Statistical methods in recent HIV noninferiority trials: reanalysis of 11 trials.
Ontology highlight
ABSTRACT:
SUBMITTER: Flandre P
PROVIDER: S-EPMC3168436 | biostudies-literature | 2011
REPOSITORIES: biostudies-literature
Publications
Statistical methods in recent HIV noninferiority trials: reanalysis of 11 trials.
PloS one 20110907 9
<h4>Background</h4>In recent years the "noninferiority" trial has emerged as the new standard design for HIV drug development among antiretroviral patients often with a primary endpoint based on the difference in success rates between the two treatment groups. Different statistical methods have been introduced to provide confidence intervals for that difference. The main objective is to investigate whether the choice of the statistical method changes the conclusion of the trials.<h4>Methods</h4> ...[more]
Similar Datasets
Project description:BackgroundRestricted mean survival time methods compare the areas under the Kaplan-Meier curves up to a time τ for the control and experimental treatments. Extraordinary claims have been made about the benefits (in terms of dramatically smaller required sample sizes) when using restricted mean survival time methods as compared to proportional hazards methods for analyzing noninferiority trials, even when the true survival distributions satisfy proportional hazardss.MethodsThrough some limited simulations and asymptotic power calculations, the authors compare the operating characteristics of restricted mean survival time and proportional hazards methods for analyzing both noninferiority and superiority trials under proportional hazardss to understand what relative power benefits there are when using restricted mean survival time methods for noninferiority testing.ResultsIn the setting of low-event rates, very large targeted noninferiority margins, and limited follow-up past τ, restricted mean survival time methods have more power than proportional hazards methods. For superiority testing, proportional hazards methods have more power. This is not a small-sample phenomenon but requires a low-event rate and a large noninferiority margin.ConclusionAlthough there are special settings where restricted mean survival time methods have a power advantage over proportional hazards methods for testing noninferiority, the larger issue in these settings is defining appropriate noninferiority margins. We find the restricted mean survival time methods lacking in these regards.
| S-EPMC8329935 | biostudies-literature
Project description:Biomarker endpoints measuring vaccine-induced immune responses are essential to HIV vaccine development because of their potential to predict the effect of a vaccine in preventing HIV infection. A vaccine's immune response profile observed in phase I immunogenicity studies is a key factor in determining whether it is advanced for further study in phase II and III efficacy trials. The multiplicity of immune variables and scientific uncertainty in their relative importance, however, pose great challenges to the development of formal algorithms for selecting vaccines to study further. Motivated by the practical need to identify a set of promising vaccines from a pool of candidate regimens for inclusion in an upcoming HIV vaccine efficacy trial, we propose a new statistical framework for the selection of vaccine regimens based on their immune response profile. In particular, we propose superiority and non-redundancy criteria to be achieved in down-selection, and develop novel statistical algorithms that integrate hypothesis testing and ranking for selecting vaccine regimens satisfying these criteria. Performance of the proposed selection algorithms are evaluated through extensive numerical studies. We demonstrate the application of the proposed methods through the comparison of immune responses between several HIV vaccine regimens. The methods are applicable to general down-selection applications in clinical trials.
| S-EPMC6092735 | biostudies-literature
Project description:BACKGROUND:Partial factorial trials compare two or more pairs of treatments on overlapping patient groups, randomising some (but not all) patients to more than one comparison. The aims of this research were to compare different methods for conducting and analysing economic evaluations on partial factorial trials and assess the implications of considering factors simultaneously rather than drawing independent conclusions about each comparison. METHODS:We estimated total costs and quality-adjusted life years (QALYs) within 10 years of surgery for 2252 patients in the Knee Arthroplasty Trial who were randomised to one or more comparisons of different surgical types. We compared three analytical methods: an "at-the-margins" analysis including all patients randomised to each comparison (assuming no interaction); an "inside-the-table" analysis that included interactions but focused on those patients randomised to two comparisons; and a Bayesian vetted bootstrap, which used results from patients randomised to one comparison as priors when estimating outcomes for patients randomised to two comparisons. Outcomes comprised incremental costs, QALYs and net benefits. RESULTS:Qualitative interactions were observed for costs, QALYs and net benefits. Bayesian bootstrapping generally produced smaller standard errors than inside-the-table analysis and gave conclusions that were consistent with at-the-margins analysis, while allowing for these interactions. By contrast, inside-the-table gave different conclusions about which intervention had the highest net benefits compared with other analyses. CONCLUSIONS:All analyses of partial factorial trials should explore interactions and assess whether results are sensitive to assumptions about interactions, either as a primary analysis or as a sensitivity analysis. For partial factorial trials closely mirroring routine clinical practice, at-the-margins analysis may provide a reasonable estimate of average costs and benefits for the whole trial population, even in the presence of interactions. However, such conclusions will be misleading if there are large interactions or if the proportion of patients allocated to different treatments differs markedly from what occurs in clinical practice. The Bayesian bootstrap provides an alternative to at-the-margins analysis for analysing clinical or economic endpoints from partial factorial trials, which allows for interactions while making use of the whole sample. The same techniques could be applied to analyses of clinical endpoints. TRIAL REGISTRATION:ISRCTN, ISRCTN45837371 . Registered on 25 April 2003.
| S-EPMC6097309 | biostudies-literature
Project description:BackgroundNoninferiority trials are increasingly being performed. However, little is known about their methodological quality. We sought to characterize noninferiority cardiovascular trials published in the highest-impact journals, features that may bias results toward noninferiority, features related to reporting of noninferiority trials, and the time trends.MethodsWe identified cardiovascular noninferiority trials published in JAMA, Lancet, or New England Journal of Medicine from 1990 to 2016. Two independent reviewers extracted the data. Data elements included the noninferiority margin and the success of studies in achieving noninferiority. The proportion of trials showing major or minor features that may have affected the noninferiority inference was determined. Major factors included the lack of presenting the results in both intention-to-treat and per-protocol/as-treated cohorts, α>0.05, the new intervention not being compared with the best alternative, not justifying the noninferiority margin, and exclusion or loss of ≥10% of the cohort. Minor factors included suboptimal blinding, allocation concealment, and others.ResultsFrom 2544 screened studies, we identified 111 noninferiority cardiovascular trials. Noninferiority margins varied widely: risk differences of 0.4% to 25%, hazard ratios of 1.05 to 2.85, odds ratios of 1.1 to 2.0, and relative risks of 1.1 to 1.8. Eighty-six trials claimed noninferiority, of which 20 showed superiority, whereas 23 (21.1%) did not show noninferiority, of which 8 also demonstrated inferiority. Only 7 (6.3%) trials were considered low risk for all the major and minor biasing factors. Among common major factors for bias, 41 (37%) did not confirm the findings in both intention-to-treat and per-protocol/as-treated cohorts and 4 (3.6%) reported discrepant results between intention-to-treat and per-protocol analyses. Forty-three (38.7%) did not justify the noninferiority margin. Overall, 27 (24.3%) underenrolled or had >10% exclusions. Sixty trials (54.0%) were open label. Allocation concealment was not maintained or unclear in 11 (9.9%). Publication of noninferiority trials increased over time (P<0.001). Fifty-two (46.8%) were published after 2010 and had a lower risk of methodological or reporting limitations for major (P=0.03) and minor factors (P=0.002).ConclusionsNoninferiority trials in highest-impact journals commonly conclude noninferiority of the tested intervention, but vary markedly in the selected noninferiority margin, and frequently have limitations that may impact the inference related to noninferiority.
| S-EPMC8416048 | biostudies-literature
Project description:BackgroundNoninferiority trials are increasingly common in cardiovascular medicine, but their reporting and interpretation are challenging, particularly when an absolute risk difference is used as noninferiority margin.ObjectivesThis study aimed to investigate the effect of using absolute rather than relative noninferiority margins in cardiovascular trials.MethodsWe reviewed noninferiority trials presented at major cardiovascular conferences from 2015 to 2022 and published within the same period. Based on the actual versus anticipated event rates in the control group, we recalculated the absolute noninferiority margin and re-assessed the trial results. The primary outcome of interest was the proportion of trials with a different interpretation after recalculation. Additionally, we analyzed the conclusion statements of these trials to determine if cautionary notes for the interpretation of study results were included.ResultsWe analyzed a total of 768 trials, of which 88 had a noninferiority design and 66 used an absolute noninferiority margin. Of 48 comparisons from 45 trials qualifying for the analysis, 11 (22.9%) had divergent results after recalculation of the absolute noninferiority margin based on the observed rather than anticipated event rate. Ten trials originally claiming noninferiority, did not meet it after the margin recalculation. All of them did not include statements suggesting cautionary interpretation of the study results in the conclusion section. Compared with the other trials, these displayed a larger median difference between anticipated and recalculated noninferiority margins (44.7% [IQR: 38.6%-56.7%] vs 15.3% [IQR: -1.5% to 28.9%]; P < 0.001).ConclusionsRecalculating noninferiority margins based on actual event rates, rather than anticipated ones, led to different outcomes in approximately 1 out of 4 cardiovascular trials, with most divergent trials lacking cautionary interpretation. These findings emphasize the importance of using or supplementing the relative noninferiority margin, particularly in studies with significant deviations between observed and expected event rates. This underscores the critical need for enhanced methodological and reporting standards in noninferiority trials, especially those employing absolute margins.
| S-EPMC11312784 | biostudies-literature
Project description:BACKGROUND:Drug safety assessments in clinical trials present unique analytical challenges. Some of these include adjusting for individual follow-up time, repeated measurements of multiple outcomes and missing data among others. Furthermore, pre-specifying appropriate analysis becomes difficult as some safety endpoints are unexpected. Although existing guidelines such as CONSORT encourage thorough reporting of adverse events (AEs) in clinical trials, they provide limited details for safety data analysis. The limited guidelines may influence suboptimal analysis by failing to account for some analysis challenges above. A typical example where such challenges exist are trials of anti-malarial drugs for malaria prevention during pregnancy. Lack of proper standardized evaluation of the safety of antimalarial drugs has limited the ability to draw conclusions about safety. Therefore, a systematic review was conducted to establish the current practice in statistical analysis for preventive antimalarial drug safety in pregnancy. METHODS:The search included five databases (PubMed, Embase, Scopus, Malaria in Pregnancy Library and Cochrane Central Register of Controlled Trials) to identify original English articles reporting Phase III randomized controlled trials (RCTs) on anti-malarial drugs for malaria prevention in pregnancy published from January 2010 to July 2019. RESULTS:Eighteen trials were included in this review that collected multiple longitudinal safety outcomes including AEs. Statistical analysis and reporting of the safety outcomes in all the trials used descriptive statistics; proportions/counts (n?=?18, 100%) and mean/median (n?=?2, 11.1%). Results presentation included tabular (n?=?16, 88.9%) and text description (n?=?2, 11.1%). Univariate inferential methods were reported in most trials (n?=?16, 88.9%); including Chi square/Fisher's exact test (n?=?12, 66.7%), t test (n?=?2, 11.1%) and Mann-Whitney/Wilcoxon test (n?=?1, 5.6%). Multivariable methods, including Poisson and negative binomial were reported in few trials (n?=?3, 16.7%). Assessment of a potential link between missing efficacy data and safety outcomes was not reported in any of the trials that reported efficacy missing data (n?=?7, 38.9%). CONCLUSION:The review demonstrated that statistical analysis of safety data in anti-malarial drugs for malarial chemoprevention in pregnancy RCTs is inadequate. The analyses insufficiently account for multiple safety outcomes potential dependence, follow-up time and informative missing data which can compromise anti-malarial drug safety evidence development, based on the available data.
| S-EPMC7085184 | biostudies-literature
Project description:In the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case-dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non-additive effects without making any assumptions at the outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs.
| S-EPMC10784464 | biostudies-literature
Project description:BackgroundIn the randomized study of interferon beta-1b (IFN beta-1b) for multiple sclerosis (MS), it has usually been evaluated the simple annual relapse rate as the study endpoint. This study aimed to investigate the performance of various regression models using information regarding the time to each recurrent event and considering the MS specific data generation process, and to estimate the treatment effect of a MS clinical trial data.MethodsWe conducted a simulation study with consideration of the pathological characteristics of MS, and applied alternative efficacy estimation methods to real clinical trial data, including 5 extended Cox regression models for time-to-event analysis, a Poisson regression model and a Poisson regression model with Generalized Estimating Equations (GEE). We adjusted for other important covariates that may have affected the outcome.ResultsWe compared the simulation results for each model. The hazard ratios of real data were estimated for each model including the effects of other covariates. The results (hazard ratios of high-dose to low-dose) of all models were approximately 0.7 (range, 0.613 - 0.769), whereas the annual relapse rate ratio was 0.714.ConclusionsThe precision of the treatment estimation was increased by application of the alternative models. This suggests that the use of alternative models that include recurrence event data may provide better analyses.
| S-EPMC3118202 | biostudies-literature
Project description:BackgroundIn non-inferiority trials with non-adherence to interventions (or non-compliance), intention-to-treat and per-protocol analyses are often performed; however, non-random non-adherence generally biases these estimates of efficacy.ObjectiveTo identify statistical methods that adjust for the impact of non-adherence and thus estimate the causal effects of experimental interventions in non-inferiority trials.DesignA systematic review was conducted by searching the Ovid MEDLINE database (31 December 2020) to identify (1) randomised trials with a primary analysis for non-inferiority that applied (or planned to apply) statistical methods to account for the impact of non-adherence to interventions, and (2) methodology papers that described such statistical methods and included a non-inferiority trial application.OutcomesThe statistical methods identified, their impacts on non-inferiority conclusions, and their advantages/disadvantages.ResultsA total of 24 papers were included (4 protocols, 13 results papers and 7 methodology papers) reporting relevant methods on 26 occasions. The most common were instrumental variable approaches (n=9), including observed adherence as a covariate within a regression model (n=3), and modelling adherence as a time-varying covariate in a time-to-event analysis (n=3). Other methods included rank preserving structural failure time models and inverse-probability-of-treatment weighting. The methods identified in protocols and results papers were more commonly specified as sensitivity analyses (n=13) than primary analyses (n=3). Twelve results papers included an alternative analysis of the same outcome; conclusions regarding non-inferiority were in agreement on six occasions and could not be compared on six occasions (different measures of effect or results not provided in full).ConclusionsAvailable statistical methods which attempt to account for the impact of non-adherence to interventions were used infrequently. Therefore, firm inferences about their influence on non-inferiority conclusions could not be drawn. Since intention-to-treat and per-protocol analyses do not guarantee unbiased conclusions regarding non-inferiority, the methods identified should be considered for use in sensitivity analyses.Prospero registration numberCRD42020177458.
| S-EPMC8756274 | biostudies-literature
Project description:BACKGROUND:In individually randomised trials we might expect interventions delivered in groups or by care providers to result in clustering of outcomes for participants treated in the same group or by the same care provider. In partially nested randomised controlled trials (pnRCTs) this clustering only occurs in one trial arm, commonly the intervention arm. It is important to measure and account for between-cluster variability in trial design and analysis. We compare analysis approaches for pnRCTs with continuous outcomes, investigating the impact on statistical inference of cluster sizes, coding of the non-clustered arm, intracluster correlation coefficient (ICCs), and differential variance between intervention and control arm, and provide recommendations for analysis. METHODS:We performed a simulation study assessing the performance of six analysis approaches for a two-arm pnRCT with a continuous outcome. These include: linear regression model; fully clustered mixed-effects model with singleton clusters in control arm; fully clustered mixed-effects model with one large cluster in control arm; fully clustered mixed-effects model with pseudo clusters in control arm; partially nested homoscedastic mixed effects model, and partially nested heteroscedastic mixed effects model. We varied the cluster size, number of clusters, ICC, and individual variance between the two trial arms. RESULTS:All models provided unbiased intervention effect estimates. In the partially nested mixed-effects models, methods for classifying the non-clustered control arm had negligible impact. Failure to account for even small ICCs resulted in inflated Type I error rates and over-coverage of confidence intervals. Fully clustered mixed effects models provided poor control of the Type I error rates and biased ICC estimates. The heteroscedastic partially nested mixed-effects model maintained relatively good control of Type I error rates, unbiased ICC estimation, and did not noticeably reduce power even with homoscedastic individual variances across arms. CONCLUSIONS:In general, we recommend the use of a heteroscedastic partially nested mixed-effects model, which models the clustering in only one arm, for continuous outcomes similar to those generated under the scenarios of our simulations study. However, with few clusters (3-6), small cluster sizes (5-10), and small ICC (?0.05) this model underestimates Type I error rates and there is no optimal model.
| S-EPMC6186141 | biostudies-other