Subgroup Identification Research Articles

Subgroups from regression trees with adjustment for prognostic effects and postselection inference.

Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounded with effects of prognostic variables and how to determine the statistical significance of treatment effects in the subgroups. We address the first question by selectively including linear prognostic effects in the subgroups in a regression tree model. The second question is more difficult because it falls within the subject of postselection inference. We use a bootstrap technique to calibrate normal-theory t intervals so that their expected coverage probability, averaged over all the subgroups in a fitted model, approximates the desired confidence level. It can also provide simultaneous confidence intervals for all subgroups. The first solution is implemented in the GUIDE algorithm and is applicable to data with missing covariate values, 2 or more treatment arms, and outcomes subject to right censoring. Bootstrap calibration is applicable to any subgroup identification method; it is not restricted to regression tree models. Two real examples are used for illustration: a diabetes trial where the outcomes are completely observed but some covariate values are missing and a breast cancer trial where the outcome is right censored.

Sinister connections: How to analyse organised crime with social network analysis?

Networks have recently become ubiquitous in many scientific fields. In criminology, social network analysis (SNA) provides a potent tool for analysis of organized crime. This paper introduces basic network terms and measures as well as advanced models and reviews their application in criminological research. The centrality measures – degree and betweenness – are introduced as means to describe relative importance of actors in the network. The centrality measures are useful also in determining strategically positioned actors within the network or providing efficient targets for disruption of criminal networks. The cohesion measures, namely density, centralization, and average geodesic distance are described and their relevance is related to the idea of efficiency-security trade-off. As the last of the basic measures, the attention is paid to subgroup identification algorithms such as cliques, k-plexes, and factions. Subgroups are essential in the discussion on the cell-structure in criminal networks. The following part of the paper is a brief overview of more sophisticated network models. Models allow for theory testing, distinguishing systematic processes from randomness, and simplification of complex network structures. Quadratic assignment procedure, blockmodels, exponential random graph models, and stochastic actor-oriented models are covered. Some important research examples include similarities in co-offending, core-periphery structures, closure and brokerage, and network evolution. Subsequently, the paper reflects the three biggest challenges for application of SNA to criminal settings – data availability, proper formulation of theories and adequate methods application. In conclusion, readers are referred to books and journals combining SNA and criminology as well as to software suitable to carry out SNA.

Subgroup identification by recursive segmentation

ABSTRACTA new modeling approach called ‘recursive segmentation’ is proposed to support the supervised exploration and identification of subgroups or clusters. It is based on the frameworks of recursive partitioning and the Patient Rule Induction Method (PRIM). Through combining these methods, recursive segmentation aims to exploit their respective strengths while reducing their weaknesses. Consequently, recursive segmentation can be applied in a very general way, that is in any (multivariate) regression, classification or survival (time-to-event) problem, using conditional inference, evolutionary learning or the CART algorithm, with predictor variables of any scale and with missing values. Furthermore, results of a synthetic example and a benchmark application study that comprises 26 data sets suggest that recursive segmentation achieves a competitive prediction accuracy and provides more accurate definitions of subgroups by models of less complexity as compared to recursive partitioning and PRIM. An application to the German Breast Cancer Study Group data demonstrates the improved interpretability and reliability of results produced by the new approach. The method is made publicly available through the R-package rseg (http://rseg.r-forge.r-project.org/).

SubID, a non-median dichotomization tool for heterogeneous populations, reveals the pan-cancer significance of INPP4B and its regulation by EVI1 in AML.

Our previous studies demonstrated that INPP4B, a member of the PI3K/Akt signaling pathway, is overexpressed in a subset of AML patients and is associated with lower response to chemotherapy and shorter survival. INPP4B expression analysis in AML revealed a right skewed frequency distribution with 25% of patients expressing significantly higher levels than the majority. The 75% low/25% high cut-off revealed the prognostic power of INPP4B expression status in AML, which would not have been apparent with a standard median cut-off approach. Our identification of a clinically relevant non-median cut-off for INPP4B indicated a need for a generalizable non-median dichotomization approach to optimally study clinically relevant genes. To address this need, we developed Subgroup Identifier (SubID), a tool which examines the relationship between a continuous variable (e.g. gene expression), and a test parameter (e.g. CoxPH or Fisher’s exact P values). In our study, Fisher’s exact SubID was used to reveal EVI1 as a transcriptional regulator of INPP4B in AML; a finding which was validated in vitro. Next, we used CoxPH SubID to conduct a pan-cancer analysis of INPP4B’s prognostic significance. Our analysis revealed that INPP4Blow is associated with shorter survival in kidney clear cell, liver hepatocellular, and bladder urothelial carcinomas. Conversely, INPP4Blow was shown to be associated with increased survival in pancreatic adenocarcinoma in three independent datasets. Overall, our study describes the development and application of a novel subgroup identification tool used to identify prognostically significant rare subgroups based upon gene expression, and for investigating the association between a gene with skewed frequency distribution and potentially important upstream and downstream genes that relate to the index gene.

Subgroup identification in dose-finding trials via model-based recursive partitioning.

An important task in early-phase drug development is to identify patients, which respond better or worse to an experimental treatment. While a variety of different subgroup identification methods have been developed for the situation of randomized clinical trials that study an experimental treatment and control, much less work has been done in the situation when patients are randomized to different dose groups. In this article, we propose new strategies to perform subgroup analyses in dose-finding trials and discuss the challenges, which arise in this new setting. We consider model-based recursive partitioning, which has recently been applied to subgroup identification in 2-arm trials, as a promising method to tackle these challenges and assess its viability using a real trial example and simulations. Our results show that model-based recursive partitioning can be used to identify subgroups of patients with different dose-response curves and improves estimation of treatment effects and minimum effective doses compared to models ignoring possible subgroups, when heterogeneity among patients is present.

A recursive partitioning approach for subgroup identification in individual patient data meta-analysis.

BackgroundMotivated by the setting of clinical trials in low back pain, this work investigated statistical methods to identify patient subgroups for which there is a large treatment effect (treatment by subgroup interaction). Statistical tests for interaction are often underpowered. Individual patient data (IPD) meta‐analyses provide a framework with improved statistical power to investigate subgroups. However, conventional approaches to subgroup analyses applied in both a single trial setting and an IPD setting have a number of issues, one of them being that factors used to define subgroups are investigated one at a time. As individuals have multiple characteristics that may be related to response to treatment, alternative exploratory statistical methods are required.MethodsTree‐based methods are a promising alternative that systematically searches the covariate space to identify subgroups defined by multiple characteristics. A tree method in particular, SIDES, is described and extended for application in an IPD meta‐analyses setting by incorporating fixed‐effects and random‐effects models to account for between‐trial variation. The performance of the proposed extension was assessed using simulation studies. The proposed method was then applied to an IPD low back pain dataset.ResultsThe simulation studies found that the extended IPD‐SIDES method performed well in detecting subgroups especially in the presence of large between‐trial variation. The IPD‐SIDES method identified subgroups with enhanced treatment effect when applied to the low back pain data.ConclusionsThis work proposes an exploratory statistical approach for subgroup analyses applicable in any research discipline where subgroup analyses in an IPD meta‐analysis setting are of interest.

Development of predictive signatures for treatment selection in precision medicine with survival outcomes.

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker-positive and biomarker-negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood-based change-point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change-point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.

Gait Pattern Analysis and Subgroup Identification in Various Neurological Conditions: A Retrospective Observational Study

Gait Pattern Analysis and Subgroup Identification in Various Neurological Conditions: A Retrospective Observational Study

DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers.

Breast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied. To predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor heterogeneity with subgroup identification based on patterns of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Retrospective study. Seventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression. T1 -weighted 3.0T DCE-MR images. Each tumor was partitioned into multiple subregions using three methods based on patterns of dynamic enhancement: 1) time to peak (TTP), 2) peak enhancement rate (PER), and 3) kinetic pattern clustering (KPC). In each tumor subregion, 18 texture features were computed. Univariate and multivariate logistic regression analyses were performed using a leave-one-out-based cross-validation (LOOCV) method. The partitioning results were compared with the same feature extraction methods across the whole tumor. In the univariate analysis, the best-performing feature was the texture statistic of sum variance in the tumor subregion with early TTP for differentiating between patients with high and low Ki-67 expression (area under the receiver operating characteristic curves, AUC = 0.748). Multivariate analysis showed that features from the tumor subregion associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. Among all regions, the tumor area with high PER at a precontrast MR image achieved the highest performance (AUC = 0.722), while the subregion that exhibited the highest overall enhancement rate based on KPC had an AUC of 0.731. These three models based on intratumoral texture analysis significantly (P < 0.01) outperformed the model using features from the whole tumor (AUC = 0.59). Texture analysis of intratumoral heterogeneity has the potential to serve as a valuable clinical marker to enhance the prediction of breast cancer prognosis. 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.

Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification

ABSTRACTPersonalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

Multiplicity issues in exploratory subgroup analysis

ABSTRACTThe general topic of subgroup identification has attracted much attention in the clinical trial literature due to its important role in the development of tailored therapies and personalized medicine. Subgroup search methods are commonly used in late-phase clinical trials to identify subsets of the trial population with certain desirable characteristics. Post-hoc or exploratory subgroup exploration has been criticized for being extremely unreliable. Principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining have been developed to address this criticism. These approaches emphasize fundamental statistical principles, including the importance of performing multiplicity adjustments to account for selection bias inherent in subgroup search.This article provides a detailed review of multiplicity issues arising in exploratory subgroup analysis. Multiplicity corrections in the context of principled subgroup search will be illustrated using the family of SIDES (subgroup identification based on differential effect search) methods. A case study based on a Phase III oncology trial will be presented to discuss the details of subgroup search algorithms with resampling-based multiplicity adjustment procedures.

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.

Multiply adjusted comparisons: A meta-analysis method to compare single-arm clinical-trial data to literature results regarding a competitor.

Prospective randomized controlled trials are difficult to obtain if a promising new therapy has to be tested against seemingly obsolete alternatives. One method to address this problem is to compare the results of (multicentre) trials to literature results. However, previous treatment-era changes and population-dependent results complicate objective comparisons. The presented approach describes a method to objectify such comparisons in cases in which individual raw data regarding a new therapy have to be compared to summary results regarding a conventional alternative published in the literature. The chosen example is the introduction of bovine neck veins as a substitute for dysfunctional human pulmonary valves, and the conventional therapeutic alternative is pulmonary-artery homografts. Literature research, subgroup identification, filtering, endpoint remodelling, weighting and, if necessary, confidence-limit calculation yield adjusted comparisons. These individual comparisons are then aggregated, first by article and then over several articles (similar to meta-analyses), resulting in a differentiated panel of answers (Multiply Adjusted Comparisons). In situations in which extensive raw data regarding a new therapeutic alternative but no randomized controlled trials and no raw data from previous studies using the conventional therapeutic alternative are available, the proposed method identifies the best evidence and is by far superior to unadjusted direct comparisons or gut feelings.

Subgroup Identification in Clinical Trials by Stochastic SIDEScreen Methods

ABSTRACTSubgroup identification for personalized medicine has become very popular in the last decade. Efficient recursive partitioning procedures adapted from machine learning are natural approaches for performing subgroup identification based on pre-defined biomarkers since they provide subgroups as terminal nodes in the decision tree. However, recursive partitioning is also known as a potentially unstable procedure with results being quite sensitive to normal sampling variability in the data. One common approach, borrowed from ensemble learning, to overcome such instability is application of recursive partitioning to multiple data sets sampled from the observed data followed by averaging the results over the collection of subgroups.This article proposes an alternative approach to subgroup identification in clinical trials that first evaluates the predictive strength of biomarkers based on variable importance and then applies recursive partitioning to the biomarkers with the highest variable importance scores. A deterministic version of this idea was implemented in the Adaptive SIDEScreen method that generates a collection of patient subgroups by retaining multiple candidate splits of each parent group by different biomarkers (Lipkovich and Dmitrienko 2014a, 2014b). Now, we extend the Adaptive SIDEScreen and introduce the Stochastic SIDEScreen method. The key idea is to introduce randomness in the subgroup generation process, borrowing from bagging methods, to produce a broader collection of subgroups. Specifically, the SIDES method, where the most promising biomarkers are selected for each parent group from a set of candidate biomarkers, is applied to multiple bootstrap samples of the data. This new approach leads to a more reliable biomarker selection process, which is especially important for smaller, early phase studies when biomarker selection is typically carried out. The method is illustrated using clinical trial examples.

Biopsychological and pathophysiological features of Cold-Heat subgroup of Sasang typology with Sasang Digestive Function Inventory, Sasang Personality Questionnaire and Body Mass Index

BackgroundThe Sasang typology is a traditional Korean personalized medicine and its Cold-Heat subgroup identification is essential for effective use of medical herbs and acupuncture. The purpose of this study was to discover differences between Cold-Heat subgroups with objective clinical measures and to examine its clinical usefulness. MethodsThe pathophysiological symptoms of the digestive system, temperament and body shape of 241 patients were measured using the Sasang Digestive Function Inventory (SDFI), Sasang Personality Questionnaire (SPQ) and Body Mass Index (BMI). The differences between Cold and Heat subgroups of each Sasang types were tested by Analysis of Covariance considering age and sex, while the associations of SDFI, SPQ and BMI with Cold-Heat subgroup were examined by logistic regression analysis. ResultsThere were significant differences between Cold and Heat subgroups in SDFI, SPQ and BMI for the So-Yang, SDFI and BMI for the Tae-Eum type and SDFI-Digestion subscale for the So-Eum type. Moreover, the SDFI-Digestion was a substantial predictor for Cold-Heat subgroup identification in three Sasang types. The logistic regression model with SDFI, SPQ and BMI correctly predicted 81.9%, 77% and 75.5% of the Cold-Heat subgroups in So-Yang, Tae-Eum and So-Eum types, respectively. ConclusionThe results of the present study showed that the objective and validated clinical measures of SDFI, SPQ and BMI would be useful for differentiating Cold-Heat subgroups of Sasang typology. Further clinical studies on pathophysiological mechanisms in Cold-Heat subgroup are required to generalize these results.

A unified approach for subgroup identification and individualized treatment recommendation with applications to randomized control trials and observational studies

A unified approach for subgroup identification and individualized treatment recommendation with applications to randomized control trials and observational studies

A Data-Driven Approach to Responder Subgroup Identification after Paired Continuous Theta Burst Stimulation.

Background: Modulation of cortical excitability by transcranial magnetic stimulation (TMS) is used for investigating human brain functions. A common observation is the high variability of long-term depression (LTD)-like changes in human (motor) cortex excitability. This study aimed at analyzing the response subgroup distribution after paired continuous theta burst stimulation (cTBS) as a basis for subject selection.Methods: The effects of paired cTBS using 80% active motor threshold (AMT) in 31 healthy volunteers were assessed at the primary motor cortex (M1) corresponding to the representation of the first dorsal interosseous (FDI) muscle of the left hand, before and up to 50 min after plasticity induction. The changes in motor evoked potentials (MEPs) were analyzed using machine-learning derived methods implemented as Gaussian mixture modeling (GMM) and computed ABC analysis.Results: The probability density distribution of the MEP changes from baseline was tri-modal, showing a clear separation at 80.9%. Subjects displaying at least this degree of LTD-like changes were n = 6 responders. By contrast, n = 7 subjects displayed a paradox response with increase in MEP. Reassessment using ABC analysis as alternative approach led to the same n = 6 subjects as a distinct category.Conclusion: Depressive effects of paired cTBS using 80% AMT endure at least 50 min, however, only in a small subgroup of healthy subjects. Hence, plasticity induction by paired cTBS might not reflect a general mechanism in human motor cortex excitability. A mathematically supported criterion is proposed to select responders for enrolment in assessments of human brain functional networks using virtual brain lesions.

Emerging pharmaceutical therapies for COPD.

COPD, for which cigarette smoking is the major risk factor, remains a worldwide burden. Current therapies provide only limited short-term benefit and fail to halt progression. A variety of potential therapeutic targets are currently being investigated, including COPD-related proinflammatory mediators and signaling pathways. Other investigational compounds target specific aspects or complications of COPD such as mucus hypersecretion and pulmonary hypertension. Although many candidate therapies have shown no significant effects, other emerging therapies have improved lung function, pulmonary hypertension, glucocorticoid sensitivity, and/or the frequency of exacerbations. Among these are compounds that inhibit the CXCR2 receptor, mitogen-activated protein kinase/Src kinase, myristoylated alanine-rich C kinase substrate, selectins, and the endothelin receptor. Activation of certain transcription factors may also be relevant, as a large retrospective cohort study of COPD patients with diabetes found that the peroxisome proliferator-activated receptor γ (PPARγ) agonists rosiglitazone and pioglitazone were associated with reduced COPD exacerbation rate. Notably, several therapies have shown efficacy only in identifiable subgroups of COPD patients, suggesting that subgroup identification may become more important in future treatment strategies. This review summarizes the status of emerging therapeutic pharmaceuticals for COPD and highlights those that appear most promising.

Differential Treatment Response to Insulin Intensification Therapy: A Post Hoc Analysis of a Randomized Trial Comparing Premixed and Basal-Bolus Insulin Regimens.

IntroductionIdentification of subgroups of patients that may benefit most from certain treatment is important because individual treatment response varies due to multiple contributing factors. The present study used the subgroup identification based on the differential effect search (SIDES) algorithm to identify subgroups with different treatment responses to insulin intensification therapies.MethodsThis was a post hoc analysis of a 24-week, multicenter, open-label, randomized, parallel study comparing prandial premixed therapy (PPT) to basal-bolus therapy (BBT). Patients with type 2 diabetes mellitus were randomized to PPT (insulin lispro mix 50/50 thrice daily with meals) or BBT (glargine at bedtime plus mealtime insulin lispro) insulin intensification therapies. The SIDES algorithm was used to identify the subgroups from at-goal patients [glycated hemoglobin (HbA1c) <7.0% (53.0 mmol/mol) at the end of 24 weeks; n = 182] who could have benefitted from insulin intensification therapies.ResultsBaseline characteristics of overall at-goal patients were comparable between PPT and BBT groups. The SIDES algorithm identified patients with race other than Caucasian (i.e., African–American, Asian, and Hispanic) and baseline fasting blood glucose (FBG) <8.89 mmol/L as a subgroup that could respond better to PPT relative to BBT than the overall at-goal patient population. In this identified subgroup population, the HbA1c mean (standard deviation) changes from baseline to endpoint in PPT and BBT groups were −2.27 (0.88)% versus −2.05 (0.75)%; p = 0.40, respectively; while in the overall at-goal patients, the HbA1c changes were −2.17 (0.79)% versus −2.34 (1.00)%; p = 0.19, respectively.ConclusionsThe preliminary results showed that the subgroup of patients with race other than Caucasian and FBG <8.89 mmol/L may respond better to premixed intensification therapy. This result provides some preliminary information for further investigation in prospective studies.FundingEli Lilly and Company.Clinical Trial RegistrationClinicaltrials.gov ID number: NCT00110370.

The patient perspective on remote monitoring of patients with an implantable cardioverter defibrillator: Narrative review and future directions.

Studies have shown that remote patient monitoring (RPM) of implantable cardioverter defibrillators (ICDs) is at least comparable to in-clinic follow-up with regard to clinical outcomes and might be cost-effective, yet RPM is not standard clinical practice within Europe. Better insight into the patient perspective on RPM may aid in its acceptance, implementation, and reimbursement. This narrative review (1) summarizes existing evidence on the impact of RPM on patient-reported outcomes and (2) discusses future directions in examining the patient perspective. Literature review indicated that only five randomized trials on RPM in ICD patients included patient-reported outcomes, with inconclusive results. Observational studies show a trend toward good patient satisfaction and acceptation of RPM. Yet, results should be interpreted with caution due to a number of limitations including a potential selection bias, use of generic/nonvalidated questionnaires, relatively short follow-up durations, and a lack of subgroup identification. Although RPM seems to be safe, effective, timely, and efficient, the patient perspective has received little attention so far. The scarce evidence on patient-reported outcomes in RPM studies seems to be positive, but future trials with a follow-up of ≥12 months and validated patient-reported outcome measures are needed. The REMOTE-CIED study from our group is the first prospective randomized controlled trial primarily designed to examine the patient perspective on RPM, and is powered to identify characteristics associated with RPM satisfaction and benefit. Results are expected in 2018 and will add valuable information to the current evidence.