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Subgroup analysis has been discussed extensively for efficacy studies. When there are heterogeneous treatment effects across subgroups, an important question is whether the treatment-by-subgroup interaction is qualitative or quantitative, where the former is usually the concern. For equivalence, however, we found no formal definition of qualitative versus quantitative interaction. In this article, we define quantitative versus concordant and discordant qualitative treatment-by-subgroup interactions for equivalence studies for the first time. We identified the most concerning discordant quantitative interaction unique to equivalence studies, where a seemingly equivalent overall treatment effect can come from the offset of the opposite directions of inequivalence in subgroups where none of the subgroups are equivalent. We also evaluated an appropriate easy-to-apply criterion: a significance threshold (i.e., alpha) for the treatment-by-subgroup interaction that warrants more investigation of heterogeneity across subgroups for equivalence studies. Based on simulation, we recommend that for a typical two-way crossover PK bioequivalence study, if the interest is in quantitative interaction, then α= 0.1 (or even higher) is recommended as a threshold; if the interest is in qualitative interaction, which is usually the case in practice, then α= 0.05 is a sufficient threshold. Use of criterion can help regulators and sponsors identify important heterogeneity in equivalence assessment. [ABSTRACT FROM AUTHOR]

Selection of treatments to fit the specific needs for a certain patient is one major challenge in modern medicine. Personalized treatments rely on established patient–treatment interactions. In recent years, various statistical methods for the identification and estimation of interactions between relevant covariates and treatment were proposed. In this article, different available methods for detection and estimation of a covariate–treatment interaction for a time-to-event outcome, namely the standard Cox regression model assuming a linear interaction, the fractional polynomials approach for interaction, the modified outcome approach, the local partial-likelihood approach, and STEPP (Subpopulation Treatment Effect Pattern Plots) were applied to data from the SPACE trial, a randomized clinical trial comparing stent-protected angioplasty (CAS) to carotid endarterectomy (CEA) in patients with symptomatic stenosis, with the aim to analyse the interaction between age and treatment. Time from primary intervention to the first relevant event (any stroke or death) was considered as outcome parameter. The analyses suggest a qualitative interaction between patient age and treatment indicating a lower risk after treatment with CAS compared to CEA for younger patients, while for elderly patients a lower risk after CEA was observed. Differences in the statistical methods regarding the observed results, applicability, and interpretation are discussed. [ABSTRACT FROM AUTHOR]

Treatment response heterogeneity has long been observed in patients affected by chronic diseases. Administering an individualized treatment rule (ITR) offers an opportunity to tailor treatment strategies according to patient-specific characteristics. Overly complex machine learning methods for estimating ITRs may produce treatment rules that have higher benefit but lack transparency and interpretability. In clinical practices, it is desirable to derive a simple and interpretable ITR while maintaining certain optimality that leads to improved benefit in subgroups of patients, if not on the overall sample. In this work, we propose a tree-based robust learning method to estimate optimal piecewise linear ITRs and identify subgroups of patients with a large benefit. We achieve these goals by simultaneously identifying qualitative and quantitative interactions through a tree model, referred to as the composite interaction tree (CITree). We show that it has improved performance compared to existing methods on both overall sample and subgroups via extensive simulation studies. Lastly, we fit CITree to Research Evaluating the Value of Augmenting Medication with Psychotherapy trial for treating patients with major depressive disorders, where we identified both qualitative and quantitative interactions and subgroups of patients with a large benefit. [ABSTRACT FROM AUTHOR]

Composite endpoints are frequently used in clinical outcome trials to provide more endpoints, thereby increasing statistical power. A key requirement for a composite endpoint to be meaningful is the absence of the so-called qualitative heterogeneity to ensure a valid overall interpretation of any treatment effect identified. Qualitative heterogeneity occurs when individual components of a composite endpoint exhibit differences in the direction of a treatment effect. In this paper, we develop a general statistical method to test for qualitative heterogeneity, that is to test whether a given set of parameters share the same sign. This method is based on the intersection-union principle and, provided that the sample size is large, is valid whatever the model used for parameters estimation. We propose two versions of our testing procedure, one based on a random sampling from a Gaussian distribution and another version based on bootstrapping. Our work covers both the case of completely observed data and the case where some observations are censored which is an important issue in many clinical trials. We evaluated the size and power of our proposed tests by carrying out some extensive Monte Carlo simulations in the case of multivariate time to event data. The simulations were designed under a variety of conditions on dimensionality, censoring rate, sample size and correlation structure. Our testing procedure showed very good performances in terms of statistical power and type I error. The proposed test was applied to a data set from a single-center, randomized, double-blind controlled trial in the area of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Summary: In clinical practice, an informative and practically useful treatment rule should be simple and transparent. However, because simple rules are likely to be far from optimal, effective methods to construct such rules must guarantee performance, in terms of yielding the best clinical outcome (highest reward) among the class of simple rules under consideration. Furthermore, it is important to evaluate the benefit of the derived rules on the whole sample and in pre‐specified subgroups (e.g., vulnerable patients). To achieve both goals, we propose a robust machine learning method to estimate a linear treatment rule that is guaranteed to achieve optimal reward among the class of all linear rules. We then develop a diagnostic measure and inference procedure to evaluate the benefit of the obtained rule and compare it with the rules estimated by other methods. We provide theoretical justification for the proposed method and its inference procedure, and we demonstrate via simulations its superior performance when compared to existing methods. Lastly, we apply the method to the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial on major depressive disorder and show that the estimated optimal linear rule provides a large benefit for mildly depressed and severely depressed patients but manifests a lack‐of‐fit for moderately depressed patients. [ABSTRACT FROM AUTHOR]

Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. The discrete instrumental variable model has testable implications for the law of the observed data. However, current assessments of instrumental validity are typically based solely on subjectmatter arguments rather than these testable implications, partly due to a lack of formal statistical tests with known properties. In this paper, we develop simple procedures for testing the binary instrumental variable model. Our methods are based on existing techniques for comparing two treatments, such as the t-test and the Gail-Simon test. We illustrate the importance of testing the instrumental variable model by evaluating the exogeneity of college proximity using the National Longitudinal Survey of Young Men. [ABSTRACT FROM AUTHOR]

Objective: The detection of subgroups involved in qualitative treatment–subgroup interactions (i.e., for one subgroup of clients treatment A outperforms treatment B, whereas for another the reverse holds true) is crucial for personalized health. In typical Randomized Controlled Trials (RCTs), the combination of a lack of a priori hypotheses and a large number of possible moderators leaves current methods insufficient to detect subgroups involved in such interactions. A recently developed method, QUalitative INteraction Trees (QUINT), offers a solution. However, the paper in which QUINT has been introduced is not easily accessible for non-methodologists. In this paper, we want to review the conceptual basis of QUINT in a nontechnical way, and illustrate its relevance for psychological applications.Method: We present a concise introduction into QUINT along with a summary of available evidence on its performance. Subsequently, we subject RCT data on the effect of motivational interviewing in a treatment for substance abuse disorders to a reanalysis with QUINT. As outcome variables, we focus on measures of retention and substance use.Results: A qualitative treatment–subgroup interaction is found for retention. By contrast, no qualitative interaction is detected for substance use.Conclusions: QUINT may lead to insightful and well-interpretable results with straightforward implications for personalized treatment assignment. [ABSTRACT FROM PUBLISHER]

This study considers the detection of treatment-by-subset interactions in a stratified, randomised clinical trial with a binary-response variable. The focus lies on the detection of qualitative interactions. In addition, the presented method is useful more generally, as it can assess the inconsistency of the treatment effects among strata by using an a priori-defined inconsistency margin. The methodology presented is based on the construction of ratios of treatment effects. In addition to multiplicity-adjusted p-values, simultaneous confidence intervals are recommended to use in detecting the source and the amount of a potential qualitative interaction. The proposed method is demonstrated on a multi-regional trial using the open-source statistical software R. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

When two alternative treatments (A and B) are available, some subgroup of patients may display a better outcome with treatment A than with B, whereas for another subgroup, the reverse may be true. If this is the case, a qualitative (i.e., disordinal) treatment-subgroup interaction is present. Such interactions imply that some subgroups of patients should be treated differently and are therefore most relevant for personalized medicine. In case of data from randomized clinical trials with many patient characteristics that could interact with treatment in a complex way, a suitable statistical approach to detect qualitative treatment-subgroup interactions is not yet available. As a way out, in the present paper, we propose a new method for this purpose, called QUalitative INteraction Trees (QUINT). QUINT results in a binary tree that subdivides the patients into terminal nodes on the basis of patient characteristics; these nodes are further assigned to one of three classes: a first for which A is better than B, a second for which B is better than A, and an optional third for which type of treatment makes no difference. Results of QUINT on simulated data showed satisfactory performance, with regard to optimization and recovery. Results of an application to real data suggested that, compared with other approaches, QUINT provided a more pronounced picture of the qualitative interactions that are present in the data. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Sprinting is often seen in a variety of sports. Focusing one's attention externally before sprinting has been demonstrated to boost sprint performance. The present study aimed to systematically review previous findings on the impact of external focus (EF), in comparison to internal focus (IF), on sprint performance. A literature search was conducted in five electronic databases (APA PsycINFO, PubMed, Scopus, SPORTDiscus, and Web of Science). A random-effects model was used to pool Hedge's g with 95% confidence intervals (CIs). The meta-analysis included six studies with a total of 10 effect sizes and 166 participants. In general, the EF condition outperformed the IF condition in sprint performance ( g = 0.279, 95% CI [0.088, 0.470], p = 0.004). The subgroup analysis, which should be viewed with caution, suggested that the benefits associated with the EF strategy were significant in low-skill sprinters ( g = 0.337, 95% CI [0.032, 0.642], p = 0.030) but not significant in high-skill sprinters ( g = 0.246, 95% CI [-0.042, 0.533], p = 0.094), although no significant difference was seen between these subgroups ( p = 0.670). The reported gain in sprint performance due to attentional focus has practical implications for coaches and athletes, as making tiny adjustments in verbal instructions can lead to significant behavioral effects of great importance in competitive sports.

Longitudinal studies suffer from patient dropout. The dropout process may be informative if there exists an association between dropout patterns and the rate of change in the response over time. Multiple patterns are plausible in that different causes of dropout might contribute to different patterns. These multiple patterns can be dichotomized into two groups: quantitative and qualitative interaction. Quantitative interaction indicates that each of the multiple sources is biasing the estimate of the rate of change in the same direction, although with differing magnitudes. Alternatively, qualitative interaction results in the multiple sources biasing the estimate of the rate of change in opposing directions. Qualitative interaction is of special concern, since it is less likely to be detected by conventional methods and can lead to highly misleading slope estimates. We explore a test for qualitative interaction based on simultaneous confidence intervals. The test accommodates the realistic situation where reasons for dropout are not fully understood, or even entirely unknown. It allows for an additional level of clustering among participating subjects. We apply these methods to a study exploring tumor growth rates in mice as well as a longitudinal study exploring rates of change in cognitive functioning for Alzheimer's patients. [ABSTRACT FROM AUTHOR]

We consider the role of interaction tests in the context of active-controlled clinical trials that aim to demonstrate the noninferiority of an experimental treatment compared to a standard (control) treatment. When the subjects can be grouped into strata (e.g., study sites, gender, race, etc.), there may be a desire to determine whether the experimental treatment is noninferior to the standard in each of the strata. We present five possible analysis strategies to test for heterogeneity of relative treatment effects among strata. These strategies are either identical to or straightforward modifications of strategies that can be used to test for interaction when the objective of the study is to show differences rather than noninferiority. The various analysis strategies implicitly depend on different definitions of interaction. Power of the various tests will be low, a phenomenon that often occurs when testing for interaction. We present simulation results to quantify the power and type I error rates under different scenarios and an example to demonstrate the proposed tests. None of the analysis strategies is best under every parameter configuration. The tests may be best used in a descriptive or exploratory manner. Extensions to two-sided equivalence testing are also discussed. [ABSTRACT FROM AUTHOR]

The shifted multiplicative model (SHMM) is used in an exploratory step-down method for identifying subsets of environments in which genotypic effects are 'separable' from environmental effects. Subsets of environments are chosen on the basis of a SHMM analysis of the entire data set. SHMM analyses of the subsets may indicate a need for further subdivision and/or suggest that a different subdivision at the previous stage should be tried. The process continues until SHMM analysis indicates that a SHMM with only one multiplicative term and its 'point of concurrence' outside (left or right) of the cluster of data points adequately fits the data in all subsets. The method is first illustrated with a simple example using a small data set from the statistical literature. Then results obtained in an international maize ( Zea mays L.) yield trial with 20 sites and nine cultivars is presented and discussed. [ABSTRACT FROM AUTHOR]

In a factorial design with two or more factors, there is nonzero interaction when the differences among the levels of one factor vary with levels of other factors. The interaction is disordinal or qualitative with respect to a specific factor, say A, if the difference between at least two levels of A is positive for some and negative for some levels of the other factors. Using standard methods of analysis, there is a potentially large probability of drawing incorrect conclusions about the signs of differences in the presence of disordinal interaction. The maximum probability of such incorrect conclusions, or directional errors, is derived for two-factor designs in which the factor of interest has two levels and the number of levels of the other factor varies. [ABSTRACT FROM AUTHOR]

The phase III, randomized, active-controlled, multicenter, open-label KEYNOTE-183 study (NCT02576977) evaluating pomalidomide and low dose dexamethasone (standard-of-care [SOC]) with or without pembrolizumab in patients with refractory or relapsed and refractory multiple myeloma (rrMM) was placed on full clinical hold by the US FDA on July 03, 2017 due to an imbalance in the number of deaths between arms. Clinically-led subgroup analyses are typically used to shed light on clinical findings. However, this approach is not always successful. We propose a systematic approach using the artificial intelligence tools to identifying risk factors and subgroups contributing to the overall death (prognostic) or to the excess death observed in the pembrolizumab plus SOC arm (predictive) of the KEYNOTE-183 study. In KEYNOTE-183, with a data cutoff date of June 02, 2017, we identified plasmacytoma as a prognostic factor, and ECOG performance status as a predictive factor of death. In addition, a qualitative interaction was observed between ECOG performance status and the treatment arm. The subsequent subgroup analysis based on ECOG performance status confirmed that more deaths were associated with pembrolizumab plus SOC versus SOC alone in patients with ECOG performance status 1. • This systematic approach identifies prognostic and predictive risk factors or subgroups contributing to the imbalance in survival observed in KEYNOTE-183. • Use of machine learning tools such as the random forest successfully identified ECOG performance status as a predictive survival factor [ABSTRACT FROM AUTHOR]

Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making may be ignored. A qualitative interaction of a variable with treatment arises when treatment effect changes direction as the value of this variable varies. The qualitative interaction indicates the importance of this variable for decision-making. Gunter, Zhu and Murphy (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually. In this article, we developed a sequential advantage selection method based on the modified S-score. Our method selects qualitatively interacted variables sequentially, and hence excludes marginally important but jointly unimportant variables or vice versa. The optimal treatment regime based on variables selected via joint model is more comprehensive and reliable. With the proposed stopping criteria, our method can handle a large amount of covariates even if sample size is small. Simulation results show our method performs well in practical settings. We further applied our method to data from a clinical trial for depression.