Treatment Effects In Subgroups Research Articles

Assessing Frailty-Specific Treatment Effect in Cardiovascular Disease: A Systematic Review.

Clinicians are increasingly considering using frailty assessments to individualize treatment for older patients. It remains uncertain whether interventions to reduce cardiovascular disease (CVD) events offer similar benefits between older adults with and without frailty. A systematic literature search was undertaken in PubMed and Embase, adhering to PRISMA guidelines. Key inclusion criteria were randomized controlled trials published between January 2007 and September 2024 with CVD outcomes as an endpoint and data on frailty-specific treatment effects. Data were collected for population characteristics, intervention, follow-up time, frailty measure, outcome rates, and frailty subgroup treatment effect. Due to heterogeneity among the studies, the results were not pooled. The search identified 151 unique studies, of which 18 were included. Using Cochrane Risk of Bias 2.0, 12 out of the 18 studies have low risk of bias. The intervention was more effective in frail participants than in non-frail counterparts in two studies (e.g., aerobic exercise), less effective in frail participants in three studies (e.g., intensive lifestyle intervention), similarly effective across frailty levels in seven studies (e.g., prasugrel), and inconclusive in six studies (e.g., edoxaban). Some treatments were similarly effective across frailty level by hazard ratio but had a greater reduction in absolute risk for frail versus non-frail patients. Cardiovascular interventions may provide differential benefits by patients' frailty. These findings suggest the potential utility of frailty assessment for optimizing cardiovascular interventions.

The application of Bayesian method in estimating heterogeneity of treatment effect

It is well recognized that the benefit of medical interventions may not be distributed evenly in the target population due to patient heterogeneity, and conclusions based on traditional randomized clinical trials do not apply to everyone. Considering the increasing cost of randomized trials and difficulties in recruiting patients, there is an urgent need to develop analytical methods to estimate treatment effects in subgroups. In particular, due to limited sample sizes of subgroups, standard analysis tends to yield wide confidence intervals for the treatment effect, which are often uninformative. In this article, the superiority of the empirical Bayes estimate of the conditional average treatment effect is demonstrated by using a linear regression model to specifically represent treatment-covariate interactions. To obtain more accurate prior distributions and posterior estimates of treatment effects in subgroups, we utilize the covariate-dependent precision parameter across subgroups. Based on subgroup data obtained from clinical trials, the covariate-dependent precision parameter is used to assess the similarity of treatment effects across subgroups, which is an approach to obtain more reasonable prior information. To verify the validity of the empirical Bayes method, we evaluate it by a real clinical trial comparing insulin and metformin.

Pharmacological interventions for co-occurring psychopathology in people with borderline personality disorder: secondary analysis of the Cochrane systematic review with meta-analyses.

Medications are commonly used to treat co-occurring psychopathology in persons with borderline personality disorder (BPD). To systematically review and integrate the evidence of medications for treatment of co-occurring psychopathology in people with BPD, and explore the role of comorbidities. Building on the current Cochrane review of medications in BPD, an update literature search was done in March 2024. We followed the methods of this Cochrane review, but scrutinised all identified placebo-controlled trials post hoc for reporting of non BPD-specific ('co-occurring') psychopathology, and explored treatment effects in subgroups of samples with and without defined co-occurring disorders. GRADE ratings were done to assess the evidence certainty. Twenty-two trials were available for quantitative analyses. For antipsychotics, we found very-low-certainty evidence (VLCE) of an effect on depressive symptoms (standardised mean difference (SMD) -0.22, P = 0.04), and low-certainty evidence (LCE) of an effect on psychotic-dissociative symptoms (SMD -0.28, P = 0.007). There was evidence of effects of anticonvulsants on depressive (SMD -0.44, P = 0.02; LCE) and anxious symptoms (SMD -1.11, P < 0.00001; VLCE). For antidepressants, no significant findings were observed (VLCE). Exploratory subgroup analyses indicated a greater effect of antipsychotics in samples including participants with co-occurring substance use disorders on psychotic-dissociative symptoms (P = 0.001). Our findings, based on VLCE and LCE only, do not support the use of pharmacological interventions in people with BPD to target co-occurring psychopathology. Overall, the current evidence does not support differential treatment effects in persons with versus without defined comorbidities. Medications should be used cautiously to target co-occurring psychopathology.

Elastic Bands Improve Oral Appliance Treatment Effect on Obstructive Sleep Apnoea: A Randomised Crossover Trial.

Oral appliances (OAs) that limit mouth opening during sleep, such as monobloc appliances, have shown superior treatment effects in subgroups of patients with obstructive sleep apnoea. The application of elastic bands on bibloc appliances may resemble these benefits. The primary objective was to investigate if application of elastic bands to bibloc appliances improves treatment success (> 50% reduction of respiratory event index (REI)), in addition to other subjective variables. Furthermore, we aimed to identify variables predicting the need for elastic bands in OA treatment. Included patients (n = 69) were randomly assigned to OA treatment with or without elastic bands. After 3 weeks, treatment effect was investigated with home respiratory polygraphy and questionnaires. Thereafter, patients changed treatment modality, with identical follow-up regime. Statistical analyses were performed using Student's t-test and Pearson's chi-squared test to investigate differences between the two treatment modalities, and logistic regression analysis was conducted to investigate variables tentatively associated with treatment success. Based on REI, the success rate with OA treatment was in favour of elastic bands (53.9% vs. 34.6%, p = 0.002). Male sex and larger maximum mouth opening were identified as predictors for increased treatment success with elastic bands. The main benefit with elastic bands seemed to be greater reduction of REI when supine. However, patients seem to prefer OA without elastic bands. Elastic bands improved OA treatment effect by reducing the REI in supine position. Patient groups that seemed to benefit from elastic bands in OA treatment were men with large maximum mouth openings.

Reference-trial-informed design to explore treatment effects in trial-underrepresented subgroups.

Reference-trial-informed design to explore treatment effects in trial-underrepresented subgroups.

Bayesian Hierarchical Models for Subgroup Analysis.

In conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much different from the treatment effect in the overall population. A Bayesian hierarchical model (BHM) can be used to derive more precise, and less heterogenous estimates of subgroup treatment effects that are closer to the treatment effect in the overall population. BHM assumes exchangeability in treatment effect across subgroups after adjusting for effect modifiers and other relevant covariates. In this article, we will discuss the technical details for applying one-way and multi-way BHM using summary-level statistics, and patient-level data for subgroup analysis. Four case studies based on four new drug applications are used to illustrate the application of these models in subgroup analyses for continuous, dichotomous, time-to-event, and count endpoints.

Impact of time-of-day infusion of camrelizumab combined with chemotherapy on efficacy in advanced malignant tumors.

e14634 Background: The combination of immune checkpoint inhibitors (ICIs) with chemotherapy has emerged as a standard first-line treatment strategy for advanced cancers. Camrelizumab and Tislelizumab, representative ICIs, have approval by the Chinese NMPA for first-line treatment of advanced cancers such as lung cancer and esophageal cancer. Previous studies have indicated that the timing of drug administration can influence the efficacy of ICIs. However, studies on the ICIs plus chemotherapy are lacking. Therefore, we conducted a retrospective study to explore the effect of the timing of infusion on patients with advanced cancer undergoing treatment with ICIs combined chemotherapy. Methods: We retrospectively enrolled a total of 253 patients diagnosed with advanced cancers between 2017 and 2023 in Guangdong Second Provincial General Hospital. Among these patients, 109 received camrelizumab (Cam cohort) plus chemotherapy and 65 received tislelizumab (Tis cohort) plus chemotherapy. Patients were grouped into morning (AM) and afternoon (PM) categories based on the timing of their ICIs administration (&lt; 1 times versus ≥ 1 times after 16:00). The Response Evaluation Criteria in Solid Tumors Criteria V.1.1 was used to evaluate the response to treatment. The primary endpoint was progression-free survival (PFS). Results: The results consistently showed that the PM group had a longer PFS than the AM group in the overall patients (p &lt; 0.01). In the Cam cohort, the PM group (n = 48) exhibited a longer PFS compared to the AM group (n = 61) (16.8 months vs. -, p = 0.02). However, no significant difference in PFS was observed among patients in the Tis cohort (AM vs. PM, n = 34 vs. n = 31, 16.1 months vs. -, p = 0.51). Subgroup analyses indicated that the PM group had a more favorable PFS treatment effect in most subgroups. The incidence of any Treatment Emerged Adverse Event (TEAE) (Cam cohort, AM vs. PM, 98.52% vs. 92.68%; Tis cohort, AM vs. PM, 97.05% vs. 100.00%) and grade 3 TEAEs (Cam cohort, AM vs. PM, 5.80% vs.7.30%; Tis cohort, AM vs.PM - vs. 12.90%) was similar in the Cam cohort and Tis cohort. The most common adverse events are anemia (Cam cohort vs. Tis cohort, 70.58% vs. 79.41%), thrombocytopenia (Cam cohort vs. Tis cohort, 54.41% vs. 58.82%) and lymphopenia (Cam cohort vs. Tis cohort, 44.11% vs. 44.11%). Conclusions: The findings of this study highlight a significant variation in daily rhythms among advanced cancer patients undergoing treatment with ICIs plus chemotherapy. Interestingly, a trend was observed in patients treated with camrelizumab, where those receiving treatment in the afternoon demonstrated a longer PFS. However, this trend was not observed in patients treated with tislelizumab. These results suggest that time-of-day infusion could potentially serve as a personalized strategy for patients treated with combination ICIs therapy.

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.

Effectiveness of PCSK9 inhibitors: A Target Trial Emulation framework based on Real-World Electronic Health Records.

Low-Density Lipoprotein (LDL) cholesterol is one of the main target for cardiovascular (CV) prevention and therapy. In the last years, Proprotein Convertase Subtilisin-Kexin type 9 inhibitors (PCSK9-i) has emerged as a key therapeutic target to lower LDL and were introduced for prevention of CV events. Recently (June 2022) the Italian Medicines Agency (AIFA) modified the eligibility criteria for the use of PCSK9-i. We designed an observational study to estimate the prevalence of eligible subjects and evaluate the effectiveness of PCSK9-i applying a Target Trial Emulation (TTE) approach based on Electronic Health Records (EHR). Subjects meeting the eligibility criteria were identified from July 2017 (when PCSK9-i became available) to December 2020. Outcomes were all-cause death and the first hospitalization. Among eligible subjects, we identified those treated at date of the first prescription. Inverse Probability of Treatment Weights (IPTW) were estimated including demographic and clinical covariates, history of treatment with statins and the month/year eligibility date. Competing risk models on weighted cohorts were used to derive the Average Treatment Effect (ATE) and the Conditional Average Treatment Effect (CATE) in subgroups of interest. Out of 1976 eligible subjects, 161 (8%) received treatment with PCSK9-i. Treated individuals were slightly younger, predominantly male, had more severe CV conditions, and were more often treated with statin compared to the untreated subjects. The latter exhibited a higher prevalence of non-CV comorbidities. A significant absolute and relative risk reduction of death and a lower relative risk for the first hospitalization was observed. The risk reduction for death was confirmed in CATE analysis. PCSk9-i were prescribed to a minority of eligible subjects. Within the TTE framework, the analysis confirmed the association between PCSK9-i and lower risk of events, aligning with findings from randomized clinical trials (RCTs). In our study, PCSK9-i provided protection specifically against all-cause death, expanding upon the evidence from RCTs that had primarily focused on composite CV outcomes.

Marginal versus conditional rate estimation for count and recurrent event data with an estimand framework

Count and recurrent event endpoints are common key efficacy endpoints in clinical research. For example, in clinical research of pulmonary diseases such as chronic obstructive pulmonary disease (COPD) or asthma, the reduction of the occurrence of a recurrent event, pulmonary exacerbation (PEx) caused by acute respiratory symptoms, is often used to measure the treatment effect. The occurrence of PEx is often analyzed with nonlinear models, such as Poisson regression or Negative Binomial regression. It is observed that model-estimated within-group PEx rates are often lower than the descriptive statistics of within-group PEx rates. Motivated by this observation, we explore their relationship mathematically and demonstrate that it is due to the difference between conditional PEx rates and population-level PEx rates (marginal rates). Our findings corroborate the recent FDA guidance (2023) [1], which discusses considerations for covariate adjustment in nonlinear models, and that conditional or subgroup treatment effects with covariate adjustment may differ from marginal treatment effects. In this article, we demonstrate how covariate adjustment impacts the estimation of event rates and rate ratios with both closed form and simulation studies. Additionally, following the ICH E9 addendum on the estimand framework [2], we discuss the estimand framework for count and recurrent event data.

Planning stepped wedge cluster randomized trials todetecttreatment effect heterogeneity.

Stepped wedge design is a popular research design that enables a rigorous evaluation of candidate interventions by using a staggered cluster randomization strategy. While analytical methods were developed for designing stepped wedge trials, the prior focus has been solely on testing for the average treatment effect. With a growing interest on formal evaluation of the heterogeneity of treatment effects across patient subpopulations, trial planning efforts need appropriate methods to accurately identify sample sizes or design configurations that can generate evidence for both the average treatment effect and variations in subgroup treatment effects. To fill in that important gap, this article derives novel variance formulas for confirmatory analyses of treatment effect heterogeneity, that are applicable to both cross-sectional and closed-cohort stepped wedge designs. We additionally point out that the same framework can be used for more efficient average treatment effect analyses via covariate adjustment, and allows the use of familiar power formulas for average treatment effect analyses to proceed. Our results further sheds light on optimal design allocations of clusters to maximize the weighted precision for assessing both the average and heterogeneous treatment effects. We apply the new methods to the Lumbar Imaging with Reporting of Epidemiology Trial, and carry out a simulation study to validate our new methods.

Quantifying and reducing inequity in average treatment effect estimation

BackgroundAcross studies of average treatment effects, some population subgroups consistently have lower representation than others which can lead to discrepancies in how well results generalize.MethodsWe develop a framework for quantifying inequity due to systemic disparities in sample representation and a method for mitigation during data analysis. Assuming subgroup treatment effects are exchangeable, an unbiased sample average treatment effect estimator will have lower mean-squared error, on average across studies, for subgroups with less representation when treatment effects vary. We present a method for estimating average treatment effects in representation-adjusted samples which enables subgroups to optimally leverage information from the full sample rather than only their own subgroup’s data. Two approaches for specifying representation adjustment are offered—one minimizes average mean-squared error for each subgroup separately and the other balances minimization of mean-squared error and equal representation. We conduct simulation studies to compare the performance of the proposed estimators to several subgroup-specific estimators.ResultsWe find that the proposed estimators generally provide lower mean squared error, particularly for smaller subgroups, relative to the other estimators. As a case study, we apply this method to a subgroup analysis from a published study.ConclusionsWe recommend the use of the proposed estimators to mitigate the impact of disparities in representation, though structural change is ultimately needed.

Pembrolizumab or Placebo Plus Chemotherapy With or Without Bevacizumab for Persistent, Recurrent, or Metastatic Cervical Cancer

The KEYNOTE-826 randomized clinical trial showed statistically significant and clinically meaningful survival benefits with the addition of pembrolizumab to chemotherapy with or without bevacizumab in patients with persistent, recurrent, or metastatic cervical cancer. Treatment effects in patient subgroups of the study population are unknown. To assess efficacy outcomes in patient subgroups of KEYNOTE-826. Exploratory subgroup analyses were conducted in a global, phase 3, randomized, double-blind, placebo-controlled clinical trial. Participants included women with persistent, recurrent, or metastatic adenocarcinoma, adenosquamous carcinoma, or squamous cell carcinoma of the cervix that had not been treated with systemic chemotherapy and was not amenable to curative treatment. This subanalysis was conducted from November 20, 2018, to May 3, 2021. Pembrolizumab, 200 mg, every 3 weeks or placebo for up to 35 cycles plus chemotherapy (paclitaxel, 175 mg/m2, plus cisplatin, 50 mg/m2, or carboplatin AUC 5 [area under the free carboplatin plasma concentration vs time curve]) with or without bevacizumab, 15 mg/kg. Overall survival (OS) and progression-free survival (PFS) by investigator assessment per Response Evaluation Criteria in Solid Tumors version 1.1 in subgroups defined by use of bevacizumab (yes or no), choice of platinum (carboplatin or cisplatin), prior chemoradiotherapy (CRT) exposure only (yes or no), and histologic type (squamous or nonsquamous) in patients with programmed cell death ligand 1-positive tumors (defined as a combined positive score [CPS] ≥1) and in the intention-to-treat population. A total of 617 patients (median age, 51 years; range, 22-82 years) were enrolled in the trial. In the CPS greater than or equal to 1 population, hazard ratios (HRs) for OS favored the pembrolizumab group in all subgroups: with bevacizumab (HR, 0.62; 95% CI, 0.45-0.87) and without bevacizumab (HR, 0.67; 95% CI, 0.47-0.96), use of carboplatin (HR, 0.65; 95% CI, 0.50-0.85) and cisplatin (HR, 0.53; 95% CI, 0.27-1.04), with prior CRT only (HR, 0.56; 95% CI, 0.39-0.81) and without prior CRT only (HR, 0.72; 95% CI, 0.52-1.00), and squamous (HR, 0.60; 95% CI, 0.46-0.79) and nonsquamous (HR, 0.70; 95% CI, 0.41-1.20) histologic type. In the intention-to-treat population, HRs for OS also favored the pembrolizumab group in all subgroups: with bevacizumab (HR, 0.63; 95% CI, 0.47-0.87) and without bevacizumab (HR, 0.74; 95% CI, 0.53-1.04), use of carboplatin (HR, 0.69; 95% CI, 0.54-0.89) or cisplatin (HR, 0.59; 95% CI, 0.32-1.09), with prior CRT only (HR, 0.64; 95% CI, 0.45-0.91) and without prior CRT only (HR, 0.71; 95% CI, 0.53-0.97), and squamous (HR, 0.61; 95% CI, 0.47-0.80) and nonsquamous (HR, 0.76; 95% CI, 0.47-1.23) histologic type. Similar to OS, the addition of pembrolizumab prolonged PFS across all subgroups in the CPS greater than or equal to 1 and intention-to-treat populations. The findings of this trial suggest that adding pembrolizumab to chemotherapy with or without bevacizumab improved OS across subgroups of patients with persistent, recurrent, or metastatic cervical cancer. ClinicalTrials.gov Identifier: NCT03635567.

Identifying the Optimal Conditioning Intensity of Hematopoietic Stem Cell Transplantation in Patients with Acute Myeloid Leukemia in Complete Remission

Background Allogeneic hematopoietic stem cell transplantation (HSCT) as consolidation therapy is considered a curative option for acute myeloid leukemia (AML) in complete remission (CR) to prevent relapse. Many studies have investigated the optimal conditioning intensity, comparing reduced-intensity conditioning regimen (RIC) and myeloablative conditioning regimen (MAC) for patients with AML in CR. However, inconsistent results have been observed, possibly due to selection biases and significant heterogeneous treatment effects when focusing on average treatment effect. Some studies have attempted to assess treatment effects in specific subgroups, highlighting the heterogeneity conditioning intensity's impact. However, these analyses typically examined one factor at a time, overlooking potential multiple-factor combinations that might contribute to such heterogeneity.To address these limitations and surpass conventional comparison studies and subgroup analyses, machine-learning methods for group identification have been developed. In this study, we aimed to identify patient groups in which MAC or RIC had a superior PFS in patients with AML in CR using a machine-learning approach. Methods Our study included 3273 patients aged 40-69 years with AML in CR from the TRUMP2. These patients were divided into training (N=2020) and validation cohorts (N=1253). In the training cohort, we employed a group identification model to identify patient subsets. Preprocessing of variables was performed to ensure the ease of use in clinical practice. We modeled the magnitude of interaction between treatment and covariates without the main effect of covariates, thus leading to the construction of a scoring system that measures the individualized-treatment effect. The objective variable was set as 1-year PFS, and the interaction effects between treatment and covariates were estimated using a regression model with propensity score weight and LASSO regularization. The sum of the interaction term between treatment and covariates was defined as the “Conditioning Intensity Score”. Patients were categorized into high-score and low-score groups using a cut-off level of median in patients who received RIC. Subsequently, in the validation cohort, we estimated the average treatment effect of RIC compared to MAC for each group with hazard ratios (HR) using inverse treatment probability weight analysis. Results In the training cohort, the median age was 55 years (IQR, 48-61 years), with 1110 (55%) male patients. Among them, 666 (33%) received RIC, while the others underwent MAC. Conditioning Intensity Model was developed and consistent with followings; age over 49 (1.4 points), age over 49 and male (0.8 points), age over 49 and poor karyotype (0.5 point), age over 49 and cord blood transplantation (0.9 points), poor karyotype (-1.6 points), poor karyotype and primary induction failure (8.0 points), poor karyotype and bone marrow transplantation (-5.3 points), cord blood transplantation and hematopoietic cell transplant comorbidity index over 2 (2.1 points) and cord blood transplantation and extra bone marrow involvement (-8.6 points). The cut-off level was set as 2.2. The weighted HR of RIC on 1-year PFS was 0.56 (95%CI; 0.40-0.79, p &amp;lt; 0.001) in the high-score group and 1.23 (95%CI: 0.97- 1.57, p=0.094) in the low-score group. In the entire validation cohort, there were no significant differences in PFS between the RIC and MAC (67.7% [95% CI: 62.1-70.3%] in the RIC group vs. 70.3% [95% CI: 67.2-73.1%] in the MAC group, P=0.318). Patients were divided into two groups based on Conditioning Intensity Score. In the high-score group, the 1-year PFS was 73.9% (95% CI: 65.0-80.9%) in the RIC group vs. 66.2% (95% CI: 58.3-73.0%) in the MAC group ( P=0.181), with a weighted HR of 0.69 (95% CI: 0.43-1.11) for PFS in the RIC group compared to that the MAC group ( P=0.125) (Figure left). Conversely, in the low-score group, the 1-year PFS was 63.7% (95% CI: 56.3-70.2%) in the RIC group vs. 71.2% (95% CI: 67.8-74.2%) in the MAC group ( P=0.031), with a weighted HR of 1.35 (95% CI: 0.96-1.89) in the RIC group compared to the MAC group (log-rank P=0.086) (Figure right). Conclusion In conclusion, we have demonstrated the feasibility of utilizing a machine learning method for subgroup identification of conditioning regimens in patients with AML in CR. This established model holds promise for designing patient-specific regimens.

Subgroup Identification Using Virtual Twins for Human Microbiome Studies.

Even when the same treatment is employed, some patients are cured, while others are not. The patients that are cured may have beneficial microbes in their body that can boost treatment effects, but it is vice versa for the patients that are not cured. That is, treatment effects can vary depending on the patient's microbiome. If the effects of candidate treatments are well-predicted based on the patient's microbiome, we can select a treatment that is suited to the patient's microbiome or alter the patient's microbiome to improve treatment effects. Here, I introduce a streamlined analytic method, microbiome virtual twins (MiVT), to probe for the interplay between microbiome and treatment. MiVT employs a new prediction method, distance-based machine learning (dML), to improve prediction accuracy in microbiome studies and a new significance test, bootstrap-based test for regression tree (BoRT), to test if each subgroup's treatment effect is the same with the overall treatment effect. MiVT will serve as a useful guideline in microbiome-based personalized medicine to select the therapy that is most suited to the patient's microbiome or to tune the patient's microbiome to be suited to the treatment.

A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer

BackgroundMany scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used.MethodsA PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported.ResultsOf 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity.ConclusionsMost studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies.

Irinotecan- vs. Oxaliplatin-Based Doublets in KRASG12C-Mutated Metastatic Colorectal Cancer-A Multicentre Propensity-Score-Matched Retrospective Analysis.

KRASG12C-mutated metastatic colorectal cancer (mCRC) has recently been recognized as a distinct druggable molecular entity; however, there are limited data on its sensitivity to standard chemotherapy. In the near future, the combination of chemotherapy plus a KRASG12C-inhibitor might become the standard of care; however, the optimal chemotherapy backbone is unknown. A multicentre retrospective analysis was conducted including KRASG12C-mutated mCRC patients treated with first-line FOLFIRI or FOLFOX +/- bevacizumab. Both unmatched and propensity-score-matched analysis (PSMA) were conducted, with PSMA controlling for: previous adjuvant chemotherapy, ECOG PS, use of bevacizumab in first line, timing of metastasis appearance, time from diagnosis to first-line start, number of metastatic sites, presence of mucinous component, gender, and age. Subgroup analyses were also performed to investigate subgroup treatment-effect interactions. KRASG12D-mutated patients were analysed as control. One hundred and four patients treated with irinotecan-(N = 47) or oxaliplatin-based (N = 57) chemotherapy were included. In the unmatched population, objective response rate (ORR) and median (m) progression-free and overall survival (mPFS and mOS) were comparable between the treatment arms. However, a late (>12 months) PFS advantage was observed with irinotecan (HR 0.62, p = 0.02). In the PSMA-derived cohort, a significant improvement with irinotecan vs. oxaliplatin was observed for both PFS and OS: 12- and 24-month PFS rates of 55% vs. 31% and 40% vs. 0% (HR 0.40, p = 0.01) and mOS 37.9 vs. 21.7 months (HR 0.45, p = 0.045), respectively. According to the subgroup analysis, interaction effects between the presence of lung metastases and treatment groups were found in terms of PFS (p for interaction = 0.08) and OS (p for interaction = 0.03), with a higher benefit from irinotecan in patients without lung metastases. No difference between treatment groups was observed in the KRASG12D-mutated cohort (N = 153). First-line irinotecan-based regimens provided better survival results in KRASG12C-mutated mCRC patients and should be preferred over oxaliplatin. These findings should also be considered when investigating chemotherapy plus targeted agent combinations.

FDA analysis of treatment efficacy based on etiology of hepatocellular carcinoma.

4117 Background: Retrospective analyses of trial-level data report that etiology of hepatocellular carcinoma (HCC) (e.g., viral versus [vs.] non-viral) may influence efficacy of systemic therapies used in advanced HCC. FDA conducted an analysis of 9 randomized controlled trials (RCTs; n=5,417) to assess treatment effects based on drug class, treatment line, and HCC etiology. Methods: FDA conducted a retrospective, exploratory analysis of 3 RCTs comparing first line (1L) treatment with anti-programmed cell death (ligand)-1-containing regimens (αPD-[L]1) to single-agent anti-vascular endothelial growth factor therapy (αVEGF), and 6 RCTs comparing second line (2L) treatment consisting of αPD-(L)1 or αVEGF to placebo. For each line of treatment (1L or 2L), meta-analyses of overall survival (OS) were performed for the overall treatment effect and by disease etiology, viral (hepatitis B [HBV], or hepatitis C [HCV]) vs. non-viral. Subgroup analyses were performed to investigate the confounding effect of subsequent therapy (yes vs. no) and region (Asian vs. non-Asian) on etiology. Trial-level hazard ratios (HRs) and 95% confidence intervals (CI) were mostly calculated using patient-level data submitted to FDA. Pooled HRs and CIs were calculated using a random-effects model. Results: While 1L αPD-(L)1 improved OS compared to 1L αVEGF in the overall population (HR [95% CI]: 0.8 [0.73, 0.88]), treatment effects were comparable for viral (HR [95% CI]: 0.75 [0.6, 0.92]; n=1,435) and non-viral (HR [95% CI]: 0.83 [0.72, 0.95]; n=979) subgroup analyses, and not influenced by subsequent therapy or region. Analyses based on specific viral infection (i.e., HBV or HCV), showed that improvement in OS for αPD-(L)1 vs. αVEGF appeared larger for HBV (HR [95% CI]: 0.7 [0.59, 0.82]; n=833) compared to HCV (HR [95% CI]: 0.83 [0.6, 1.16]; n=602). Treatment effects were also comparable in 2L for different etiologies for both αPD-(L)1 (HR [95% CI] for viral: 0.76 [0.62, 0.93]; n=529 and non-viral: 0.85 [0.65, 1.11]; n=337) and αVEGF (HR [95% CI] for viral: 0.79 [0.69, 0.9]; n=1,288 and non-viral: 0.74 [0.63, 0.88]; n=849), and not influenced by subsequent therapy. Nonetheless, compared to placebo, αPD-(L)1 had a larger treatment effect with HBV (HR [95% CI]: 0.69 [0.5, 0.94]; n=459) compared to non-viral etiologies. Conclusions: The results did not demonstrate that etiology of HCC was predictive of OS for both treatment classes based on retrospective, exploratory meta-analyses of 9 RCTs; however, the treatment effects may be more pronounced in patients with HBV who received αPD-(L)1 as 1L or 2L. Uncertainty remains regarding subgroup treatment effects given limited number of trials in the analyses (mostly leading to approvals), overlapping CIs, number of patients in some subgroups, retrospective nature, and exclusion of trials with inadequate data regarding etiology of HCC.

Finding the (biomarker-defined) subgroup of patients who benefit from a novel therapy: No time for a game of hide and seek.

An important element of precision medicine is the ability to identify, for a specific therapy, those patients for whom benefits of that therapy meaningfully exceed the risks. To achieve this goal, treatment effect usually is examined across subgroups defined by a variety of factors, including demographic, clinical, or pathologic characteristics or by molecular attributes of patients or their disease. Frequently such subgroups are defined by the measurement of biomarkers. Even though such examination is necessary when pursuing this goal, the evaluation of treatment effect across a variety of subgroups is statistically fraught due to both the danger of inflated false-positive error rate from multiple testing and the inherent insensitivity to how treatment effects differ across subgroups.Pre-specification of subgroup analyses with appropriate control of false-positive (i.e. type I) error is recommended when possible. However, when subgroups are specified by biomarkers, which could be measured by different assays and might lack established interpretation criteria, such as cut-offs, it might not be possible to fully specify those subgroups at the time a new therapy is ready for definitive evaluation in a Phase 3 trial. In these situations, further refinement and evaluation of treatment effect in biomarker-defined subgroups might have to take place within the trial. A common scenario is that evidence suggests that treatment effect is a monotone function of a biomarker value, but optimal cut-offs for therapy decisions are not known. In this setting, hierarchical testing strategies are widely used, where testing is first conducted in a particular biomarker-positive subgroup and then is conducted in the expanded pool of biomarker-positive and biomarker-negative patients, with control for multiple testing. A serious limitation of this approach is the logical inconsistency of excluding the biomarker-negatives when evaluating effects in the biomarker-positives, yet allowing the biomarker-positives to drive the assessment of whether a conclusion of benefit could be extrapolated to the biomarker-negative subgroup.Examples from oncology and cardiology are described to illustrate the challenges and pitfalls. Recommendations are provided for statistically valid and logically consistent subgroup testing in these scenarios as alternatives to reliance on hierarchical testing alone, and approaches for exploratory assessment of continuous biomarkers as treatment effect modifiers are discussed.

Subgroup analyses and pre-specification.

Large clinical trials provide the opportunity to assess treatment effects in subgroups of patients, based on baseline demographic and disease-related factors, and there is always great interest in these analyses. Generally, the term "pre-specification" has major ramifications for clinical trials, particularly for adequate and well-controlled trials that are designed for formal hypothesis testing. Pre-specification is the "holy grail" of modern trials, as choosing analytical approaches with data in-hand will inflate the type I error rate. But "pre-specification" often has a different meaning with respect to subgroup analyses.