Ilya Lipkovich

Strategies for Identifying Predictive Biomarkers and Subgroups with Enhanced Treatment Effect in Clinical Trials Using SIDES

Several approaches to identification of predictive biomarkers and subgroups of patients with enhanced treatment effect have been proposed in the literature. The SIDES method introduced in Lipkovich et al. (2011) adopts a recursive partitioning algorithm for screening treatment-by-biomarker interactions. This article introduces an improved biomarker discovery/subgroup search method (SIDEScreen). The SIDEScreen method relies on a two-stage procedure that first selects a small number of biomarkers with the highest predictive ability based on an appropriate variable importance score and then identifies subgroups with enhanced treatment effect based on the selected biomarkers. The two-stage approach helps increase the signal-to-noise ratio by screening out noninformative biomarkers. We evaluate operating characteristics of the standard SIDES method and two SIDEScreen procedures based on fixed and adaptive screens. Our main finding is that the adaptive SIDEScreen method is a more flexible biomarker discovery tool than SIDES and it better handles multiplicity in complex subgroup search problems. The methods presented in the article are illustrated using a clinical trial example.

Tutorial in biostatistics: data‐driven subgroup identification and analysis in clinical trials

It is well known that both the direction and magnitude of the treatment effect in clinical trials are often affected by baseline patient characteristics (generally referred to as biomarkers). Characterization of treatment effect heterogeneity plays a central role in the field of personalized medicine and facilitates the development of tailored therapies. This tutorial focuses on a general class of problems arising in data-driven subgroup analysis, namely, identification of biomarkers with strong predictive properties and patient subgroups with desirable characteristics such as improved benefit and/or safety. Limitations of ad-hoc approaches to biomarker exploration and subgroup identification in clinical trials are discussed, and the ad-hoc approaches are contrasted with principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining. A general framework for evaluating predictive biomarkers and identification of associated subgroups is introduced. The tutorial provides a review of a broad class of statistical methods used in subgroup discovery, including global outcome modeling methods, global treatment effect modeling methods, optimal treatment regimes, and local modeling methods. Commonly used subgroup identification methods are illustrated using two case studies based on clinical trials with binary and survival endpoints. Copyright

Delayed-start analysis: Mild Alzheimer's disease patients in solanezumab trials, 3.5 years

Solanezumab is an anti‐amyloid monoclonal antibody in clinical testing for treatment of Alzheimers disease (AD). Its mechanism suggests the possibility of slowing the progression of AD.

A Multiple-Imputation-Based Approach to Sensitivity Analyses and Effectiveness Assessments in Longitudinal Clinical Trials

It is important to understand the effects of a drug as actually taken (effectiveness) and when taken as directed (efficacy). The primary objective of this investigation was to assess the statistical performance of a method referred to as placebo multiple imputation (pMI) as an estimator of effectiveness and as a worst reasonable case sensitivity analysis in assessing efficacy. The pMI method assumes the statistical behavior of placebo- and drug-treated patients after dropout is the statistical behavior of placebo-treated patients. Thus, in the effectiveness context, pMI assumes no pharmacological benefit of the drug after dropout. In the efficacy context, pMI is a specific form of a missing not at random analysis expected to yield a conservative estimate of efficacy. In a simulation study with 18 scenarios, the pMI approach generally provided unbiased estimates of effectiveness and conservative estimates of efficacy. However, the confidence interval coverage was consistently greater than the nominal coverage rate. In contrast, last and baseline observation carried forward (LOCF and BOCF) were conservative in some scenarios and anti-conservative in others with respect to efficacy and effectiveness. As expected, direct likelihood (DL) and standard multiple imputation (MI) yielded unbiased estimates of efficacy and tended to overestimate effectiveness in those scenarios where a drug effect existed. However, in scenarios with no drug effect, and therefore where the true values for both efficacy and effectiveness were zero, DL and MI yielded unbiased estimates of efficacy and effectiveness.

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.

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

Abstract 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.

The challenges of evaluating dose response in flexible-dose trials using marginal structural models

Assessing dose response from flexible-dose clinical trials is problematic. The true dose effect may be obscured and even reversed in observed data because dose is related to both previous and subsequent outcomes. To remove selection bias, we propose marginal structural models, inverse probability of treatment-weighting (IPTW) methodology. Potential clinical outcomes are compared across dose groups using a marginal structural model (MSM) based on a weighted pooled repeated measures analysis (generalized estimating equations with robust estimates of standard errors), with dose effect represented by current dose and recent dose history, and weights estimated from the data (via logistic regression) and determined as products of (i) inverse probability of receiving dose assignments that were actually received and (ii) inverse probability of remaining on treatment by this time. In simulations, this method led to almost unbiased estimates of true dose effect under various scenarios. Results were compared with those obtained by unweighted analyses and by weighted analyses under various model specifications. The simulation showed that the IPTW MSM methodology is highly sensitive to model misspecification even when weights are known. Practitioners applying MSM should be cautious about the challenges of implementing MSM with real clinical data. Clinical trial data are used to illustrate the methodology.

Biomarker Evaluation and Subgroup Identification in a Pneumonia Development Program Using SIDES

This chapter discusses the general problem of exploratory subgroup analysis in the context of late-stage clinical development. In this context, exploratory subgroup analysis focuses on biomarker discovery and identification of subgroups with enhanced treatment effect in large clinical trial databases. A case study based on a Phase III development program in patients with nosocomial pneumonia is used to compare traditional approaches to subgroup search, based on univariate assessments of individual biomarkers, and a novel subgroup exploration method, which utilizes a recursive partitioning algorithm with a local treatment effect modeling approach. The SIDES (Subgroup Identification based on Differential Effect Search) method and its extensions (SIDEScreen method) have been used in multiple Phase II and Phase III programs to perform a comprehensive evaluation of candidate biomarkers and identify biomarker-based subgroup of patients with desirable characteristics (improved efficacy or acceptable safety). The chapter provides a detailed summary of key features of the SIDES method, including complexity control (subgroup pruning), biomarker screening to prevent data overfitting and application of resampling-based techniques to account for Type I error rate inflation inherent in subgroup exploration.

Who will benefit from antidepressants in the acute treatment of bipolar depression? A reanalysis of the STEP-BD study by Sachs et al. 2007, using Q-learning

BackgroundThere is substantial uncertainty regarding the efficacy of antidepressants in the treatment of bipolar disorders.MethodsTraditional randomized controlled trials and statistical methods are not designed to discover if, when, and to whom an intervention should be applied; thus, other methodological approaches are needed that allow for the practice of personalized, evidence-based medicine with patients with bipolar depression.ResultsDynamic treatment regimes operationalize clinical decision-making as a sequence of decision rules, one per stage of clinical intervention, that map patient information to a recommended treatment. Using data from the acute depression randomized care (RAD) pathway of the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study, we estimate an optimal dynamic treatment regime via Q-learning.ConclusionsThe estimated optimal treatment regime presents some evidence that patients in the RAD pathway of STEP-BD who experienced a (hypo)manic episode before the depressive episode may do better to forgo adding an antidepressant to a mandatory mood stabilizer.

Comparison of Several Multiple Imputation Strategies for Repeated Measures Analysis of Clinical Scales: To Truncate or Not To?

We evaluated via a simulation study several strategies for imputing missing ordinal outcomes in a longitudinal clinical trial, contrasting methods that involve truncation of imputed values outside plausible ranges with those that do not. Our aim was to identify a preferred imputation strategy for estimating treatment difference at study endpoint. Plausible data were simulated via resampling of existing placebo data sets and adding treatment effect; then different imputation strategies were evaluated under missingness at random (MAR) and varying dropout rates. Our conclusion is that imputation methods based on rounding and truncation lead to larger bias than strategies based on simple methods based on (nontruncated) multivariate normal distribution.