTS

We identified 316 medication trials, of which 116 (37%) used a surrogate primary endpoint and 106 (34%) used a composite primary endpoint. Among 118 trials in which the primary endpoint involved mortality, 32 (27%) used disease-specific mortality rather than all-cause mortality. Among 157 trials with positive results, 69 (44%) reported these results in the abstract exclusively in relative terms. Trials using surrogate endpoints and disease-specific mortality as an endpoint were more likely to be exclusively commercially funded (45% vs. 29%, difference 15% [95% CI 5%-26%], P = 0.004, and 39% vs. 16%, difference 22% [95% CI 6%-37%], P = 0.007, respectively). Trials using surrogate endpoints were more likely to report positive results (66% vs. 49%, difference 17% [95% CI 5%-28%], P = 0.006) while those using mortality endpoints were less likely to be positive (46% vs. 62%, difference -16% [95% CI -27%--4%], P = 0.01).

CONCLUSIONS

The use of surrogate and composite endpoints, endpoints involving disease-specific mortality, and relative risk reporting is common. Articles should highlight the limitations of these endpoints and should report results in absolute terms.