Iterative Causal Forest: A Novel Algorithm for Subgroup Identification

Precisely and efficiently identifying subgroups with heterogeneous treatment effects (HTEs) in real-world evidence studies remains a challenge. Based on the causal forest (CF) method, we developed an iterative CF (iCF) algorithm to identify HTEs in subgroups defined by important variables. Our method iteratively grows different depths of the CF with important effect modifiers, performs plurality votes to obtain decision trees (subgroup decisions) for a family of CFs with different depths, and then finds the cross-validated subgroup decision that best predicts the treatment effect as a final subgroup decision. We simulated 12 different scenarios and showed that the iCF outperformed other machine learning methods for interaction/subgroup identification in the majority of scenarios assessed. Using a 20% random sample of fee-for-service Medicare beneficiaries initiating sodium-glucose cotransporter-2 inhibitors or glucagon-like peptide-1 receptor agonists, we implemented the iCF to identify subgroups with HTEs for hospitalized heart failure. Consistent with previous studies suggesting patients with heart failure benefit more from sodium-glucose cotransporter-2 inhibitors, iCF successfully identified such a subpopulation with HTEs and additive interactions. The iCF is a promising method for identifying subgroups with HTEs in real-world data where the potential for unmeasured confounding can be limited by study design.

Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation

We present the motivation, experience, and learnings from a data challenge conducted at a large pharmaceutical corporation on the topic of subgroup identification. The data challenge aimed at exploring approaches to subgroup identification for future clinical trials. To mimic a realistic setting, participants had access to 4 Phase III clinical trials to derive a subgroup and predict its treatment effect on a future study not accessible to challenge participants. A total of 30 teams registered for the challenge with around 100 participants, primarily from Biostatistics organization. We outline the motivation for running the challenge, the challenge rules, and logistics. Finally, we present the results of the challenge, the participant feedback as well as the learnings. We also present our view on the implications of the results on exploratory analyses related to treatment effect heterogeneity.

A platform for comparing subgroup identification methodologies

Since the advent of the phrase "subgroup identification," there has been an explosion of methodologies that seek to identify meaningful subgroups of patients with exceptional response in order to further the realization of personalized medicine. However, to perform fair comparison and understand what methods work best under different clinical trials situations, a common platform is needed for comparative effectiveness of these various approaches. In this paper, we describe a comprehensive project that created an extensive platform for evaluating subgroup identification methods as well as a publicly posted challenge that was used to elicit new approaches. We proposed a common data-generating model for creating virtual clinical trial datasets that contain subgroups of exceptional responders encompassing the many dimensions of the problem or null scenarios in which there are no such subgroups. Furthermore, we created a common scoring system for evaluating performance of purported methods for identifying subgroups. This makes it possible to benchmark methodologies in order to understand what methods work best under different clinical trial situations. The findings from this project produced considerable insights and allow us to make recommendations for how the statistical community can better compare and contrast old and new subgroup identification methodologies.

Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials

The identification and estimation of heterogeneous treatment effects in biomedical clinical trials are challenging, because trials are typically planned to assess the treatment effect in the overall trial population. Nevertheless, the identification of how the treatment effect may vary across subgroups is of major importance for drug development. In this work, we review some existing simulation work and perform a simulation study to evaluate recent methods for identifying and estimating the heterogeneous treatments effects using various metrics and scenarios relevant for drug development. Our focus is not only on a comparison of the methods in general, but on how well these methods perform in simulation scenarios that reflect real clinical trials. We provide the R package benchtm that can be used to simulate synthetic biomarker distributions based on real clinical trial data and to create interpretable scenarios to benchmark methods for identification and estimation of treatment effect heterogeneity.

Covariate-adjusted value-guided subgroup identification via boosting

It is widely recognized that treatment effects could differ across subgroups of patients. Subgroup analysis, which assesses such heterogeneity, provides valuable information in developing personalized therapies. There has been extensive research developing novel statistical methods for subgroup identification. The recent contribution is a value-guided subgroup identification method that directly maximizes treatment benefit at the subgroup level for survival outcome, rather than relying on individual treatment effect estimation. In this paper, we first completed this framework by illustrating its application to continuous and binary outcomes. More importantly, we extended the original framework to account for the prognostic effects and named this new method Covariate-Adjusted Value-guided subgroup identification via boosting (CAVboost). The original method directly used the outcome to formulate the value function for subgroup identification. Since the outcome can further be decomposed as prognostic effects and treatment effects, specifying the prognostic effects as the covariates of a model for the outcome can single out the treatment effects and improve the power to detect them across subgroups. Our proposed CAVboost was based on this key idea. It used a covariate-adjusted treatment effect estimator, instead of the outcome itself, to formulate the value function for subgroup identification. CAVboost estimates the treatment effect by using covariates to account for the prognostic effects, which mimics the idea of using covariates in an ANCOVA estimator. We showed that CAVboost could effectively improve the subgroup identification capability for both continuous and binary outcomes.

Considerations for identifying the "right" subgroup in adaptive enrichment trials

Adaptive Enrichment Trials aim to make efficient use of data in a pivotal trial of a new targeted therapy to both (a) more precisely identify who benefits from that therapy and (b) improve the likelihood of successfully concluding that the drug is effective, while controlling the probability of false positives. There are a number of frameworks for conducting such a trial and decisions that must be made regarding how to identify that target subgroup. Among those decisions, one must choose how aggressively to restrict enrollment criteria based on the accumulating evidence in the trial. In this article, we empirically evaluate the impact of aggressive versus conservative enrollment restrictions on the power of the trial to detect an effect of treatment. We identify that, in some cases, a more aggressive strategy can substantially improve power. This additionally raises an important question regarding label indication: To what degree do we need a formal test of the hypothesis of no treatment effect in the exact population implied by the label indication? We discuss this question and evaluate how our answer for adaptive enrichment trials may relate to the answer implied by current practice for broad eligibility trials.

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

Identification of Homogeneous Subgroups from Resting-State fMRI Data

The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.

Estimation and visualization of heterogeneous treatment effects for multiple outcomes

We consider two-arm comparison in clinical trials. The objective is to identify a population with characteristics that make the treatment effective. Such a population is called a subgroup. This identification can be made by estimating the treatment effect and identifying the interactions between treatments and covariates. For a single outcome, there are several ways available to identify the subgroups. There are also multiple outcomes, but they are difficult to interpret and cannot be applied to outcomes other than continuous values. In this paper, we thus propose a new method that allows for a straightforward interpretation of subgroups and deals with both continuous and binary outcomes. The proposed method introduces latent variables and adds Lasso sparsity constraints to the estimated loadings to facilitate the interpretation of the relationship between outcomes and covariates. The interpretation of the subgroups is made by visualizing treatment effects and latent variables. Since we are performing sparse estimation, we can interpret the covariates related to the treatment effects and subgroups. Finally, simulation and real data examples demonstrate the effectiveness of the proposed method.

Change plane model averaging for subgroup identification

Central to personalized medicine and tailored therapies is discovering the subpopulations that account for treatment effect heterogeneity and are likely to benefit more from given interventions. In this article, we introduce a change plane model averaging method to identify subgroups characterized by linear combinations of predictive variables and multiple cut-offs. We first fit a sequence of statistical models, each incorporating the thresholding effect of one particular covariate. The estimation of submodels is accomplished through an iterative integration of a change point detection method and numerical optimization algorithms. A frequentist model averaging approach is then employed to linearly combine the submodels with optimal weights. Our approach can deal with high-dimensional settings involving enormous potential grouping variables by adopting the sparsity-inducing penalties. Simulation studies are conducted to investigate the prediction and subgrouping performance of the proposed method, with a comparison to various competing subgroup detection methods. Our method is applied to a dataset from a warfarin pharmacogenetics study, producing some new findings.

Multi-threshold proportional hazards model and subgroup identification

We propose a novel two-stage procedure for change point detection and parameter estimation in a multi-threshold proportional hazards model. In the first stage, we estimate the number of thresholds by formulating the threshold detection problem as a variable selection problem and applying the penalized partial likelihood approach. In the second stage, the change point locations are refined by a grid search and the standard inference for segment regression can then follow. The proposed model and estimation procedure could lend support to subgroup identification and personalized treatment recommendation in medical research. We establish the consistency of the threshold estimators and regression coefficient estimators under technical conditions. The finite sample performance of the method is demonstrated via simulation studies and two cancer data examples.

A two-stage adaptive clinical trial design with data-driven subgroup identification at interim analysis

In this paper, we consider randomized controlled clinical trials comparing two treatments in efficacy assessment using a time to event outcome. We assume a relatively small number of candidate biomarkers available in the beginning of the trial, which may help define an efficacy subgroup which shows differential treatment effect. The efficacy subgroup is to be defined by one or two biomarkers and cut-offs that are unknown to the investigator and must be learned from the data. We propose a two-stage adaptive design with a pre-planned interim analysis and a final analysis. At the interim, several subgroup-finding algorithms are evaluated to search for a subgroup with enhanced survival for treated versus placebo. Conditional powers computed based on the subgroup and the overall population are used to make decision at the interim to terminate the study for futility, continue the study as planned, or conduct sample size recalculation for the subgroup or the overall population. At the final analysis, combination tests together with closed testing procedures are used to determine efficacy in the subgroup or the overall population. We conducted simulation studies to compare our proposed procedures with several subgroup-identification methods in terms of a novel utility function and several other measures. This research demonstrated the benefit of incorporating data-driven subgroup selection into adaptive clinical trial designs.

A permutation procedure to detect heterogeneous treatments effects in randomized clinical trials while controlling the type I error rate

BACKGROUND/AIMS

Secondary analyses of randomized clinical trials often seek to identify subgroups with differential treatment effects. These discoveries can help guide individual treatment decisions based on patient characteristics and identify populations for which additional treatments are needed. Traditional analyses require researchers to pre-specify potential subgroups to reduce the risk of reporting spurious results. There is a need for methods that can detect such subgroups without a priori specification while allowing researchers to control the probability of falsely detecting heterogeneous subgroups when treatment effects are uniform across the study population.

METHODS

We propose a permutation procedure for tuning parameter selection that allows for type I error control when testing for heterogeneous treatment effects framed within the Virtual Twins procedure for subgroup identification. We verify that the type I error rate can be controlled at the nominal rate and investigate the power for detecting heterogeneous effects when present through extensive simulation studies. We apply our method to a secondary analysis of data from a randomized trial of very low nicotine content cigarettes.

RESULTS

In the absence of type I error control, the observed type I error rate for Virtual Twins was between 99% and 100%. In contrast, models tuned via the proposed permutation were able to control the type I error rate and detect heterogeneous effects when present. An application of our approach to a recently completed trial of very low nicotine content cigarettes identified several variables with potentially heterogeneous treatment effects.

CONCLUSIONS

The proposed permutation procedure allows researchers to engage in secondary analyses of clinical trials for treatment effect heterogeneity while maintaining the type I error rate without pre-specifying subgroups.

Robust structured heterogeneity analysis approach for high-dimensional data

Revealing relationships between genes and disease phenotypes is a critical problem in biomedical studies. This problem has been challenged by the heterogeneity of diseases. Patients of a perceived same disease may form multiple subgroups, and different subgroups have distinct sets of important genes. It is hence imperative to discover the latent subgroups and reveal the subgroup-specific important genes. Some heterogeneity analysis methods have been proposed in the recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate data contamination and ignore the interconnections among genes. Aiming at these shortages, we develop a robust structured heterogeneity analysis approach to identify subgroups, select important genes as well as estimate their effects on the phenotype of interest. Possible data contamination is accommodated by employing the Huber loss function. A sparse overlapping group lasso penalty is imposed to conduct regularization estimation and gene identification, while taking into account the possibly overlapping cluster structure of genes. This approach takes an iterative strategy in the similar spirit of K-means clustering. Simulations demonstrate that the proposed approach outperforms alternatives in revealing the heterogeneity and selecting important genes for each subgroup. The analysis of Cancer Cell Line Encyclopedia data leads to biologically meaningful findings with improved prediction and grouping stability.

Robust method for optimal treatment decision making based on survival data

Identifying the optimal treatment decision rule, where the best treatment for an individual varies according to his/her characteristics, is of great importance when treatment effect heterogeneity exists. We develop methods for estimating the optimal treatment decision rule based on data with survival time as the primary endpoint. Our methods are based on a flexible semiparametric accelerated failure time model, where only the treatment contrast (ie, the difference in means between treatments) is parameterized and all other aspects are unspecified. An individual's treatment contrast is firstly estimated robustly by an augmented inverse probability weighted estimator (AIPWE). Then the optimal decision rule is estimated by minimizing the loss between the treatment contrast and the AIPWE contrast. Two loss functions with different strategies to account for censoring are proposed. The proposed loss functions distinguish from existing ones in that they are based on treatment contrasts, which completely determine the optimal treatment rule. Our methods can further incorporate a penalty term to select variables that are only important for treatment decision making, while taking advantage of all covariates predictive of outcomes to improve performance. Comprehensive simulation studies have been conducted to evaluate performances of the proposed methods relative to existing methods. The proposed methods are illustrated with an application to the ACTG 175 clinical trial on HIV-infected patients.

Identification and inference for subgroups with differential treatment efficacy from randomized controlled trials with survival outcomes through multiple testing

With the uptake of targeted therapies, instead of the "one-fits-all" approach, modern randomized controlled trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye disease, age-related macular degeneration (AMD), we develop a simultaneous inference procedure to identify and infer subgroups with differential treatment efficacy in RCTs with time-to-event outcomes. Specifically, we formulate the multiple testing problem through contrasts and construct their simultaneous confidence intervals, which appropriately control both within- and across-marker multiplicity. Realistic simulations are conducted using real genotype data to evaluate the method performance under various scenarios. The method is then applied to AREDS to assess the efficacy of antioxidants and zinc combination in delaying AMD progression. Multiple gene regions including ESRRB-VASH1 on chromosome 14 have been identified with subgroups showing differential efficacy. We further validate our findings in an independent subsequent RCT, AREDS2, by discovering consistent differential treatment responses in the targeted and non-targeted subgroups identified from AREDS. This multiple-testing-based simultaneous inference approach provides a step forward to confidently identify and infer subgroups in modern drug development.

Alike and different: Associations between orthorexic eating behaviors and exercise addiction

OBJECTIVE

Symptoms of exercise addiction, a state of compulsively engaging in intense exercise, and orthorexic eating attitudes, the obsession with eating only healthy foods, often occur together. It is assumed that some more general psychological traits underlie this association. Main aim of this report was to examine similarities and differences between orthorexic eating and addictive exercising.

METHOD

Six hundred and eight individuals completed an online survey (mean age: 27.5, SD = 11.0 years; 76.5% women) measuring exercise addiction (Exercise Addiction inventory, EAI), orthorexic eating (Düsseldorfer Orthorexie Skala, DOS), personality domains (Big-Five Inventory-10), anxiety and depression (Hospital Anxiety and Depression Scale).

RESULTS

Correlations between the DOS and EAI were .43 in women and .62 in men. Structural equation models identified gender-specific as well as behavior-specific psychological correlates. Among women, anxiety correlated with both EAI and DOS. In addition, the DOS correlated with depression and neuroticism while the EAI correlated with conscientiousness. In men, both scales were associated with conscientiousness and the EAI also correlated with extraversion. Clusterability analysis provided no evidence for clusters based on DOS and EAI.

DISCUSSION

Present results showed a substantial correlation between addictive exercising and orthorexic eating, however, coefficients were smaller than expected and appeared higher in men. Both behaviors shared few psychological traits (anxiety in women, conscientiousness in men) thereby questioning the assumption of a similar origin. Additionally, gender-specific psychological correlates point to the need for different disease management approaches in women and men.

Learning and confirming a class of treatment responders in clinical trials

Clinical trials require substantial effort and time to complete, and regulatory agencies may require two successful efficacy trials before approving a new drug. One way to improve the chance of follow-up success is to identify a subpopulation among whom treatment effects are estimated to be beneficial, and enrolling future studies from this subpopulation. In this article we study confirmable responder class (CRC) learning, where the objective is to learn in a random half of the dataset (training set) a subpopulation among whom the predicted conditional ATE (CATE) suggests clinically meaningful benefit, with maximum power of being confirmed via hypothesis test in the other half (test set). We studied a set of CRC learners across simulated datasets in which either all patients benefited, or only some did. Performance metrics included the rate of confirmation in the test set, and the classification accuracy of the CRC compared with the group with true CATE>0. In trials where all patients benefitted, only two learners were able to consistently identify most of the population as the CRC. One of these also performed especially well when only some patients benefitted, having relatively high confirmation rates, and showing robustness to the dimension of the covariate vector and population characteristics. This learner is based on cross-validation, and is a possible avenue for further development of model selection criteria for CRC learning. We also show that the performance of all methods can be improved by using both halves of the original dataset as training and test sets in turn.

Multithreshold change plane model: Estimation theory and applications in subgroup identification

We propose a multithreshold change plane regression model which naturally partitions the observed subjects into subgroups with different covariate effects. The underlying grouping variable is a linear function of observed covariates and thus multiple thresholds produce change planes in the covariate space. We contribute a novel two-stage estimation approach to determine the number of subgroups, the location of thresholds, and all other regression parameters. In the first stage we adopt a group selection principle to consistently identify the number of subgroups, while in the second stage change point locations and model parameter estimates are refined by a penalized induced smoothing technique. Our procedure allows sparse solutions for relatively moderate- or high-dimensional covariates. We further establish the asymptotic properties of our proposed estimators under appropriate technical conditions. We evaluate the performance of the proposed methods by simulation studies and provide illustrations using two medical data examples. Our proposal for subgroup identification may lead to an immediate application in personalized medicine.

Hierarchical Bayesian clustering design of multiple biomarker subgroups (HCOMBS)

Given the Food and Drug Administration's (FDA's) acceptance of master protocol designs in recent guidance documents, the oncology field is rapidly moving to address the paradigm shift to molecular subtype focused studies. Identifying new "marker-based" treatments requires new methodologies to address the growing demand to conduct clinical trials in smaller molecular subpopulations, identify effective treatment and marker interactions, and control for false positives. We introduce our methodology, Hierarchical Bayesian Clustering Design of Multiple Biomarker Subgroups (HCOMBS), a two-stage umbrella Phase II design with effect size clustering and information borrowing across multiple biomarker-treatment pairs. HCOMBS was designed to reduce required sample size, differentiate between varying effect sizes, and control for operating characteristics in the multi-arm setting. When compared to independently applied Simon's Optimal two-stage design, we showed through simulations that HCOMBS required less participants per treatment arm with a well-controlled family-wise error rate and desirable marginal power. Additionally, HCOMBS features a statistical approach that simultaneously conducts clustering and hypothesis testing in one step. We also applied the proposed design on the alliance brain metastases umbrella trial.

[Subgroup identification based on accelerated failure time model combined with adaptive elastic net]

OBJECTIVE

To solve the problem of identifying subgroup in a randomized clinical trial with respect to survival time, we present a strategy based on accelerated failure time model to identify the subgroup with an enhanced treatment effect.

OBJECTIVE

We fitted and compared univariate accelerated failure time (AFT) models and penalized AFT models regularized by adaptive elastic net to identify the candidate covariates. Based on these covariates, we utilized change-point algorithm to classify the patient subgroups. A two-stage adaptive design was adopted to verify the treatment effect in certain subgroups. Simulations were conducted across different scenarios to evaluate the performance of the models.

OBJECTIVE

As the correlation between covariates differed, the power of the models with an adaptive design was stable. In the two-stage adaptive design, the power of the models was the highest when the two significance levels (α1 and α2) were allocated to be 0.035 and 0.015, respectively. A better effect of the responder subgroup was associated with a higher power of the models. For a fixed sample size, the power decreased as the covariate number to sample size ratio increased, but the power showed a stable trend when the ratio was above 1. The univariate models showed different distribution patterns of the parameters for different survival time, while their distribution was relatively stable in the penalized AFT models.

OBJECTIVE

The correlation between the covariates does not affect the performance of univariate AFT models and penalized AFT models. (0.035, 0.015) can be used as a reference for the significance level of an adaptive design. For small differences in the treatment effect between the responder and the non-responder, the penalized AFT model including the main effect of covariate (Penalized, Eq_in) outperforms the univariate AFT model excluding the main effect of covariate (Univariate, Eq_ex). Univariate, Eq_ex performs better when the covariate number to sample size ratio is less than 1, but is outperformed by Penalized, Eq_in when the ratio is above 1. The parameter distribution of survival time has a greater impact on the univariate models but a smaller impact on the penalized models.

Confident identification of subgroups from SNP testing in RCTs with binary outcomes

In modern drug development, genotype information becomes more frequently collected in randomized controlled trials (RCTs) for individualized risk prediction and personalized medicine development. Finding single nucleotide polymorphisms (SNPs) that are predictive of differential treatment efficacy, measured by a clinical outcome, is fundamentally different and more challenging than the traditional association test for a quantitative trait. With the objective to confidently identify and infer genetic subgroups with enhanced treatment efficacy from a large RCT for an eye disease, age-related macular degeneration (AMD), where the clinical endpoint is binary (progressed or not), we propose a novel SNP-testing procedure for binary clinical outcomes. Specifically, we formulate four contrasts to simultaneously assess all possible genetic effects on a logic-respecting efficacy measure, the relative risk (between treatment and control). Our method controls both within- and across-SNP multiplicity rigorously. We then use real genotype data to perform chromosome-wide simulations to evaluate our method performance and to provide practical recommendations. Finally, we apply the proposed method to perform a genome-wide SNP testing for the AMD trial and successfully identify multiple gene regions with genetic subgroups exhibiting enhanced efficacy in terms of decreasing the AMD progression rate.

Assessing and communicating heterogeneity of treatment effects for patient subpopulations: The hardest problem there is

Heterogeneity is an enormously complex problem because there are so many dimensions and variables that can be considered when assessing which ones may influence an efficacy or safety outcome for an individual patient. This is difficult in randomized controlled trials and even more so in observational settings. An alternative approach is presented in which the individual patient becomes the "subgroup," and similar patients are identified in the clinical trial database or electronic medical record that can be used to predict how that individual patient may respond to treatment.

Subgroup Splits in Diverse Work Teams: Subgroup Perceptions but Not Demographic Faultlines Affect Team Identification and Emotional Exhaustion

We investigate the relationship between (hypothetical) subgroup splits (i.e., faultlines), subjectively perceived subgroups, and team identification and emotional exhaustion. Based on the job demands-resources model and on self-categorization theory, we propose that faultline strength and perceived subgroups negatively affect emotional exhaustion, mediated by team identification. We further propose that subgroup identification moderates the mediation such that subgroup identification compensates low levels of team identification. We tested our hypotheses with a two-wave questionnaire study in a sample of 105 participants from 48 teams from various contexts. We found an effect of perceived subgroups on emotional exhaustion mediated by team identification, but no direct or indirect effect of faultline strength on emotional exhaustion. We also could not find that subgroup identification moderates the effect of team identification on emotional exhaustion. We discuss the need for further research on the link of subgroup splits in work teams and the rise of psychological health issues and derive that measures to prevent burnout should primarily focus on avoiding or reducing subgroup perception whereas affecting the actual demographic composition of work team should be of lower priority.

A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data

In randomized clinical trials with survival outcome, there has been an increasing interest in subgroup identification based on baseline genomic, proteomic markers, or clinical characteristics. Some of the existing methods identify subgroups that benefit substantially from the experimental treatment by directly modeling outcomes or treatment effect. When the goal is to find an optimal treatment for a given patient rather than finding the right patient for a given treatment, methods under the individualized treatment regime framework estimate an individualized treatment rule that would lead to the best expected clinical outcome as measured by a value function. Connecting the concept of value function to subgroup identification, we propose a nonparametric method that searches for subgroup membership scores by maximizing a value function that directly reflects the subgroup-treatment interaction effect based on restricted mean survival time. A gradient tree boosting algorithm is proposed to search for the individual subgroup membership scores. We conduct simulation studies to evaluate the performance of the proposed method and an application to an AIDS clinical trial is performed for illustration.

Monte Carlo approaches to frequentist multiplicity-adjusted benefiting subgroup identification

One common goal of subgroup analyses is to determine the subgroup of the population for which a given treatment is effective. Like most problems in subgroup analyses, this benefiting subgroup identification requires careful attention to multiple testing considerations, especially Type I error inflation. To partially address these concerns, the credible subgroups approach provides a pair of bounding subgroups for the benefiting subgroup, constructed so that with high posterior probability one is contained by the benefiting subgroup while the other contains the benefiting subgroup. To date, this approach has been presented within the Bayesian paradigm only, and requires sampling from the posterior of a Bayesian model. Additionally, in many cases, such as regulatory submission, guarantees of frequentist operating characteristics are helpful or necessary. We present Monte Carlo approaches to constructing confidence subgroups, frequentist analogues to credible subgroups that replace the posterior distribution with an estimate of the joint distribution of personalized treatment effect estimates, and yield frequentist interpretations and coverage guarantees. The estimated joint distribution is produced using either draws from asymptotic sampling distributions of estimated model parameters, or bootstrap resampling schemes. The approach is applied to a publicly available dataset from randomized trials of Alzheimer's disease treatments.

Change point detection in Cox proportional hazards mixture cure model

The mixture cure model has been widely applied to survival data in which a fraction of the observations never experience the event of interest, despite long-term follow-up. In this paper, we study the Cox proportional hazards mixture cure model where the covariate effects on the distribution of uncured subjects' failure time may jump when a covariate exceeds a change point. The nonparametric maximum likelihood estimation is used to obtain the semiparametric estimates. We employ a two-step computational procedure involving the Expectation-Maximization algorithm to implement the estimation. The consistency, convergence rate and asymptotic distributions of the estimators are carefully established under technical conditions and we show that the change point estimator is n consistency. The m out of n bootstrap and the Louis algorithm are used to obtain the standard errors of the estimated change point and other regression parameter estimates, respectively. We also contribute a test procedure to check the existence of the change point. The finite sample performance of the proposed method is demonstrated via simulation studies and real data examples.

Subgroup analysis based on structured mixed-effects models for longitudinal data

In recent years, subgroup analysis has emerged as an important tool to identify unknown subgroup memberships. However, subgroup analysis is still under-studied for longitudinal data. In this paper, we propose a structured mixed-effects approach for longitudinal data to model subgroup distribution and identify subgroup membership simultaneously. In the proposed structured mixed-effects model, the heterogeneous treatment effect is modeled as a random effect from a two-component mixture model, while the membership of the mixture model is incorporated using a logistic model with respect to some covariates. One advantage of our approach is that we are able to derive the estimation of the treatment effects through an EM-type algorithm which keeps the subgroup membership unchanged over time. Our numerical studies and real data example demonstrate that the proposed model outperforms other competing methods.

A model-based multithreshold method for subgroup identification

Thresholding variable plays a crucial role in subgroup identification for personalized medicine. Most existing partitioning methods split the sample based on one predictor variable. In this paper, we consider setting the splitting rule from a combination of multivariate predictors, such as the latent factors, principle components, and weighted sum of predictors. Such a subgrouping method may lead to more meaningful partitioning of the population than using a single variable. In addition, our method is based on a change point regression model and thus yields straight forward model-based prediction results. After choosing a particular thresholding variable form, we apply a two-stage multiple change point detection method to determine the subgroups and estimate the regression parameters. We show that our approach can produce two or more subgroups from the multiple change points and identify the true grouping with high probability. In addition, our estimation results enjoy oracle properties. We design a simulation study to compare performances of our proposed and existing methods and apply them to analyze data sets from a Scleroderma trial and a breast cancer study.

[Subgroup identification based on the Logistic model]

We propose a subgroup identification method based on the Logistic model for data from a two-arm clinical trial with dichotomous outcome variables.In this method, binary Logistic regression models are established for each group to calculate the outcome probabilities of each patient for comparison.According to the established rules, the patients are classified into their corresponding subgroups to establish a multinomial Logistic regression model.We simulated the false rate, correct judgment rate, coincidence rate and model correct judgment rate for different sample sizes and carried out an example analysis.The results of simulation showed that for different sample sizes, the false rates of this method were below 0.07 and the correct judgment rates were all above 0.75 with adequate coincidence rates and model correct judgment rates, demonstrating the effectiveness and reliability of the proposed method for subgroup identification.

Multiplicity-adjusted semiparametric benefiting subgroup identification in clinical trials

Background A recent focus in the health sciences has been the development of personalized medicine, which includes determining the population for which a given treatment is effective. Due to limited data, identifying the true benefiting population is a challenging task. To tackle this difficulty, the credible subgroups approach provides a pair of bounding subgroups for the true benefiting subgroup, constructed so that one is contained by the benefiting subgroup while the other contains the benefiting subgroup with high probability. However, the method has so far only been developed for parametric linear models. Methods In this article, we develop the details required to follow the credible subgroups approach in more realistic settings by considering nonlinear and semiparametric regression models, supported for regulatory science by conditional power simulations. We also present an improved multiple testing approach using a step-down procedure. We evaluate our approach via simulations and apply it to data from four trials of Alzheimer's disease treatments carried out by AbbVie. Results Semiparametric modeling yields credible subgroups that are more robust to violations of linear treatment effect assumptions, and careful choice of the population of interest as well as the step-down multiple testing procedure result in a higher rate of detection of benefiting types of patients. The approach allows us to identify types of patients that benefit from treatment in the Alzheimer's disease trials. Conclusion Attempts to identify benefiting subgroups of patients in clinical trials are often met with skepticism due to a lack of multiplicity control and unrealistically restrictive assumptions. Our proposed approach merges two techniques, credible subgroups, and semiparametric regression, which avoids these problems and makes benefiting subgroup identification practical and reliable.

Cluster Analysis to Identify Possible Subgroups in Tinnitus Patients

Introduction

In tinnitus treatment, there is a tendency to shift from a “one size fits all” to a more individual, patient-tailored approach. Insight in the heterogeneity of the tinnitus spectrum might improve the management of tinnitus patients in terms of choice of treatment and identification of patients with severe mental distress. The goal of this study was to identify subgroups in a large group of tinnitus patients.

Methods

Data were collected from patients with severe tinnitus complaints visiting our tertiary referral tinnitus care group at the University Medical Center Groningen. Patient-reported and physician-reported variables were collected during their visit to our clinic. Cluster analyses were used to characterize subgroups. For the selection of the right variables to enter in the cluster analysis, two approaches were used: (1) variable reduction with principle component analysis and (2) variable selection based on expert opinion.

Results

Various variables of 1,783 tinnitus patients were included in the analyses. Cluster analysis (1) included 976 patients and resulted in a four-cluster solution. The effect of external influences was the most discriminative between the groups, or clusters, of patients. The “silhouette measure” of the cluster outcome was low (0.2), indicating a “no substantial” cluster structure. Cluster analysis (2) included 761 patients and resulted in a three-cluster solution, comparable to the first analysis. Again, a “no substantial” cluster structure was found (0.2).

Conclusion

Two cluster analyses on a large database of tinnitus patients revealed that clusters of patients are mostly formed by a different response of external influences on their disease. However, both cluster outcomes based on this dataset showed a poor stability, suggesting that our tinnitus population comprises a continuum rather than a number of clearly defined subgroups.

Patient subgroup identification for clinical drug development

Causal mechanism of relationship between the clinical outcome (efficacy or safety endpoints) and putative biomarkers, clinical baseline, and related predictors is usually unknown and must be deduced empirically from experimental data. Such relationships enable the development of tailored therapeutics and implementation of a precision medicine strategy in clinical trials to help stratify patients in terms of disease progression, clinical response, treatment differentiation, and so on. These relationships often require complex modeling to develop the prognostic and predictive signatures. For the purpose of easier interpretation and implementation in clinical practice, defining a multivariate biomarker signature in terms of thresholds (cutoffs/cut points) on individual biomarkers is preferable. In this paper, we propose some methods for developing such signatures in the context of continuous, binary and time-to-event endpoints. Results from simulations and case study illustration are also provided. Copyright © 2017 John Wiley & Sons, Ltd.

General guidance on exploratory and confirmatory subgroup analysis in late-stage clinical trials

This article focuses on a broad class of statistical and clinical considerations related to the assessment of treatment effects across patient subgroups in late-stage clinical trials. This article begins with a comprehensive review of clinical trial literature and regulatory guidelines to help define scientifically sound approaches to evaluating subgroup effects in clinical trials. All commonly used types of subgroup analysis are considered in the article, including different variations of prospectively defined and post-hoc subgroup investigations. In the context of confirmatory subgroup analysis, key design and analysis options are presented, which includes conventional and innovative trial designs that support multi-population tailoring approaches. A detailed summary of exploratory subgroup analysis (with the purpose of either consistency assessment or subgroup identification) is also provided. The article promotes a more disciplined approach to post-hoc subgroup identification and formulates key principles that support reliable evaluation of subgroup effects in this setting.

Making what's advanced today routine tomorrow

Statistical principles and ongoing proliferation of novel statistical methodologies have dramatically improved the clinical drug development process. This journey over the last seven decades reshaped the pharmaceutical industry and regulatory agencies, highlighted the importance of statistical thinking in drug development and decision-making, and, most importantly, improved the lives of countless patients around the world. Some significant highlights in the history of this journey are recounted here as well as some exciting opportunities of what the future may hold for the science and profession of statistics.

Estimating optimal treatment regimes via subgroup identification in randomized control trials and observational studies

With new treatments and novel technology available, personalized medicine has become an important piece in the new era of medical product development. Traditional statistics methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Motivated by the recent development of outcome weighted learning framework, we propose an alternative algorithm to search treatment assignments which has a connection with subgroup identification problems. Our method focuses on applications from clinical trials to generate easy to interpret results. This framework is able to handle two or more than two treatments from both randomized control trials and observational studies. We implement our algorithm in C++ and connect it with R. Its performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study. Copyright © 2016 John Wiley & Sons, Ltd.

Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review

Important objectives in the development of stratified medicines include the identification and confirmation of subgroups of patients with a beneficial treatment effect and a positive benefit-risk balance. We report the results of a literature review on methodological approaches to the design and analysis of clinical trials investigating a potential heterogeneity of treatment effects across subgroups. The identified approaches are classified based on certain characteristics of the proposed trial designs and analysis methods. We distinguish between exploratory and confirmatory subgroup analysis, frequentist, Bayesian and decision-theoretic approaches and, last, fixed-sample, group-sequential, and adaptive designs and illustrate the available trial designs and analysis strategies with published case studies.

Identification of drug-induced toxicity biomarkers for treatment determination

Drug-induced organ toxicity (DIOT) that leads to the removal of marketed drugs or termination of candidate drugs has been a leading concern for regulatory agencies and pharmaceutical companies. In safety studies, the genomic assays are conducted after the treatment so that drug-induced adverse effects can occur. Two types of biomarkers are observed: biomarkers of susceptibility and biomarkers of response. This paper presents a statistical model to distinguish two types of biomarkers and procedures to identify susceptible subpopulations. The biomarkers identified are used to develop classification model to identify susceptible subpopulation. Two methods to identify susceptibility biomarkers were evaluated in terms of predictive performance in subpopulation identification, including sensitivity, specificity, and accuracy. Method 1 considered the traditional linear model with a variable-by-treatment interaction term, and Method 2 considered fitting a single predictor variable model using only treatment data. Monte Carlo simulation studies were conducted to evaluate the performance of the two methods and impact of the subpopulation prevalence, probability of DIOT, and sample size on the predictive performance. Method 2 appeared to outperform Method 1, which was due to the lack of power for testing the interaction effect. Important statistical issues and challenges regarding identification of preclinical DIOT biomarkers were discussed. In summary, identification of predictive biomarkers for treatment determination highly depends on the subpopulation prevalence. When the proportion of susceptible subpopulation is 1% or less, a very large sample size is needed to ensure observing sufficient number of DIOT responses for biomarker and/or subpopulation identifications.

Understanding heterogeneity in response to antidiabetes treatment: a post hoc analysis using SIDES, a subgroup identification algorithm

BACKGROUND

Treatment response in patients with type 2 diabetes mellitus (T2DM) varies because of different genotypic and phenotypic characteristics. Results of clinical trials are based largely on aggregated estimates of treatment effect rather than individualized outcomes. This research assessed heterogeneity and differential treatment response using the subgroup identification based on differential effect search (SIDES) algorithm with data from a large multinational study.

METHODS

This was a retrospective analysis of the DURABLE trial, a randomized, open-label, two-arm, parallel study. The trial enrolled 2091 insulin-naïve T2DM patients aged 30 to 80 years. Patients received twice-daily insulin lispro mix 75/25 (LM75/25) or once-daily insulin glargine (insulin glargine). The SIDES methodology was used to find subgroups where the treatment effect was substantially different from what was observed in the full population of the clinical trial. A subgroup identification tool implementing the SIDES algorithm was used to examine data for 1092 patients (584 LM75/25 and 508 insulin glargine), achieving at-goal 12- or 24-week glycated hemoglobin A1c (A1C) (≤7.0%).

RESULTS

The overall at-goal population treatment difference (A1C reduction) was not clinically meaningful, but a clinically meaningful difference was observed (A1C reduction 2.31% ± 0.06% LM75/25 versus 2.01% ± 0.07% insulin glargine; p = .001) in patients with a baseline fasting insulin level >11.4 μIU/ml and age ≤56 years.

CONCLUSION

The observation that younger patients with higher levels of fasting insulin may benefit from a regimen that includes short-acting insulin targeting postprandial glycemia helps explain the heterogeneity in response and warrants further investigation.