Solutions for surrogacy validation with longitudinal outcomes for a gene therapy

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

Designing and analyzing clinical trials with composite outcomes: consideration of possible treatment differences between the individual outcomes

When the individual outcomes within a composite outcome appear to have different treatment effects, either in magnitude or direction, researchers may question the validity or appropriateness of using this composite outcome as a basis for measuring overall treatment effect in a randomized controlled trial. The question remains as to how to distinguish random variation in estimated treatment effects from important heterogeneity within a composite outcome. This paper suggests there may be some utility in directly testing the assumption of homogeneity of treatment effect across the individual outcomes within a composite outcome. We describe a treatment heterogeneity test for composite outcomes based on a class of models used for the analysis of correlated data arising from the measurement of multiple outcomes for the same individuals. Such a test may be useful in planning a trial with a primary composite outcome and at trial end with final analysis and presentation. We demonstrate how to determine the statistical power to detect composite outcome treatment heterogeneity using the POISE Trial data. Then we describe how this test may be incorporated into a presentation of trial results with composite outcomes. We conclude that it may be informative for trialists to assess the consistency of treatment effects across the individual outcomes within a composite outcome using a formalized methodology and the suggested test represents one option.     

Estimating individualized treatment regimes from crossover designs

The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a prespecified outcome is of particular interest. Several methods have been proposed for estimating an optimal ITR from clinical trial data in the parallel group setting where each subject is randomized to a single intervention. However, little work has been done in the area of estimating the optimal ITR from crossover study designs. Such designs naturally lend themselves to precision medicine since they allow for observing the response to multiple treatments for each patient. In this paper, we introduce a method for estimating the optimal ITR using data from a 2 × 2 crossover study with or without carryover effects. The proposed method is similar to policy search methods such as outcome weighted learning; however, we take advantage of the crossover design by using the difference in responses under each treatment as the observed reward. We establish Fisher and global consistency, present numerical experiments, and analyze data from a feeding trial to demonstrate the improved performance of the proposed method compared to standard methods for a parallel study design.     

Inference for the effect of treatment on survival probability in randomized trials with noncompliance and administrative censoring

In many clinical studies with a survival outcome, administrative censoring occurs when follow-up ends at a prespecified date and many subjects are still alive. An additional complication in some trials is that there is noncompliance with the assigned treatment. For this setting, we study the estimation of the causal effect of treatment on survival probability up to a given time point among those subjects who would comply with the assignment to both treatment and control. We first discuss the standard instrumental variable (IV) method for survival outcomes and parametric maximum likelihood methods, and then develop an efficient plug-in nonparametric empirical maximum likelihood estimation (PNEMLE) approach. The PNEMLE method does not make any assumptions on outcome distributions, and makes use of the mixture structure in the data to gain efficiency over the standard IV method. Theoretical results of the PNEMLE are derived and the method is illustrated by an analysis of data from a breast cancer screening trial. From our limited mortality analysis with administrative censoring times 10 years into the follow-up, we find a significant benefit of screening is present after 4 years (at the 5% level) and this persists at 10 years follow-up.     

A general framework for subgroup detection via one-step value difference estimation

Recent statistical methodology for precision medicine has focused on either identification of subgroups with enhanced treatment effects or estimating optimal treatment decision rules so that treatment is allocated in a way that maximizes, on average, predefined patient outcomes. Less attention has been given to subgroup testing, which involves evaluation of whether at least a subgroup of the population benefits from an investigative treatment, compared to some control or standard of care. In this work, we propose a general framework for testing for the existence of a subgroup with enhanced treatment effects based on the difference of the estimated value functions under an estimated optimal treatment regime and a fixed regime that assigns everyone to the same treatment. Our proposed test does not require specification of the parametric form of the subgroup and allows heterogeneous treatment effects within the subgroup. The test applies to cases when the outcome of interest is either a time-to-event or a (uncensored) scalar, and is valid at the exceptional law. To demonstrate the empirical performance of the proposed test, we study the type I error and power of the test statistics in simulations and also apply our test to data from a Phase III trial in patients with hematological malignancies.     

Parameter estimation in longitudinal studies with outcome-dependent follow-up

In many observational studies, individuals are measured repeatedly over time, although not necessarily at a set of prespecified occasions. Instead, individuals may be measured at irregular intervals, with those having a history of poorer health outcomes being measured with somewhat greater frequency and regularity; i.e., those individuals with poorer health outcomes may have more frequent follow-up measurements and the intervals between their repeated measurements may be shorter. In this article, we consider estimation of regression parameters in models for longitudinal data where the follow-up times are not fixed by design but can depend on previous outcomes. In particular, we focus on general linear models for longitudinal data where the repeated measures are assumed to have a multivariate Gaussian distribution. We consider assumptions regarding the follow-up time process that result in the likelihood function separating into two components: one for the follow-up time process, the other for the outcome process. The practical implication of this separation is that the former process can be ignored when making likelihood-based inferences about the latter; i.e., maximum likelihood (ML) estimation of the regression parameters relating the mean of the longitudinal outcomes to covariates does not require that a model for the distribution of follow-up times be specified. As a result, standard statistical software, e.g., SAS PROC MIXED (Littell et al., 1996, SAS System for Mixed Models), can be used to analyze the data. However, we also demonstrate that misspecification of the model for the covariance among the repeated measures will, in general, result in regression parameter estimates that are biased. Furthermore, results of a simulation study indicate that the potential bias due to misspecification of the covariance can be quite considerable in this setting. Finally, we illustrate these results using data from a longitudinal observational study (Lipshultz et al., 1995, New England Journal of Medicine 332, 1738-1743) that explored the cardiotoxic effects of doxorubicin chemotherapy for the treatment of acute lymphoblastic leukemia in children.     

Power of the Wilcoxon–Mann–Whitney test for non‐inferiority in the presence of death‐censored observations

In clinical trials with patients in a critical state, death may preclude measurement of a quantitative endpoint of interest, and even early measurements, for example for intention‐to‐treat analysis, may not be available. For example, a non‐negligible proportion of patients with acute pulmonary embolism will die before 30 day measurements on the efficacy of thrombolysis can be obtained. As excluding such patients may introduce bias, alternative analyses, and corresponding means for sample size calculation are needed. We specifically consider power analysis in a randomized clinical trial setting in which the goal is to demonstrate noninferiority of a new treatment as compared to a reference treatment. Also, a nonparametric approach may be needed due to the distribution of the quantitative endpoint of interest. While some approaches have been developed in a composite endpoint setting, our focus is on the continuous endpoint affected by death‐related censoring, for which no approach for noninferiority is available. We propose a solution based on ranking the quantitative outcome and assigning worst rank scores to the patients without quantitative outcome because of death. Based on this, we derive power formulae for a noninferiority test in the presence of death‐censored observations, considering settings with and without ties. The approach is illustrated for an exemplary clinical trial in pulmonary embolism. The results there show a substantial effect of death on power, also depending on differential effects in the two trial arms. Therefore, use of the proposed formulae is advisable whenever there is death to be expected before measurement of a quantitative primary outcome of interest.     

Randomization inference with general interference and censoring

Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where it is a priori assumed that there is “partial interference,” in the sense that individuals can be partitioned into groups wherein there is no interference between individuals in different groups. Bowers et al. (2012, Political Anal, 21, 97–124) and Bowers et al. (2016, Political Anal, 24, 395–403) consider randomization-based inferential methods that allow for more general interference structures in the context of randomized experiments. In this paper, extensions of Bowers et al. that allow for failure time outcomes subject to right censoring are proposed. Permitting right-censored outcomes is challenging because standard randomization-based tests of the null hypothesis of no treatment effect assume that whether an individual is censored does not depend on treatment. The proposed extension of Bowers et al. to allow for censoring entails adapting the method of Wang et al. (2010, Biostatistics, 11, 676–692) for two-sample survival comparisons in the presence of unequal censoring. The methods are examined via simulation studies and utilized to assess the effects of cholera vaccination in an individually randomized trial of 73 000 children and women in Matlab, Bangladesh.     

Estimation in regression models for longitudinal binary data with outcome-dependent follow-up

In many observational studies, individuals are measured repeatedly over time, although not necessarily at a set of pre-specified occasions. Instead, individuals may be measured at irregular intervals, with those having a history of poorer health outcomes being measured with somewhat greater frequency and regularity. In this paper, we consider likelihood-based estimation of the regression parameters in marginal models for longitudinal binary data when the follow-up times are not fixed by design, but can depend on previous outcomes. In particular, we consider assumptions regarding the follow-up time process that result in the likelihood function separating into two components: one for the follow-up time process, the other for the outcome measurement process. The practical implication of this separation is that the follow-up time process can be ignored when making likelihood-based inferences about the marginal regression model parameters. That is, maximum likelihood (ML) estimation of the regression parameters relating the probability of success at a given time to covariates does not require that a model for the distribution of follow-up times be specified. However, to obtain consistent parameter estimates, the multinomial distribution for the vector of repeated binary outcomes must be correctly specified. In general, ML estimation requires specification of all higher-order moments and the likelihood for a marginal model can be intractable except in cases where the number of repeated measurements is relatively small. To circumvent these difficulties, we propose a pseudolikelihood for estimation of the marginal model parameters. The pseudolikelihood uses a linear approximation for the conditional distribution of the response at any occasion, given the history of previous responses. The appeal of this approximation is that the conditional distributions are functions of the first two moments of the binary responses only. When the follow-up times depend only on the previous outcome, the pseudolikelihood requires correct specification of the conditional distribution of the current outcome given the outcome at the previous occasion only. Results from a simulation study and a study of asymptotic bias are presented. Finally, we illustrate the main results using data from a longitudinal observational study that explored the cardiotoxic effects of doxorubicin chemotherapy for the treatment of acute lymphoblastic leukemia in children.     

Identification of subpopulations with distinct treatment benefit rate using the Bayesian tree

Characterization of a subpopulation by the difference in marginal means of the outcome under the intervention and control may not be sufficient to provide informative guidance for individual decision and public policy making. Specifically, often we are interested in the treatment benefit rate (TBR), that is, the probability of benefitting an intervention in a meaningful way. For binary outcomes, TBR is the proportion that has “unfavorable” outcome under the control and “favorable” outcome under the intervention. Identification of subpopulations with distinct TBR by baseline characteristics will have significant implications in clinical setting where a medical intervention with potential negative health impact is under consideration for a given patient. In addition, these subpopulations with unique TBR set the basis for guidance in implementing the intervention toward a more personalized scheme of treatment. In this article, we propose a Bayesian tree based latent variable model to seek subpopulations with distinct TBR. Our method offers a nonparametric Bayesian framework that accounts for the uncertainty in estimating potential outcomes and allows more exhaustive search of the partitions of the baseline covariates space. The method is evaluated through a simulation study and applied to a randomized clinical trial of implantable cardioverter defibrillators to reduce mortality.     

Causal inference with outcomes truncated by death in multiarm studies

It is challenging to evaluate causal effects when the outcomes of interest suffer from truncation-by-death in many clinical studies; that is, outcomes cannot be observed if patients die before the time of measurement. To address this problem, it is common to consider average treatment effects by principal stratification, for which, the identifiability results and estimation methods with a binary treatment have been established in previous literature. However, in multiarm studies with more than two treatment options, estimation of causal effects becomes more complicated and requires additional techniques. In this article, we consider identification, estimation, and bounds of causal effects with multivalued ordinal treatments and the outcomes subject to truncation-by-death. We define causal parameters of interest in this setting and show that they are identifiable either using some auxiliary variable or based on linear model assumption. We then propose a semiparametric method for estimating the causal parameters and derive their asymptotic results. When the identification conditions are invalid, we derive sharp bounds of the causal effects by use of covariates adjustment. Simulation studies show good performance of the proposed estimator. We use the estimator to analyze the effects of a four-level chronic toxin on fetal developmental outcomes such as birth weight in rats and mice, with data from a developmental toxicity trial conducted by the National Toxicology Program. Data analyses demonstrate that a high dose of the toxin significantly reduces the weights of pups.     

Testing for heterogeneity in the utility of a surrogate marker

In studies that require long-term and/or costly follow-up of participants to evaluate a treatment, there is often interest in identifying and using a surrogate marker to evaluate the treatment effect. While several statistical methods have been proposed to evaluate potential surrogate markers, available methods generally do not account for or address the potential for a surrogate to vary in utility or strength by patient characteristics. Previous work examining surrogate markers has indicated that there may be such heterogeneity, that is, that a surrogate marker may be useful (with respect to capturing the treatment effect on the primary outcome) for some subgroups, but not for others. This heterogeneity is important to understand, particularly if the surrogate is to be used in a future trial to replace the primary outcome. In this paper, we propose an approach and estimation procedures to measure the surrogate strength as a function of a baseline covariate W and thus examine potential heterogeneity in the utility of the surrogate marker with respect to W. Within a potential outcome framework, we quantify the surrogate strength/utility using the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate. We propose testing procedures to test for evidence of heterogeneity, examine finite sample performance of these methods via simulation, and illustrate the methods using AIDS clinical trial data.     

Mixed effects logistic regression models for multiple longitudinal binary functional limitation responses with informative drop-out and confounding by baseline outcomes

In the context of analyzing multiple functional limitation responses collected longitudinally from the Longitudinal Study of Aging (LSOA), we investigate the heterogeneity of these outcomes with respect to their associations with previous functional status and other risk factors in the presence of informative drop-out and confounding by baseline outcomes. We accommodate the longitudinal nature of the multiple outcomes with a unique extension of the nested random effects logistic model with an autoregressive structure to include drop-out and baseline outcome components with shared random effects. Estimation of fixed effects and variance components is by maximum likelihood with numerical integration. This shared parameter selection model assumes that drop-out is conditionally independent of the multiple functional limitation outcomes given the underlying random effect representing an individual's trajectory of functional status across time. Whereas it is not possible to fully assess the adequacy of this assumption, we assess the robustness of this approach by varying the assumptions underlying the proposed model such as the random effects structure, the drop-out component, and omission of baseline functional outcomes as dependent variables in the model. Heterogeneity among the associations between each functional limitation outcome and a set of risk factors for functional limitation, such as previous functional limitation and physical activity, exists for the LSOA data of interest. Less heterogeneity is observed among the estimates of time-level random effects variance components that are allowed to vary across functional outcomes and time. We also note that. under an autoregressive structure, bias results from omitting the baseline outcome component linked to the follow-up outcome component by subject-level random effects.     

Short- and long-term effects of mud-bath treatment on hand osteoarthritis: a randomized clinical trial

The aim of this study was to evaluate both the short-term and the long-term effectiveness of spa therapy in patients with primary hand osteoarthritis (OA). This was a prospective randomized, single blind controlled trial. Sixty outpatients with primary bilateral hand OA were included in the study and randomized to one of two groups. One group (n?=?30) was treated with 12 daily local mud packs and generalized thermal baths with a sulfate-calcium-magnesium-fluorides mineral water added to usual treatment. The control group (n?=?30) continued regular outpatient care routine (exercise, NSAIDs and/or analgesics). Each patient was examined at baseline, after 2 weeks, and after 3, 6, 9 and 12 months. Primary outcome measures were global spontaneous hand pain on a visual analogue scale (VAS) and the functional index for hand osteoarthritis (FIHOA) score; secondary outcomes were health assessment questionnaire (HAQ), duration of morning stiffness, medical outcomes study 36-item short form (SF-36) and symptomatic drugs consumption. Our results demonstrated that the efficacy of spa therapy was significant in all the assessed parameters, both at the end of therapy and after 3 months; the values of FIHOA, HAQ and drugs consumption continued to be significantly better after 6 months in comparison with baseline. There were no significant modifications of the parameters throughout the follow-up in the control group. Differences between the two groups were significant for all parameters at the 15th day and at 3 months follow-up; regarding FIHOA, HAQ, and symptomatic drugs consumption, the difference between the two groups persisted and was significant at 6month follow-up. Tolerability of spa therapy seemed to be good. In conclusion, our results confirm that the beneficial effects of spa therapy in patients with hand OA last over time.     

Efficacy of intervention strategies in a brachial plexus global avulsion model in the rat

The treatment of brachial plexus avulsion lesions invariably involves the use of neurotization procedures. Although some of these therapeutic strategies have been used for the past 20 years to restore selective function to the injured extremity, the individual efficacy of these nerve transfers has not been measured objectively, thereby rendering the prognostication of outcomes for these techniques a major problem. Using a true global avulsion model, the present study compares outcomes of the various neurotization procedures for the first time. The strength of this experimental model is that each nerve transfer tested leads to a common terminal pathway involving a single target-namely, the biceps muscle. Thus, quantitative measurements of biceps restoration will provide strong clues to the power of axonal regrowth of that particular motor pool. This study also introduces the Terzis grooming test, a modified behavioral test that can be quantified and that can provide an overall functional scale in the assessment of outcome. Thirty-five Sprague-Dawley rats were divided into seven groups, with each group testing a different motor donor for biceps reinnervation. The ipsilateral brachial plexus was globally avulsed in all animals, with the exception of the ipsilateral C7 group, and the respective motor donor coapted in an end-to-end fashion to the musculocutaneous nerve. Functional outcomes were measured by the Terzis grooming test, electromyography, biceps muscle force measurements, motor end plate counts, and quantitative axonal morphometry. The values of the different parameters were expressed as a standard score on a common scale. The relative standings of each group on each parameter were compared. Superior outcome was observed in the phrenic, the hypoglossal, and the ipsilateral C7 groups.     

Nonparametric and semiparametric trend analysis for stratified recurrence times

Recurrent event data are frequently encountered in longitudinal follow-up studies when the occurrences of multiple events are considered as the major outcomes. Suppose that the recurrent events are of the same type and the variable of interest is the recurrence time between successive events. In many applications, the distributional pattern of recurrence times can be used as an index for the progression of a disease. Such a distributional pattern is important for understanding the natural history of a disease or for confirming long-term treatment effect. In this article, we discuss and define the comparability of recurrence times. Nonparametric and semiparametric methods are developed for testing trend of recurrence time distributions and estimating trend parameters in regression models. The construction of the methods is based on comparable recurrence times from stratified data. A real data example is presented to illustrate the use of methodology.     

Prolonged conservative care versus early surgery in patients with sciatica caused by lumbar disc herniation: two year results of a randomised controlled trial

Objectives To evaluate the effects of early lumbar disc surgery compared with prolonged conservative care for patients with sciatica over two years of follow-up.Design Randomised controlled trial.Setting Nine Dutch hospitals.Participants 283 patients with 6-12 weeks of sciatica.Interventions Early surgery or an intended six months of continued conservative treatment, with delayed surgery if needed.Main outcome measures Scores from Roland disability questionnaire for sciatica, visual analogue scale for leg pain, and Likert self rating scale of global perceived recovery.Results Of the 141 patients assigned to undergo early surgery, 125 (89%) underwent microdiscectomy. Of the 142 patients assigned to conservative treatment, 62 (44%) eventually required surgery, seven doing so in the second year of follow-up. There was no significant overall difference between treatment arms in disability scores during the first two years (P=0.25). Improvement in leg pain was faster for patients randomised to early surgery, with a significant difference between “areas under the curves” over two years (P=0.05). This short term benefit of early surgery was no longer significant by six months and continued to narrow between six months and 24 months. Patient satisfaction decreased slightly between one and two years for both groups. At two years 20% of all patients reported an unsatisfactory outcome.Conclusions Early surgery achieved more rapid relief of sciatica than conservative care, but outcomes were similar by one year and these did not change during the second year.Trial Registry ISRCT No 26872154.     

Effects of curcumin supplementation on blood glucose,insulin resistance and androgens in patients with polycystic ovary syndrome: A randomized double-blind placebo-controlled clinical trial

BackgroundCurcumin is a biologically active phytochemical ingredient found in turmeric. It has several pharmacologic effects that might benefit patients with polycystic ovary syndrome (PCOS).ObjectiveWe hypothesized curcumin to be effective in improving blood sugar levels, insulin resistance and hyperandrogenism in individuals with PCOS.MethodsIn a randomized double-blind placebo-controlled trial, individuals with PCOS were treated with curcumin (500 mg three times daily) or placebo for 12 weeks. Primary outcome measures were fasting plasma glucose (FPG), fasting insulin (FI), sex hormone levels, and hirsutism (Ferriman-Gallwey [mFG] score). Secondary outcomes included anthropometric measurements.ResultsOf 72 randomized individuals, 67 completed the trial. The two groups were comparable at baseline. At the end of the study, FPG and Dehydroepiandrosterone levels had decreased significantly in the intervention group compared to control (difference of change (post-pre) between intervention and placebo groups: -4.11 mg/dL; 95% CI: -8.35, -0.35 mg/dL; p = 0.033 and -26.53 microg/dL; 95% CI: -47.99, -4.34 µg/dL; p = 0.035, respectively). We also observed a statistically non-significant increase (p = 0.082) in Estradiol levels in the intervention group compared to control. No serious adverse events were reported throughout the trial.ConclusionsCurcumin might be a safe and useful supplement to ameliorate PCOS-associated hyperandrogenemia and hyperglycemia. However, longer trials investigating different dosages in longer durations are needed to underpin these findings.     

Investigation of the effect of Interleukin-1 receptor antagonist (IL-1ra) on markers of inflammation in non-ST elevation acute coronary syndromes (The MRC-ILA-HEART Study)

Background

Point of care testing (PoCT) may be a useful adjunct in the management of chronic conditions in general practice (GP). The provision of pathology test results at the time of the consultation could lead to enhanced clinical management, better health outcomes, greater convenience and satisfaction for patients and general practitioners (GPs), and savings in costs and time. It could also result in inappropriate testing, increased consultations and poor health outcomes resulting from inaccurate results. Currently there are very few randomised controlled trials (RCTs) in GP that have investigated these aspects of PoCT.

Design/Methods

The Point of Care Testing in General Practice Trial (PoCT Trial) was an Australian Government funded multi-centre, cluster randomised controlled trial to determine the safety, clinical effectiveness, cost effectiveness and satisfaction of PoCT in a GP setting. The PoCT Trial covered an 18 month period with the intervention consisting of the use of PoCT for seven tests used in the management of patients with diabetes, hyperlipidaemia and patients on anticoagulant therapy. The primary outcome measure was the proportion of patients within target range, a measure of therapeutic control. In addition, the PoCT Trial investigated the safety of PoCT, impact of PoCT on patient compliance to medication, stakeholder satisfaction, cost effectiveness of PoCT versus laboratory testing, and influence of geographic location.

Discussion

The paper provides an overview of the Trial Design, the rationale for the research methodology chosen and how the Trial was implemented in a GP environment. The evaluation protocol and data collection processes took into account the large number of patients, the broad range of practice types distributed over a large geographic area, and the inclusion of pathology test results from multiple pathology laboratories. The evaluation protocol developed reflects the complexity of the Trial setting, the Trial Design and the approach taken within the funding provided. The PoCT Trial is regarded as a pragmatic RCT, evaluating the effectiveness of implementing PoCT in GP and every effort was made to ensure that, in these circumstances, internal and external validity was maintained.

Trial Registration

12612605000272695