Different subtypes of chronic fatigue in childhood cancer survivors: A DCCSS LATER study.

INTRODUCTION: The aim of the current study was to investigate whether subtypes of chronic fatigue (CF) can be identified in childhood cancer survivors (CCS), and if so, to determine the characteristics of participants with a specific subtype. METHODS: Participants were included from the nationwide DCCSS LATER cohort. The Checklist Individual Strength (CIS) was completed to assess fatigue. Participants with CF (scored ≥35 on the fatigue severity subscale and indicated to suffer from fatigue for ≥6 months) were divided into subgroups using two-step cluster analysis based on the CIS concentration, motivation, and physical activity subscales. Differences between groups on demographics, psychosocial, lifestyle, and treatment-related variables were determined using ANOVA and chi-square analyses (univariable) and multinomial regression analysis (multivariable). RESULTS: A total of 1910 participants participated in the current study (n = 450 with CF; n = 1460 without CF). Three CF subgroups were identified: Subgroup 1 (n = 133, 29% of participants) had CF with problems in physical activity; Subgroup 2 (n = 111, 25% of participants) had CF with difficulty concentrating; and Subgroup 3 (n = 206, 46% of participants) had multi-dimensional CF. Compared to Subgroup 1, Subgroup 2 more often reported sleep problems, limitations in social functioning, and less often have more than two comorbidities. Subgroup 3 more often reported depression, sleep problems, a lower self-esteem, and limitations in social functioning and a lower educational level compared to Subgroup 1. CONCLUSION: Different subgroups of CCS with CF can be identified based on fatigue dimensions physical activity, motivation and concentration. Results suggest that different intervention strategies, tailored for each subgroup, might be beneficial.

Sunitinib for Metastatic Renal Cell Carcinoma: Real-World Data from the STAR-TOR Registry and Detailed Literature Review.

INTRODUCTION: We evaluated the effectiveness and safety profile of the tyrosine kinase inhibitor sunitinib in patients with advanced or metastatic renal cell carcinoma (a/mRCC) in a real-world setting. METHODS: We analyzed data of adult a/mRCC patients treated with sunitinib. Data were derived from the German non-interventional post-approval multicenter STAR-TOR registry (NCT00700258). Progression-free survival (PFS), overall survival (OS), and adverse events (AEs) were evaluated using descriptive statistics and survival analyses for the entire cohort and patient subgroups. RESULTS: A total of 116 study sites recruited 702 patients treated with sunitinib (73.1% male; median age 68.0 years; median Karnofsky index 90%) between November 2010 and May 2020. The most frequent histological subtype was clear cell RCC (81.6%). Sunitinib was administered as first-line treatment in 83.5%, as second line in 11.7%, and as third line or beyond in 4.8% of the patients. Drug-related AEs and serious AEs were reported in 66.3% and 13.9% of the patients, respectively (most common AE: gastrointestinal disorders; 39.7% of all patients). CONCLUSIONS: This study adds further real-world evidence of the persisting relevance of sunitinib for patients with a/mRCC who cannot receive or tolerate immune checkpoint inhibitors. The study population includes a high proportion of patients with unfavorable MSKCC poor-risk score, but shows still good PFS and OS results, while the drug demonstrates a favorable safety profile. The STAR-TOR registry is also registered in the database of US library of medicine (NCT00700258).

Disability reduction following a lumbar stabilization exercise program for low back pain: large vs. small improvement subgroup analyses of physical and psychological variables.

BACKGROUND: Little is known about why patients with low back pain (LBP) respond differently to treatment, and more specifically, to a lumbar stabilization exercise program. As a first step toward answering this question, the present study evaluates how subgroups of patients who demonstrate large and small clinical improvements differ in terms of physical and psychological changes during treatment. METHODS: Participants (n = 110) performed the exercise program (clinical sessions and home exercises) over eight weeks, with 100 retained at six-month follow-up. Physical measures (lumbar segmental instability, motor control impairments, range of motion, trunk muscle endurance and physical performance tests) were collected twice (baseline, end of treatment), while psychological measures (fear-avoidance beliefs, pain catastrophizing, psychological distress, illness perceptions, outcome expectations) were collected at four time points (baseline, mid-treatment, end of treatment, follow-up). The participants were divided into three subgroups (large, moderate and small clinical improvements) based on the change of perceived disability scores. ANOVA for repeated measure compared well-contrasted subgroups (large vs. small improvement) at different times to test for SUBGROUP × TIME interactions. RESULTS: Statistically significant interactions were observed for several physical and psychological measures. In all these interactions, the large- and small-improvement subgroups were equivalent at baseline, but the large-improvement subgroup showed more improvements over time compared to the small-improvement subgroup. For psychological measures only (fear-avoidance beliefs, pain catastrophizing, illness perceptions), between-group differences reached moderate to strong effect sizes, at the end of treatment and follow-up. CONCLUSIONS: The large-improvement subgroup showed more improvement than the small-improvement subgroup with regard to physical factors typically targeted by this specific exercise program as well as for psychological factors that are known to influence clinical outcomes.

Severer air pollution, poorer cognitive function: Findings from 176,345 elders in Northwestern China.

BACKGROUND: Limited evidence exists regarding the link between air pollution exposure and cognitive function in developing countries, particularly in areas with abundant natural sources of particulate matter. OBJECTIVES: To investigate this association in a large representative sample of the elderly in northwestern China. METHODS: We performed a cross-sectional study among 176,345 participants aged 60-100 years in northwestern China in 2020. A satellite-based spatiotemporal model was applied to assess three-year annual averages of particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5), ≤ 10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) at residential address. Poor cognitive function was assessed using the Mini-Mental State Examination (MMSE). Generalized linear mixed models were used to assess associations. RESULTS: Compared with participants with the lowest quartiles of PM2.5, PM10, and O3 levels, those with the second, third, and highest quartiles of air pollutants consistently showed increased odds of poor cognitive function and decreased MMSE scores. The odds ratios of poor cognitive function associated with a 10 µg/m3 increment in PM2.5, PM10, and O3 were 1.26 (95 % confidence interval [CI]: 1.17, 1.36), 1.06 (95 %CI: 1.04, 1.08), and 2.76 (95 %CI: 2.11, 3.62), respectively. Subgroup analyses suggested stronger associations between air pollution exposures and poor cognitive function among participants who were younger, were non-Uyghur and were physically active. CONCLUSION: Long-term exposures to PM2.5, PM10 and O3 were associated with poor cognitive function in elders. Our results suggest that reducing air pollution may alleviate the burden of poor cognitive function in the elderly.

Do patients with cN0 oral squamous cell carcinoma benefit from elective neck dissection? A large-scale population-based study.

BACKGROUND: The neck management of clinical-nodal negative (cN0) oral squamous cell carcinoma (OSCC) remains controversial. Elective neck dissection (END) and observation are the main strategies, but it is still not clear who could benefit the most from END. The purpose of this study was to clarify the potential clinical factors that affect the therapeutic value of END and to explore the actual characteristics associated with benefit from END. METHODS: Patients with cN0 OSCC were identified in the SEER database from 2000 to 2019. 5-year Overall survival (OS) and disease-specific survival (DSS) were analyzed using the Kaplan‒Meier method, and the hazard ratios (HRs) for survival were estimated using the Cox regression model. Multiple subgroup analyses of DSS and OS among different factors, comparing END and No END, were performed. RESULTS: A total of 17,019 patients with cN0 OSCC were included. The basic survival analysis and Cox regression model showed that END increased the probability of 5-year DSS and OS and was an independent prognostic factor. However, among patients who underwent only primary tumor surgery, no significant differences were found between the END and No END groups in 5-year DSS (P = 0. 585) and OS (P = 0.465). Further subgroup analysis showed that primary sites and T stage, but not other factors, might influence the benefit of END. Significant differences were found for T1 (P < 0.001 for OS) and T2 (P = 0.001 for DSS and < 0.001 for OS) tongue squamous cell carcinoma (TSCC) but not for other primary tumor sites. CONCLUSION: This large-scale retrospective population-based cohort study suggests that not all patients with cN0 OSCC could benefit from END. Patients with cN0 TSCC are recommended to undergo END, especially with early-stage tumors.

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.

Considerations for Subgroup Analyses in Cluster-Randomized Trials Based on Aggregated Individual-Level Predictors.

In research assessing the effect of an intervention or exposure, a key secondary objective often involves assessing differential effects of this intervention or exposure in subgroups of interest; this is often referred to as assessing effect modification or heterogeneity of treatment effects (HTE). Observed HTE can have important implications for policy, including intervention strategies (e.g., will some patients benefit more from intervention than others?) and prioritizing resources (e.g., to reduce observed health disparities). Analysis of HTE is well understood in studies where the independent unit is an individual. In contrast, in studies where the independent unit is a cluster (e.g., a hospital or school) and a cluster-level outcome is used in the analysis, it is less well understood how to proceed if the HTE analysis of interest involves an individual-level characteristic (e.g., self-reported race) that must be aggregated at the cluster level. Through simulations, we show that only individual-level models have power to detect HTE by individual-level variables; if outcomes must be defined at the cluster level, then there is often low power to detect HTE by the corresponding aggregated variables. We illustrate the challenges inherent to this type of analysis in a study assessing the effect of an intervention on increasing COVID-19 booster vaccination rates at long-term care centers.

LPP polymorphisms are risk factors for allergic rhinitis in the Chinese Han population.

BACKGROUND: Lipoma preferred partner (LPP) polymorphisms are related to immune diseases, but the role of LPP gene in the pathogenesis of allergic rhinitis (AR) is unclear. The current study aimed to explore the contribution of LPP variants to AR susceptibility in the Chinese Han population. METHODS: A total of 992 healthy controls and 992 patients with AR were recruited. Agena MassARRAY system was applied for genotyping. Odds ratios (OR) and 95% confidence intervals (CI) adjusted by age, sex, and body mass index (BMI) were calculated to conduct the risk assessment of LPP variants in people with a predisposition to AR. Additionally, multifactor dimensionality reduction (MDR) was applied to identify high-order interaction models for AR risk. RESULTS: We found that rs2030519-G (p = 0.027, OR: 1.15, 95% CI: 1.02-1.31), rs6780858-G (p = 0.019, OR: 1.16, 95% CI: 1.03-1.32), and rs60946162-T (p = 0.014, OR: 1.18, 95% CI: 1.03-1.34) were associated with increased susceptibility to AR. Subgroup analyses indicated the interaction of LPP polymorphisms in terms of age, gender, and BMI with AR susceptibility (p < 0.05, OR > 1). MDR analysis revealed that rs60946162 had the information gain (0.40%) of individual attribute regarding AR. CONCLUSION: Our results first determined that rs2030519, rs6780858, and rs60946162 were correlated with increased susceptibility to AR in the Chinese Han population, which add to our understanding of the impact of LPP gene variants on AR development.

Neoadjuvant versus definitive radiochemotherapy of locoregionally advanced oesophageal cancer-who benefits?

PURPOSE: For years, there have been discussions on whether neoadjuvant radiochemotherapy followed by surgery (nRCT-S) is superior to definitive radiochemotherapy (dRCT) as the standard of care for locoregionally advanced oesophageal cancer (OC). This retrospective study aimed to evaluate our patient cohort regarding differences in survival and recurrence between nRCT­S and dRCT. METHODS: Data from 68 patients with dRCT and 33 patients with nRCT­S treated from 2010 to 2018 were analysed. Comorbidities were recorded using the Charlson Comorbidity Index (CCI). Recurrence patterns were recorded as in-field or out-field. Kaplan-Meier analyses were used to compare survival data (overall survival [OS], progression-free survival [PFS], and locoregional control [LRC]). RESULTS: Patients with nRCT­S showed significantly lower CCI values than those with dRCT (p = 0.001). The median follow-up was 47 months. The median OS times were 31 months for nRCT­S and 12 months for dRCT (p = 0.009), the median PFS times were 11 and 9 months, respectively (p = 0.057), and the median LRC times were not reached and 23 months, respectively (p = 0.037). The only further factor with a significant impact on OS was the CCI (p = 0.016). In subgroup analyses for comorbidities regarding differences in OS, the superiority of the nRCT­S remained almost significant for CCI values 2-6 (p = 0.061). CONCLUSION: Our study showed significantly longer OS and LRC for patients with nRCT­S than for those with dRCT. Due to different comorbidities in the groups, it can be deduced from the subgroup analysis that patients with few comorbidities seem to especially profit from nRCT­S.

Transporting Subgroup Analyses of Randomized Controlled Trials for Planning Implementation of New Interventions.

Subgroup analyses of randomized controlled trials guide resource allocation and implementation of new interventions by identifying groups of individuals who are likely to benefit most from the intervention. Unfortunately, trial populations are rarely representative of the target populations of public health or clinical interest. Unless the relevant differences between trial and target populations are accounted for, subgroup results from trials might not reflect which groups in the target population will benefit most from the intervention. Transportability provides a rigorous framework for applying results derived in potentially highly selected study populations to external target populations. The method requires that researchers measure and adjust for all variables that 1) modify the effect of interest and 2) differ between the target and trial populations. To date, applications of transportability have focused on the external validity of overall study results and understanding within-trial heterogeneity; however, this approach has not yet been used for subgroup analyses of trials. Through an example from the Iniciativa Profilaxis Pre-Exposición (iPrEx) study (multiple countries, 2007-2010) of preexposure prophylaxis for human immunodeficiency virus, we illustrate how transporting subgroup analyses can produce target-specific subgroup effect estimates and numbers needed to treat. This approach could lead to more tailored and accurate guidance for resource allocation and cost-effectiveness analyses.

Efficacy and safety of oral semaglutide in Japanese patients with type 2 diabetes: A post hoc subgroup analysis of the PIONEER 1, 3, 4 and 8 trials.

AIMS: To evaluate, through exploratory post hoc subgroup analyses, the efficacy and safety of oral semaglutide versus comparators in Japanese patients enrolled in the global PIONEER 1, 3, 4 and 8 clinical trials. MATERIALS AND METHODS: Patients were randomized to once-daily oral semaglutide 3, 7 or 14 mg or comparator (placebo, sitagliptin 100 mg or liraglutide 1.8 mg). Change from baseline in glycated haemoglobin (HbA1c) and body weight, and proportions of patients attaining HbA1c <7.0% (53 mmol/mol) and body weight loss ≥5%, were analysed at week 26 for all Japanese patients in each trial separately using the treatment policy estimand (regardless of treatment discontinuation or rescue medication use). Adverse events (AEs) were analysed descriptively. RESULTS: Reductions in HbA1c from baseline in Japanese patients were 1.0% to 1.2% (11.3 mmol/mol to 13.3 mmol/mol) and 1.4% to 1.7% (15.7 mmol/mol to 18.3 mmol/mol) for oral semaglutide 7 mg and 14 mg, respectively. HbA1c reductions were similar or greater than with comparators. Body weight reductions were 1.0% to 2.7% and 3.7% to 4.7% for oral semaglutide 7 mg and 14 mg, respectively, and were generally greater with oral semaglutide than comparators. As expected, the main class of AEs was gastrointestinal, and these AEs comprised most commonly mild-to-moderate constipation, nausea and diarrhoea. CONCLUSIONS: Oral semaglutide appears efficacious and well tolerated in Japanese patients across the type 2 diabetes spectrum.

A systematic review of subgroup analyses in randomised clinical trials in cardiovascular disease.

BACKGROUND: Subgroup analyses are frequently used to assess heterogeneity of treatment effects in randomised clinical trials. Inconsistent, improper and incomplete implementation, reporting and interpretation have been identified as ongoing challenges. Further, subgroup analyses were frequently criticised because of unreliable or potentially misleading results. More recently, recommendations and guidelines have been provided to improve the reporting of data in this regard. METHODS: This systematic review was based on a literature search within the digital archives of three selected medical journals, The New England Journal of Medicine, The Lancet and Circulation. We reviewed articles of randomised clinical trials in the domain of cardiovascular disease which were published in 2015 and 2016. We screened and evaluated the selected articles for the mode of implementation and reporting of subgroup analyses. RESULTS: We were able to identify a total of 130 eligible publications of randomised clinical trials. In 89/130 (68%) articles, results of at least one subgroup analysis were presented. This was dependent on the considered journal (p < 0.001), the number of included patients (p < 0.001) and the lack of statistical significance of a trial's primary analysis (p < 0.001). The number of reported subgroup analyses ranged from 1 to 101 (median = 13). We were able to comprehend the specification time of reported subgroup analyses for 71/89 (80%) articles, with 55/89 (62%) articles presenting exclusively pre-specified analyses. This information was not always traceable on the basis of provided trial protocols and often did not include the pre-definition of cut-off values for the categorization of subgroups. The use of interaction tests was reported in 84/89 (94%) articles, with 36/89 (40%) articles reporting heterogeneity of the treatment effect for at least one primary or secondary trial outcome. Subgroup analyses were reported more frequently for larger randomised clinical trials, and if primary analyses did not reach statistical significance. Information about the implementation of subgroup analyses was reported most consistently for articles from The New England Journal of Medicine, since it was also traceable on the basis of provided trial protocols. We were able to comprehend whether subgroup analyses were pre-specified in a majority of the reviewed publications. Even though results of multiple subgroup analyses were reported for most published trials, a corresponding adjustment for multiple testing was rarely considered. CONCLUSION: Compared to previous reviews in this context, we observed improvements in the reporting of subgroup analyses of cardiovascular randomised clinical trials. Nonetheless, critical shortcomings, such as inconsistent reporting of the implementation and insufficient pre-specification, persist.

Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach.

BACKGROUND: Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials. METHODS: We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate-subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups. RESULTS: Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. CONCLUSION: Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate-subgroup interactions in the propensity score model.

Determinants of response to inhaled extrafine triple therapy in asthma: analyses of TRIMARAN and TRIGGER.

BACKGROUND: A number of single-inhaler triple therapies are being developed for asthma, including the extrafine formulation of beclometasone dipropionate (BDP), formoterol fumarate (FF), and glycopyrronium (G). Given asthma is a heterogenous disease, we investigated whether the clinical response to the addition of the long-acting muscarinic antagonist component within inhaled triple therapy was impacted by a range of clinical characteristics. METHODS: These were pre-specified and post-hoc sub-group analyses of TRIMARAN and TRIGGER, which were double-blind, 52-week studies comparing medium-strength (100/6/10 µg; TRIMARAN) and high-strength (200/6/10 µg; TRIGGER) BDP/FF/G with the respective BDP/FF strengths in adults with uncontrolled asthma and a history of ≥ 1 exacerbation. Co-primary endpoints were pre-dose forced expiratory volume in 1 s (FEV1) at Week 26 and the rate of moderate-to-severe exacerbations over 52 weeks. Key secondary endpoints: peak FEV1 at Week 26 and average morning peak expiratory flow over the first 26 weeks in each study, and severe exacerbation rate over 52 weeks (pooled data). RESULTS: Baseline clinical characteristics (pre-specified analyses) had no consistent effect on the lung function improvements with BDP/FF/G. For the exacerbation endpoints, sub-groups with higher reversibility gained greatest relative benefit from BDP/FF/G versus BDP/FF. In post-hoc analyses with patients sub-grouped by screening blood eosinophil values, in TRIMARAN the greatest relative effect of BDP/FF/G versus BDP/FF on the lung function endpoints was in the ≤ 300 cells/µL group; in TRIGGER, eosinophil levels did not markedly influence the relative efficacy of BDP/FF/G versus BDP/FF. Eosinophil levels did not influence relative efficacy on moderate-to-severe or severe exacerbations. CONCLUSION: Overall, the relative efficacy of extrafine BDP/FF/G versus BDP/FF was not influenced by a range of clinical characteristics. However, some patient sub-groups gained additional benefit from BDP/FF/G for certain endpoints. In particular, for exacerbations the relative efficacy of BDP/FF/G was greater in more reversible patients. Trial registration ClinicalTrials.gov: TRIMARAN, NCT02676076 (registered February 8, 2016, https://clinicaltrials.gov/ct2/show/NCT02676076?term=NCT02676076&draw=2&rank=1 ,); TRIGGER, NCT02676089 (registered February 8, 2016, https://clinicaltrials.gov/ct2/show/NCT02676089?term=NCT02676089&draw=2&rank=1 ).

Integrating expert opinions with clinical trial data to analyse low-powered subgroup analyses: a Bayesian analysis of the VeRDiCT trial.

BACKGROUND: Typically, subgroup analyses in clinical trials are conducted by comparing the intervention effect in each subgroup by means of an interaction test. However, trials are rarely, if ever, adequately powered for interaction tests, so clinically important interactions may go undetected. We discuss the application of Bayesian methods by using expert opinions alongside the trial data. We applied this methodology to the VeRDiCT trial investigating the effect of preoperative volume replacement therapy (VRT) versus no VRT (usual care) in diabetic patients undergoing cardiac surgery. Two subgroup effects were of clinical interest, a) preoperative renal failure and b) preoperative type of antidiabetic medication. METHODS: Clinical experts were identified within the VeRDiCT trial centre in the UK. A questionnaire was designed to elicit opinions on the impact of VRT on the primary outcome of time from surgery until medically fit for hospital discharge, in the different subgroups. Prior beliefs of the subgroup effect of VRT were elicited face-to-face using two unconditional and one conditional questions per subgroup analysis. The robustness of results to the 'community of priors' was assessed. The community of priors was built using the expert priors for the mean average treatment effect, the interaction effect or both in a Bayesian Cox proportional hazards model implemented in the STAN software in R. RESULTS: Expert opinions were obtained from 7 clinicians (6 cardiac surgeons and 1 cardiac anaesthetist). Participating experts believed VRT could reduce the length of recovery compared to usual care and the greatest benefit was expected in the subgroups with the more severe comorbidity. The Bayesian posterior estimates were more precise compared to the frequentist maximum likelihood estimate and were shifted toward the overall mean treatment effect. CONCLUSIONS: In the VeRDiCT trial, the Bayesian analysis did not provide evidence of a difference in treatment effect across subgroups. However, this approach increased the precision of the estimated subgroup effects and produced more stable treatment effect point estimates than the frequentist approach. Trial methodologists are encouraged to prospectively consider Bayesian subgroup analyses when low-powered interaction tests are planned. TRIAL REGISTRATION: ISRCTN, ISRCTN02159606 . Registered 29th October 2008.

Comparison of propensity score methods for pre-specified subgroup analysis with survival data.

Examining medical products' benefits and risks in different population subsets is often necessary for informing public health decisions. In observational cohort studies, safety analyses by pre-specified subgroup can be powered, and are informative about different population subsets' risks if the study designs or analyses adequately control for confounding. However, few guidelines exist on how to simultaneously control for confounding and conduct subgroup analyses. In this simulation study, we evaluated the performance, in terms of bias, efficiency and coverage, of six propensity score methods in 24 scenarios by estimating subgroup-specific hazard ratios of average treatment effect in the treated with Cox regression models. The subgroup analysis methods control for confounding either by propensity score matching or by inverse probability treatment weighting. These methods vary as to whether they subset information or borrow it across subgroups to estimate the propensity score. Simulation scenarios varied by size of subgroup, strength of association of subgroup with exposure, strength of association of subgroup with outcome (simulated survival), and outcome incidence. Results indicated that subsetting the data by the subgrouping variable, to estimate the propensity score and hazard ratio, has the smallest bias, far exceeding any penalty in precision. Moreover, weighting methods pay a heavier price in bias than do matching methods when the propensity score model is misspecified and the subgrouping variable is a strong confounder.

A multiple comparison procedure for dose-finding trials with subpopulations.

Identifying subgroups of patients with an enhanced response to a new treatment has become an area of increased interest in the last few years. When there is knowledge about possible subpopulations with an enhanced treatment effect before the start of a trial it might be beneficial to set up a testing strategy, which tests for a significant treatment effect not only in the full population, but also in these prespecified subpopulations. In this paper, we present a parametric multiple testing approach for tests in multiple populations for dose-finding trials. Our approach is based on the MCP-Mod methodology, which uses multiple comparison procedures (MCPs) to test for a dose-response signal, while considering multiple possible candidate dose-response shapes. Our proposed methods allow for heteroscedastic error variances between populations and control the family-wise error rate over tests in multiple populations and for multiple candidate models. We show in simulations that the proposed multipopulation testing approaches can increase the power to detect a significant dose-response signal over the standard single-population MCP-Mod, when the specified subpopulation has an enhanced treatment effect.

Age-treatment subgroup analyses in Cochrane intervention reviews: a meta-epidemiological study.

BACKGROUND: There is growing interest in evaluating differences in healthcare interventions across routinely collected demographic characteristics. However, individual subgroup analyses in randomized controlled trials are often not prespecified, adjusted for multiple testing, or conducted using the appropriate statistical test for interaction, and therefore frequently lack credibility. Meta-analyses can be used to examine the validity of potential subgroup differences by collating evidence across trials. Here, we characterize the conduct and clinical translation of age-treatment subgroup analyses in Cochrane reviews. METHODS: For a random sample of 928 Cochrane intervention reviews of randomized trials, we determined how often subgroup analyses of age are reported, how often these analyses have a P < 0.05 from formal interaction testing, how frequently subgroup differences first observed in an individual trial are later corroborated by other trials in the same meta-analysis, and how often statistically significant results are included in commonly used clinical management resources (BMJ Best Practice, UpToDate, Cochrane Clinical Answers, Google Scholar, and Google search). RESULTS: Among 928 Cochrane intervention reviews, 189 (20.4%) included plans to conduct age-treatment subgroup analyses. The vast majority (162 of 189, 85.7%) of the planned analyses were not conducted, commonly because of insufficient trial data. There were 22 reviews that conducted their planned age-treatment subgroup analyses, and another 3 reviews appeared to perform unplanned age-treatment subgroup analyses. These 25 (25 of 928, 2.7%) reviews conducted a total of 97 age-treatment subgroup analyses, of which 65 analyses (in 20 reviews) had non-overlapping subgroup levels. Among the 65 age-treatment subgroup analyses, 14 (21.5%) did not report any formal interaction testing. Seven (10.8%) reported P < 0.05 from formal age-treatment interaction testing; however, none of these seven analyses were in reviews that discussed the potential biological rationale or clinical significance of the subgroup findings or had results that were included in common clinical practice resources. CONCLUSION: Age-treatment subgroup analyses in Cochrane intervention reviews were frequently planned but rarely conducted, and implications of detected interactions were not discussed in the reviews or mentioned in common clinical resources. When subgroup analyses are performed, authors should report the findings, compare the results to previous studies, and outline any potential impact on clinical care.

A review of the performance of different methods for propensity score matched subgroup analyses and a summary of their application in peer-reviewed research studies.

PURPOSE: When evaluating safety signals, there is often interest in understanding safety in all patients for whom compared treatments are reasonable alternatives, as well as in specific subgroups of interest. There are numerous ways that propensity score (PS) matching can be implemented for subgroup analyses. METHODS: We conducted a systematic literature review of methods papers that compared the performance of alternative methods to implement PS matched subgroup analyses and examined how frequently different PS matching methods have been used for subgroup analyses in applied studies. RESULTS: We identified 5 methods papers reporting small improvements in covariate balance and bias with use of a subgroup-specific PS instead of a mis-specified overall PS within subgroups. Applied research papers frequently used PS for subgroups in ways not evaluated in methods papers. Thirty three percent used PS to match in the overall cohort and broke the matched sets for subgroup analysis without further adjustment. CONCLUSIONS: While the performance of several alternative ways to use PS matching in subgroup analyses has been evaluated in methods literature, these evaluations do not include the most commonly used methods to implement PS matched subgroup analyses in applied studies. There is a need to better understand the relative performance of commonly used methods for PS matching in subgroup analyses, particularly within settings encountered during active surveillance, where there may be low exposure, infrequent outcomes, and multiple subgroups of interest.

Gabapentin in procedure-specific postoperative pain management - preplanned subgroup analyses from a systematic review with meta-analyses and trial sequential analyses.

BACKGROUND: It has been argued that postoperative pain treatment should be "procedure-specific", since different analgesics may have specific effects dependent on the surgical procedure. The aim of the present subgroup analysis was to compare the beneficial and harmful effects of perioperative gabapentin treatment in different surgical procedures. METHODS: Relevant databases were searched for randomized clinical trials (RCTs) comparing gabapentin versus placebo. Two authors independently screened titles and abstracts, extracted data and assessed risk of bias. The primary outcomes were differences in 24-h morphine consumption, and serious adverse events (SAE) between surgical procedures. These subgroup analyses were predefined in a PRISMA compliant systematic review registered at PROSPERO (ID: CRD42013006538). It was predefined that conclusions should primarily be based on trials classified as overall low risk of bias. RESULTS: Seventy-four RCTs with 5645 patients were included, assessing benefit and harm in cholecystectomy, hysterectomy, mastectomy, and arthroplasty surgery, spinal surgery, and thoracic surgery. Only eight of 74 trials were classified as overall low risk of bias limiting our ability to conclude on the estimates in most meta-analyses. The differences between surgical procedures in these trials were not statistically significant when tested for subgroup differences. Fifteen trials with 1377 patients reported a total of 59 SAEs, most of which were observed in the thoracic surgery group. CONCLUSION: Both beneficial and harmful effects in these subgroup analyses were influenced by bias and insufficient data, limiting conclusions. With these limitations, we could not adequately test for differences in beneficial or harmful outcomes between six surgical subgroups undergoing perioperative gabapentin treatment.