Group Sequential Methods Research Articles

Practical and analytical considerations when performing interim analyses in diagnostic test accuracy studies

Interim analysis is a common methodology in randomised clinical trials but has received less attention in studies of diagnostic test accuracy. In such studies, early termination for futility may be beneficial if early evidence indicates that a diagnostic test is unlikely to achieve a clinically useful level of diagnostic performance, as measured by the sensitivity and specificity. In this paper, we describe relevant practical and analytical considerations when planning and performing interim analysis in diagnostic accuracy studies, focusing on stopping rules for futility. We present an adaptation of the exact group sequential method for diagnostic testing, with R code provided for implementing this method in practice. The method is illustrated using two simulated data sets and data from a published diagnostic accuracy study for point-of-care testing for SARS-CoV-2. The considerations described in this paper can be used to guide decisions as to when an interim analysis in a diagnostic accuracy study is suitable and highlight areas for further methodological development.

Group sequential methods based on supremum logrank statistics underproportional and nonproportionalhazards.

Despite the widespread use of Cox regression for modeling treatment effects in clinical trials, in immunotherapy oncology trials and other settings therapeutic benefits are not immediately realized thereby violating the proportional hazards assumption. Weighted logrank tests and the so-called Maxcombo test involving the combination of multiple logrank test statistics have been advocated to increase power for detecting effects in these and other settings where hazards are nonproportional. We describe a testing framework based on supremum logrank statistics created by successively analyzing and excluding early events, or obtained using a moving time window. We then describe how such tests can be conducted in a group sequential trial with interim analyses conducted for potential early stopping of benefit. The crossing boundaries for the interim test statistics are determined using an easy-to-implement Monte Carlo algorithm. Numerical studies illustrate the good frequency properties of the proposed group sequential methods.

Upstrapping to determine futility: predicting future outcomes nonparametrically from past data

BackgroundClinical trials often involve some form of interim monitoring to determine futility before planned trial completion. While many options for interim monitoring exist (e.g., alpha-spending, conditional power), nonparametric based interim monitoring methods are also needed to account for more complex trial designs and analyses. The upstrap is one recently proposed nonparametric method that may be applied for interim monitoring.MethodsUpstrapping is motivated by the case resampling bootstrap and involves repeatedly sampling with replacement from the interim data to simulate thousands of fully enrolled trials. The p-value is calculated for each upstrapped trial and the proportion of upstrapped trials for which the p-value criteria are met is compared with a pre-specified decision threshold. To evaluate the potential utility for upstrapping as a form of interim futility monitoring, we conducted a simulation study considering different sample sizes with several different proposed calibration strategies for the upstrap. We first compared trial rejection rates across a selection of threshold combinations to validate the upstrapping method. Then, we applied upstrapping methods to simulated clinical trial data, directly comparing their performance with more traditional alpha-spending and conditional power interim monitoring methods for futility.ResultsThe method validation demonstrated that upstrapping is much more likely to find evidence of futility in the null scenario than the alternative across a variety of simulations settings. Our three proposed approaches for calibration of the upstrap had different strengths depending on the stopping rules used. Compared to O’Brien-Fleming group sequential methods, upstrapped approaches had type I error rates that differed by at most 1.7% and expected sample size was 2–22% lower in the null scenario, while in the alternative scenario power fluctuated between 15.7% lower and 0.2% higher and expected sample size was 0–15% lower.ConclusionsIn this proof-of-concept simulation study, we evaluated the potential for upstrapping as a resampling-based method for futility monitoring in clinical trials. The trade-offs in expected sample size, power, and type I error rate control indicate that the upstrap can be calibrated to implement futility monitoring with varying degrees of aggressiveness and that performance similarities can be identified relative to considered alpha-spending and conditional power futility monitoring methods.

Group sequential testing under instrumented difference-in-differences approach.

Unmeasured confounding is a major obstacle to reliable causal inference based on observational studies. Instrumented difference-in-differences (iDiD), a novel idea connecting instrumental variable and standard DiD, ameliorates the above issue by explicitly leveraging exogenous randomness in an exposure trend. In this article, we utilize the above idea of iDiD, and propose a novel group sequential testing method that provides valid inference even in the presence of unmeasured confounders. At each time point, we estimate the average or conditional average treatment effect under iDiD setting using the data accumulated up to that time point, and test the significance of the treatment effect. We derive the joint distribution of the test statistics under the null using the asymptotic properties of M-estimation, and the group sequential boundaries are obtained using the -spending functions. The performance of our proposed approach is evaluated on both synthetic data and Clinformatics Data Mart Database (OptumInsight, Eden Prairie, MN) to examine the association between rofecoxib and acute myocardial infarction, and our method detects significant adverse effect of rofecoxib much earlier than the time when it was finally withdrawn from the market.

Application of Group Sequential Methods to the 2-in-1 Design and Its Extensions for Interim Monitoring

The 2-in-1 adaptive design (Chen et al. 2018) allows seamless expansion of an ongoing Phase 2 trial into a Phase 3 trial to expedite a drug development program. Under a mild assumption expected to generally hold in practice, as Slepian’s lemma guarantees, the trial can be tested at the full alpha level with or without expansion, sacrificing no statistical power. The endpoint used for expansion decisions can be the same as or different from the primary endpoints, and there is no restriction on the expansion threshold. Due to its flexibility and robustness, it has drawn immediate attention from academic researchers and industry practitioners. The design has been substantially extended in the literature and successfully implemented in multiple trials. Group sequential methods are a cornerstone in trial monitoring. A preliminary investigation (Chen, Li, and Deng) suggests that it can be naturally incorporated into the 2-in-1 design without providing formal mathematical proof. In this article, we fill the gap by providing a sufficient condition that is expected to generally hold in practice to unlock the full potential of the 2-in-1 design and pave the way for its broader applications. In practice, the condition can be verified with trial data as needed using simulation studies per the FDA guideline on adaptive designs. We also discuss a special case that guarantees the validity without the need for any simulation checking.

Enhancing English learning materials with data-driven learning: a mixed-methods study of task motivation

ABSTRACT The purpose of the present mixed methods study was to investigate if enhancing Focus on Form (FonF) tasks with data-driven learning (DDL) affects English as a Foreign Language (EFL) learners’ task motivation from the self-determination theory (SDT) perspective. Following a quasi-experimental comparison group pretest-posttest and sequential explanatory mixed methods designs, 76 female English-major university students were randomly assigned to comparison and intervention groups and exposed to the control and treatment, respectively. The control involved completing 15 non-DDL tasks, whereas the treatment was completing 15 DDL-enhanced tasks over a month. The results of a task motivation questionnaire and semi-structured interviews showed that DDL-enhanced tasks increase learners’ task motivation from the SDT perspective. In other words, enhancing FonF tasks with DDL leads to more self-determined motivation to perform English tasks. That is, discovery learning which is a kind of self-regulated learning, along with awareness raising, in tasks with authentic language data where the primary attention is on meaning, tends to promote learners’ agency, and result in more motivation to learn English. This kind of motivation which seems to be the result of increased achievement in English language skills, autonomous language learning, association with others, internal reward, and usefulness, is most influential, as it is self-determined.

Group sequential methods for interim monitoring of randomized clinical trials with time-lagged outcome.

The primary analysis in two-arm clinical trials usually involves inference on a scalar treatment effect parameter; for example, depending on the outcome, the difference of treatment-specific means, risk difference, risk ratio, or odds ratio. Most clinical trials are monitored for the possibility of early stopping. Because ordinarily the outcome on any given subject can be ascertained only after some time lag, at the time of an interim analysis, among the subjects already enrolled, the outcome is known for only a subset and is effectively censored for those who have not been enrolled sufficiently long for it to be observed. Typically, the interim analysis is based only on the data from subjects for whom the outcome has been ascertained. A goal of an interim analysis is to stop the trial as soon as the evidence is strong enough to do so, suggesting that the analysis ideally should make the most efficient use of all available data, thus including information on censoring as well as other baseline and time-dependent covariates in a principled way. A general group sequential framework is proposed for clinical trials with a time-lagged outcome. Treatment effect estimators that take account of censoring and incorporate covariate information at an interim analysis are derived using semiparametric theory and are demonstrated to lead to stronger evidence for early stopping than standard approaches. The associated test statistics are shown to have the independent increments structure, so that standard software can be used to obtain stopping boundaries.

Design and analysis of group-sequential clinical trials based on a modestly weighted log-rank test in anticipation of a delayed separation of survival curves: A practical guidance.

A common feature of many recent trials evaluating the effects of immunotherapy on survival is that non-proportional hazards can be anticipated at the design stage. This raises the possibility to use a statistical method tailored towards testing the purported long-term benefit, rather than applying the more standard log-rank test and/or Cox model. Many such proposals have been made in recent years, but there remains a lack of practical guidance on implementation, particularly in the context of group-sequential designs. In this article, we aim to fill this gap. We illustrate how the POPLAR trial, which compared immunotherapy versus chemotherapy in non-small-cell lung cancer, might have been re-designed to be more robust to the presence of a delayed effect using the modestly-weighted log-rank test in a group-sequential setting. We provide step-by-step instructions on how to analyse a hypothetical realization of the trial, based on this new design. Basic theory on weighted log-rank tests and group-sequential methods is covered, and an accompanying R package (including vignette) is provided.

Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

BackgroundAdaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power.ResultsThe purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during phase II and III. This approach is based on comparing the performance of the different treatment arms in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm, and treatment selection, removing an arm from the trial permanently. The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package ’barts’.ConclusionThe proposed methods for trial design provide an attractive alternative to their frequentist counterparts.

Interim monitoring in sequential multiple assignment randomized trials.

A sequential multiple assignment randomized trial (SMART) facilitates the comparison of multiple adaptive treatment strategies (ATSs) simultaneously. Previous studies have established a framework to test the homogeneity of multiple ATSs by a global Wald test through inverse probability weighting. SMARTs are generally lengthier than classical clinical trials due to the sequential nature of treatment randomization in multiple stages. Thus, it would be beneficial to add interim analyses allowing for an early stop if overwhelming efficacy is observed. We introduce group sequential methods to SMARTs to facilitate interim monitoring based on the multivariate chi-square distribution. Simulation studies demonstrate that the proposed interim monitoring in SMART (IM-SMART) maintains the desired type I error and power with reduced expected sample size compared to the classical SMART. Finally, we illustrate our method by reanalyzing a SMART assessing the effects of cognitive behavioral and physical therapies in patients with knee osteoarthritis and comorbid subsyndromal depressivesymptoms.

Group sequential testing for cluster randomized trials with time-to-event endpoint.

We propose group sequential methods for cluster randomized trials (CRTs) with time-to-event endpoint. The alpha spending function approach is used for sequential data monitoring. The key to this approach is determining the joint distribution of test statistics and the information fraction at the time of interim analysis. We prove that the sequentially computed log-rank statistics in CRTs do not have independent increment property. We also propose an information fraction for group sequential trials with clustered survival data and a corresponding sample size determination approach. Extensive simulation studies are conducted to evaluate the performance of our proposed testing procedure using some existing alpha spending functions in terms of expected sample size and maximal sample size. Real study examples are taken to demonstrate our method.

EMBR-17. DE-INTENSIFICATION OF RADIOTHERAPY IN RIGOROUSLY DEFINED LOW-RISK WNT-SUBGROUP MEDULLOBLASTOMA IS ASSOCIATED WITH UNACCEPTABLY HIGH RISK OF NEURAXIAL FAILURE: RESULTS FROM THE PROSPECTIVE FOR-WNT STUDY

BackgroundMedulloblastoma is a heterogenous disease comprising four molecular subgroups (WNT, SHH, Group 3, and Group 4) with varying outcomes. Excellent long-term survival (>90%) has prompted de-intensification of therapy in WNT-subgroup medulloblastoma globally. FOR-WNT is one such prospective study (CTRI/2017/12/010767) testing the hypothesis that focal conformal radiotherapy (RT) (54Gy/30 fractions/6-weeks) with avoidance of upfront craniospinal irradiation (CSI) followed by standard adjuvant chemotherapy significantly reduces RT-related late toxicity without unduly compromising survival in low-risk WNT-subgroup medulloblastoma (residual tumor <1.5cm2 with no evidence of metastases in children aged between 3–16 years). MethodsPatients with low-risk WNT-subgroup medulloblastoma were enrolled after written informed consent/assent. To ensure patient safety, stopping rules were devised according to group-sequential method. ResultsBetween July 2017 till Feb 2019, seven children of WNT-pathway medulloblastoma were treated with focal conformal RT followed by 6-cycles of adjuvant chemotherapy (cisplatin, cyclophosphamide, and vincristine). One child succumbed to acute renal failure during chemotherapy, while the other 6 patients completed all 6-cycles as planned. Three children were detected with neuraxial failure (supratentorial brain and/or spine) without synchronous local recurrence in the treated tumor-bed on surveillance neuro-imaging between 1.5–2 years from index diagnosis following which the study was terminated prematurely. All 3 children with relapse were treated with salvage CSI (35Gy/21 fractions) with (conformal avoidance of previously treated tumor-bed) plus boost irradiation (10.8-18Gy/6–10 fractions) of metastatic deposits resulting in complete/near complete response and are alive with controlled disease. The other 3 children have not shown any evidence of relapse for over 2-years from index diagnosis and remain on active clinico-radiological surveillance. ConclusionIn rigorously defined low-risk WNT-subgroup medulloblastoma, avoidance of upfront CSI is associated with unacceptably high risk of neuraxial failure. A successor study (FOR-WNT 2) incorporating low-dose CSI (18Gy/10 fractions) with similar tumor-bed dose and adjuvant systemic chemotherapy is currently underway.

Statistical methods for the analysis of adverse event data in randomised controlled trials: a scoping review and taxonomy

BackgroundStatistical methods for the analysis of harm outcomes in randomised controlled trials (RCTs) are rarely used, and there is a reliance on simple approaches to display information such as in frequency tables. We aimed to identify whether any statistical methods had been specifically developed to analyse prespecified secondary harm outcomes and non-specific emerging adverse events (AEs).MethodsA scoping review was undertaken to identify articles that proposed original methods or the original application of existing methods for the analysis of AEs that aimed to detect potential adverse drug reactions (ADRs) in phase II-IV parallel controlled group trials. Methods where harm outcomes were the (co)-primary outcome were excluded.Information was extracted on methodological characteristics such as: whether the method required the event to be prespecified or could be used to screen emerging events; and whether it was applied to individual events or the overall AE profile. Each statistical method was appraised and a taxonomy was developed for classification.ResultsForty-four eligible articles proposing 73 individual methods were included. A taxonomy was developed and articles were categorised as: visual summary methods (8 articles proposing 20 methods); hypothesis testing methods (11 articles proposing 16 methods); estimation methods (15 articles proposing 24 methods); or methods that provide decision-making probabilities (10 articles proposing 13 methods). Methods were further classified according to whether they required a prespecified event (9 articles proposing 12 methods), or could be applied to emerging events (35 articles proposing 61 methods); and if they were (group) sequential methods (10 articles proposing 12 methods) or methods to perform final/one analyses (34 articles proposing 61 methods).ConclusionsThis review highlighted that a broad range of methods exist for AE analysis. Immediate implementation of some of these could lead to improved inference for AE data in RCTs. For example, a well-designed graphic can be an effective means to communicate complex AE data and methods appropriate for counts, time-to-event data and that avoid dichotomising continuous outcomes can improve efficiencies in analysis. Previous research has shown that adoption of such methods in the scientific press is limited and that strategies to support change are needed.Trial registrationPROSPERO registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=97442

Extensions of the 2-in-1 adaptive design

The 2-in-1 adaptive design [Chen et al. 2018] allows seamless expansion of an ongoing Phase 2 trial into a Phase 3 trial to expedite a drug development program. An intermediate endpoint can be used for the adaptive decision. Under a mild assumption that is expected to generally hold in practice, both the Phase 2 trial (in case of no expansion) and the Phase 3 trial (in case of expansion) can be tested at the full alpha level without inflating the overall Type I error of the study. Due to its flexibility and robustness, the design has quickly generated a lot of interest since its recent publication.We make three major extensions to the 2-in-1 design in this paper: 1) an increase of adaptive decisions from two to any number; 2) incorporation of the group sequential method for data monitoring; 3) expansion of the univariate design to multivariate. These extensions not only facilitate the application of 2-in-1 design in practice but also stimulate research interest in related statistical issues. Although we mainly use hypothetical trials in oncology and neuroscience for illustration, the application of the 2-in-1 design and its extensions is not limited to the two therapeutic areas.

Information growth for sequential monitoring of clinical trials with a stepped wedge cluster randomized design and unknown intracluster correlation.

In a stepped wedge study design, study clusters usually start with the baseline treatment and then cross over to the intervention at randomly determined times. Such designs are useful when the intervention must be delivered at the cluster level and are becoming increasingly common in practice. In these trials, if the outcome is death or serious morbidity, one may have an ethical imperative to monitor the trial and stop before maximum enrollment if the new therapy is proven to be beneficial. In addition, because formal monitoring allows for the stoppage of trials when a significant benefit for new therapy has been ruled out, their use can make a research program more efficient. However, use of the stepped wedge cluster randomized study design complicates the implementation of standard group sequential monitoring methods. Both the correlation of observations introduced by the clustered randomization and the timing of crossover from one treatment to the other impact the rate of information growth, an important component of an interim analysis. We simulated cross-sectional stepped wedge study data in order to evaluate the impact of sequential monitoring on the Type I error and power when the true intracluster correlation is unknown. We studied the impact of varying intracluster correlations, treatment effects, methods of estimating the information growth, and boundary shapes. While misspecified information growth can impact both the Type I error and power of a study in some settings, we observed little inflation of the Type I error and only moderate reductions in power across a range of misspecified information growth patterns in our simulations. Taking the study design into account and using either an estimate of the intracluster correlation from the ongoing study or other data in the same clusters should allow for easy implementation of group sequential methods in future stepped wedge designs.

Nonparametric group sequential methods for recurrent and terminal events from multiple follow-up windows.

Few methods are currently available for group sequential analysis of recurrent events data subject to a terminal event in the clinical trial setting. This research helps fill this gap by developing a completely nonparametric group sequential monitoring procedure for use with the two-sample Tayob and Murray statistic. Advantages of the Tayob and Murray statistic include high power to detect treatment differences when there is correlation between recurrent event times or between recurrent and terminal events in an individual. This statistic does not suffer bias from dependent censoring, regardless of the correlation between event times in an individual. This manuscript briefly reviews the Tayob and Murray statistic, develops and describes how to use methods for its group sequential analysis, and through simulation, compares its operating characteristics with those of Cook and Lawless, which is currently in use as the only available nonparametric method for group sequential analysis of recurrent event data. The merits of our proposed approach are most clearly demonstrated when gap times between recurrent events are correlated; when gap times between events are independent, the Cook and Lawless method is difficult to beat. Simulations demonstrate that as correlation between recurrent event times grows, the reduction in power using the Cook and Lawless approach is substantial when compared to our method. Finally, we use our method to analyze recurrent acute exacerbation outcomes from the azithromycin in chronic obstructive pulmonary disease trial.

USE of group sequential test methods in the survival analysis in aquaculture

The information obtained as a result of research in natural sciences should have a high statistical power, that is also valid for the field of aquaculture. The sample size needs to be increased in order to increase the statistical power of a scientific study. However, studying on large sample size may not always be possible. Therefore, many experimental designs have been developed for data collection purposes to keep the sample size minimum. Group sequential designs have been enhanced for the data entered sequentially to the trial most of the time in medicine, production, economy etc. With these designs, the studies can be early terminated and results can also be obtained the results based on with high statistical power with smaller sample size. These experimental designs are specifically applied to survival data. In the survival analysis used in the field of aquaculture, all of the samples are included at the beginning. One of the important risk in this method is the loss of most or all of the samples due to unexpected effects. If this occurs, the trial must be discontinued or repeated and procedure then becomes time consuming and expensive. To minimize these risks the use of group sequential test methods is proposed in our study. It can be done by a standard treatment (or survival etc.) group and a (or more) new treatment (or survival etc.) group(s). For this approach, 450 rainbow trout were divided into three sequential groups including 1. Control, 2. Aeromonas hyrophilia infected group and 3. Yersinia ruckeri infected group. The survival-time of each sequential run was evaluated by means of four interim analyses via a retrospective trial design. By entering new samples during the trial, both unexpected risks were minimized and continuous data was provided. After the third interim analysis, it was determined that the survival times of the three groups were significantly different from each other and the early termination of the trial was approved.

Strategies for validating biomarkers using data from a reference set.

Candidate biomarkers discovered in the laboratory need to be rigorously validated before advancing to clinical application. However, it is often expensive and time-consuming to collect the high quality specimens needed for validation; moreover, such specimens are often limited in volume. The Early Detection Research Network has developed valuable specimen reference sets that can be used by multiple labs for biomarker validation. To optimize the chance of successful validation, it is critical to efficiently utilize the limited specimens in these reference sets on promising candidate biomarkers. Towards this end, we propose a novel two-stage validation strategy that partitions the samples in the reference set into two groups for sequential validation. The proposed strategy adopts the group sequential testing method to control for the type I error rate and rotates group membership to maximize the usage of available samples. We develop analytical formulas for performance parameters of this strategy in terms of the expected numbers of biomarkers that can be evaluated and the truly useful biomarkers that can be successfully validated, which can provide valuable guidance for future study design. The performance of our proposed strategy for validating biomarkers with respect to the points on the receiver operating characteristic curve are evaluated via extensive simulation studies and compared with the default strategy of validating each biomarker using all samples in the reference set. Different types of early stopping rules and boundary shapes in the group sequential testing method are considered. Compared with the default strategy, our proposed strategy makes more efficient use of the limited resources in the reference set by allowing more candidate biomarkers to be evaluated, giving a better chance of having truly useful biomarkers successfully validated.

Control of Type I Error Rates in Bayesian Sequential Designs

Bayesian approaches to phase II clinical trial designs are usually based on the posterior distribution of the parameter of interest and calibration of certain threshold for decision making. If the posterior probability is computed and assessed in a sequential manner, the design may involve the problem of multiplicity, which, however, is often a neglected aspect in Bayesian trial designs. To effectively maintain the overall type I error rate, we propose solutions to the problem of multiplicity for Bayesian sequential designs and, in particular, the determination of the cutoff boundaries for the posterior probabilities. We present both theoretical and numerical methods for finding the optimal posterior probability boundaries with α-spending functions that mimic those of the frequentist group sequential designs. The theoretical approach is based on the asymptotic properties of the posterior probability, which establishes a connection between the Bayesian trial design and the frequentist group sequential method. The numerical approach uses a sandwich-type searching algorithm, which immensely reduces the computational burden. We apply least-square fitting to find the α-spending function closest to the target. We discuss the application of our method to single-arm and double-arm cases with binary and normal endpoints, respectively, and provide a real trial example for each case.

Applying sequential surveillance methods that use regression adjustment or weighting to control confounding in a multisite, rare-event, distributed setting: Part 2 in-depth example of a reanalysis of the measles-mumps-rubella-varicella combination vaccine and seizure risk

ObjectiveIn-depth example of two new group sequential methods for postmarket safety monitoring of new medical products. Study Design and SettingExisting trial-based group sequential approaches have been extended to adjust for confounders, accommodate rare events, and address privacy-related constraints on data sharing. Most adaptations have involved design-based confounder strategies, for example, self-controlled or exposure matching, while analysis-based approaches like regression and weighting have received less attention. We describe the methodology of two new group sequential approaches that use analysis-based confounder adjustment (GS GEE) and weighting (GS IPTW). Using data from the Food and Drug Administration's Sentinel network, we apply both methods in the context of a known positive association: the measles-mumps-rubella-varicella vaccine and seizure risk in infants. ResultsEstimates from both new approaches were similar and comparable to prior studies using design-based methods to address confounding. The time to detection of a safety signal was considerably shorter for GS IPTW, which estimates a risk difference, compared to GS GEE, which provides relative estimates of excess risk. ConclusionFuture group sequential safety surveillance efforts should consider analysis-based confounder adjustment techniques that evaluate safety signals on the risk difference scale to achieve greater statistical power and more timely results.