What are the statistical implications of treatment non-compliance in cluster randomized trials: A simulation study.
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ABSTRACT:
SUBMITTER: Moerbeek M
PROVIDER: S-EPMC6856967 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveRandomised controlled trials (RCTs) are often considered as the gold standard for assessing new health interventions. Patients are randomly assigned to receive an intervention or control. The effect of the intervention can be estimated by comparing outcomes between groups, whose prognostic factors are expected to balance by randomisation. However, patients' non-compliance with their assigned treatment will undermine randomisation and potentially bias the estimate of treatment effect. Through simulation, we aim to compare common approaches in analysing non-compliant data under different non-compliant scenarios.SettingsBased on a real study, we simulated hypothetical trials by varying three non-compliant factors: the type, randomness and degree of non-compliance. We compared the intention-to-treat (ITT), as-treated (AT), per-protocol (PP), instrumental variable (IV) and complier average casual effect (CACE) analyses to estimate large (50% improvement over the control), moderate (25% improvement) and null (same as the control) treatment effects. Different approaches were compared by the bias of estimate, mean square error (MSE) and 95% coverage of the true value.ResultsFor a large or moderate treatment effect, the ITT estimate was considerably biased in all scenarios. The AT, PP, IV and CACE estimates were unbiased when non-compliant behaviours were random. The IV estimate was unbiased when non-compliant behaviours were symmetrically dependent on patients' conditions. The PP estimate was mostly unbiased when patients in the control group did not have access to the intervention. When the intervention was not different from the control, the ITT was less biased than the other approaches. Similar results were found when comparing the MSE and 95% coverage.ConclusionsThe standard ITT analysis under non-compliance is biased when the intervention has a moderate or large effect. Alternative analyses can provide unbiased or less biased estimates. Based on the results, we make some suggestions on choosing optimal approaches for analysing specific non-compliant scenarios.
| S-EPMC4067862 | biostudies-literature
Project description:IntroductionNon-compliance is a common challenge for researchers and may reduce the power of an intention-to-treat analysis. Whilst a per protocol approach attempts to deal with this issue, it can result in biased estimates. Several methods to resolve this issue have been identified in previous reviews, but there is limited evidence supporting their use. This review aimed to identify simulation studies which compare such methods, assess the extent to which certain methods have been investigated and determine their performance under various scenarios.MethodsA systematic search of several electronic databases including MEDLINE and Scopus was carried out from conception to 30th November 2022. Included papers were published in a peer-reviewed journal, readily available in the English language and focused on comparing relevant methods in a superiority randomised controlled trial under a simulation study. Articles were screened using these criteria and a predetermined extraction form used to identify relevant information. A quality assessment appraised the risk of bias in individual studies. Extracted data was synthesised using tables, figures and a narrative summary. Both screening and data extraction were performed by two independent reviewers with disagreements resolved by consensus.ResultsOf 2325 papers identified, 267 full texts were screened and 17 studies finally included. Twelve methods were identified across papers. Instrumental variable methods were commonly considered, but many authors found them to be biased in some settings. Non-compliance was generally assumed to be all-or-nothing and only occurring in the intervention group, although some methods considered it as time-varying. Simulation studies commonly varied the level and type of non-compliance and factors such as effect size and strength of confounding. The quality of papers was generally good, although some lacked detail and justification. Therefore, their conclusions were deemed to be less reliable.ConclusionsIt is common for papers to consider instrumental variable methods but more studies are needed that consider G-methods and compare a wide range of methods in realistic scenarios. It is difficult to make conclusions about the best method to deal with non-compliance due to a limited body of evidence and the difficulty in combining results from independent simulation studies.Prospero registration numberCRD42022370910.
| S-EPMC10724933 | biostudies-literature
Project description:Smartphone-based devices are increasingly recognized to assess disease symptoms in daily life (e.g. ecological momentary assessment, EMA). Despite this development in digital psychiatry, clinical trials are mainly based on point assessments of psychopathology. This study investigated expectable increases in statistical power by intense assessment in randomized controlled trials (RCTs). A simulation study, based on three scenarios and several empirical data sets, estimated power gains of two- or fivefold pre-post-assessment. For each condition, data sets of various effect sizes were generated, and AN(C)OVAs were applied to the sample of interest (N = 50-N = 200). Power increases ranged from 6% to 92%, with higher gains in more underpowered scenarios and with higher number of repeated assessments. ANCOVA profited from a more precise estimation of the baseline covariate, resulting in additional gains in statistical power. Fivefold pre-post EMA resulted in highest absolute statistical power and clearly outperformed traditional questionnaire assessments. For example, ANCOVA of automatized PHQ-9 questionnaire data resulted in absolute power of 55 (for N = 200 and d = 0.3). Fivefold EMA, however, resulted in power of 88.9. Non-parametric and multi-level analyses resulted in comparable outcomes. Besides providing psychological treatment, digital mental health can help optimizing sensitivity in RCT-based research. Intense assessment appears advisable whenever psychopathology needs to be assessed with high precision at pre- and post-assessment (e.g. small sample sizes, small treatment effects, or when applying optimization problems like machine learning). First empiric studies are promising, but more evidence is needed. Simulations for various effects and a short guide for popular power software are provided for study planning.
| S-EPMC7090342 | biostudies-literature
Project description:Cluster randomized trials (CRTs) have been widely used in field experiments treating a cluster of individuals as the unit of randomization. This study focused particularly on situations where CRTs are accompanied by a common complication, namely, treatment noncompliance or, more generally, intervention nonadherence. In CRTs, compliance may be related not only to individual characteristics but also to the environment of clusters individuals belong to. Therefore, analyses ignoring the connection between compliance and clustering may not provide valid results. Although randomized field experiments often suffer from both noncompliance and clustering of the data, these features have been studied as separate rather than concurrent problems. On the basis of Monte Carlo simulations, this study demonstrated how clustering and noncompliance may affect statistical inferences and how these two complications can be accounted for simultaneously. In particular, the effect of the intervention on individuals who not only were assigned to active intervention but also abided by this intervention assignment (complier average causal effect) was the focus. For estimation of intervention effects considering noncompliance and data clustering, an ML-EM estimation method was employed.
| S-EPMC2917590 | biostudies-literature
Project description:BackgroundThe adoption of cluster randomized trials (CRTs) with the stratified design is currently gaining widespread interest. In the stratified design, clusters are first grouped into two or more strata and then randomized into treatment groups within each stratum. In this study, we evaluated the performance of several commonly used methods for analyzing continuous data from stratified CRTs.MethodsThis is a simulation study where we compared four methods: mixed-effects, generalized estimating equation (GEE), cluster-level (CL) linear regression and meta-regression methods to analyze the continuous data from stratified CRTs using a simulation study with varying numbers of clusters, cluster sizes, intra-cluster correlation coefficients (ICCs) and effect sizes. This study was based on a stratified CRT with one stratification variable with two strata. The performance of the methods was evaluated in terms of the type I error rate, empirical power, root mean square error (RMSE), and width and coverage of the 95% confidence interval (CI).ResultsGEE and meta-regression methods had high type I error rates, higher than 10%, for the small number of clusters. All methods had similar accuracy, measured through RMSE, except meta-regression. Similarly, all methods but meta-regression had similar widths of 95% CIs for the small number of clusters. For the same sample size, the empirical power for all methods decreased as the value of the ICC increased.ConclusionIn this study, we evaluated the performance of several methods for analyzing continuous data from stratified CRTs. Meta-regression was the least efficient method compared to other methods.
| S-EPMC10313865 | biostudies-literature
Project description:BackgroundStepped-wedge cluster randomized trials (SWCRTs) are a type of cluster-randomized trial in which clusters are randomized to cross-over to the active intervention sequentially at regular intervals during the study period. For SWCRTs, sequential imbalances of cluster-level characteristics across the random sequence of clusters may lead to biased estimation. Our study aims to examine the effects of balancing cluster-level characteristics in SWCRTs.MethodsTo quantify the level of cluster-level imbalance, a novel imbalance index was developed based on the Spearman correlation and rank regression of the cluster-level characteristic with the cross-over timepoints. A simulation study was conducted to assess the impact of sequential cluster-level imbalances across different scenarios varying the: number of sites (clusters), sample size, number of cross-over timepoints, site-level intra-cluster correlation coefficient (ICC), and effect sizes. SWCRTs assumed either an immediate "constant" treatment effect, or a gradual "learning" treatment effect which increases over time after crossing over to the active intervention. Key performance metrics included the relative root mean square error (RRMSE) and relative mean bias.ResultsFully-balanced designs almost always had the highest efficiency, as measured by the RRMSE, regardless of the number of sites, ICC, effect size, or sample sizes at each time for SWCRTs with learning effect. A consistent decreasing trend of efficiency was observed by increasing RRMSE as imbalance increased. For example, for a 12-site study with 20 participants per site/timepoint and ICC of 0.10, between the most balanced and least balanced designs, the RRMSE efficiency loss ranged from 52.5% to 191.9%. In addition, the RRMSE was decreased for larger sample sizes, larger number of sites, smaller ICC, and larger effect sizes. The impact of pre-balancing diminished when there was no learning effect.ConclusionThe impact of pre-balancing on preventing efficiency loss was easily observed when there was a learning effect. This suggests benefit of pre-balancing with respect to impacting factors of treatment effects.
| S-EPMC10496299 | biostudies-literature
Project description:Within cluster randomized trials no algorithms exist to generate a full enumeration of a block randomization, balancing for covariates across treatment arms. Furthermore, often for practical reasons multiple blocks are required to fully randomize a study, which may not have been well balanced within blocks.We present a convenient and easy to use randomization tool to undertake allocation concealed block randomization. Our algorithm highlights allocations that minimize imbalance between treatment groups across multiple baseline covariates. We demonstrate the algorithm using a cluster randomized trial in primary care (the PRE-EMPT Study) and show that the software incorporates a trade off between independent random allocations that were likely to be imbalanced, and predictable deterministic approaches that would minimise imbalance. We extend the methodology of single block randomization to allocate to multiple blocks conditioning on previous allocations.The algorithm is included as Additional file 1 and we advocate its use for robust randomization within cluster randomized trials.
| S-EPMC2588445 | biostudies-other
Project description:Cluster randomized trials evaluate the effect of a treatment on persons nested within clusters, where treatment is randomly assigned to clusters. Current equations for the optimal sample size at the cluster and person level assume that the outcome variances and/or the study costs are known and homogeneous between treatment arms. This paper presents efficient yet robust designs for cluster randomized trials with treatment-dependent costs and treatment-dependent unknown variances, and compares these with 2 practical designs. First, the maximin design (MMD) is derived, which maximizes the minimum efficiency (minimizes the maximum sampling variance) of the treatment effect estimator over a range of treatment-to-control variance ratios. The MMD is then compared with the optimal design for homogeneous variances and costs (balanced design), and with that for homogeneous variances and treatment-dependent costs (cost-considered design). The results show that the balanced design is the MMD if the treatment-to control cost ratio is the same at both design levels (cluster, person) and within the range for the treatment-to-control variance ratio. It still is highly efficient and better than the cost-considered design if the cost ratio is within the range for the squared variance ratio. Outside that range, the cost-considered design is better and highly efficient, but it is not the MMD. An example shows sample size calculation for the MMD, and the computer code (SPSS and R) is provided as supplementary material. The MMD is recommended for trial planning if the study costs are treatment-dependent and homogeneity of variances cannot be assumed.
| S-EPMC6120518 | biostudies-other
Project description:BackgroundTo individualize treatment decisions based on patient characteristics, identification of an interaction between a biomarker and treatment is necessary. Often such potential interactions are analysed using data from randomized clinical trials intended for comparison of two treatments. Tests of interactions are often lacking statistical power and we investigated if and how a consideration of further prognostic variables can improve power and decrease the bias of estimated biomarker-treatment interactions in randomized clinical trials with time-to-event outcomes.MethodsA simulation study was performed to assess how prognostic factors affect the estimate of the biomarker-treatment interaction for a time-to-event outcome, when different approaches, like ignoring other prognostic factors, including all available covariates or using variable selection strategies, are applied. Different scenarios regarding the proportion of censored observations, the correlation structure between the covariate of interest and further potential prognostic variables, and the strength of the interaction were considered.ResultsThe simulation study revealed that in a regression model for estimating a biomarker-treatment interaction, the probability of detecting a biomarker-treatment interaction can be increased by including prognostic variables that are associated with the outcome, and that the interaction estimate is biased when relevant prognostic variables are not considered. However, the probability of a false-positive finding increases if too many potential predictors are included or if variable selection is performed inadequately.ConclusionsWe recommend undertaking an adequate literature search before data analysis to derive information about potential prognostic variables and to gain power for detecting true interaction effects and pre-specifying analyses to avoid selective reporting and increased false-positive rates.
| S-EPMC5819679 | biostudies-literature
Project description:Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes, especially in non-inferiority trials. We performed simulations of a non-inferiority trial with a time-fixed treatment and a binary endpoint in order to: i) explore the impact of various patterns of non-adherence and analysis methods on treatment effect estimates; ii) quantify the probability of claiming non-inferiority when the experimental treatment effect is actually inferior; and iii) evaluate alternative methods such as inverse probability weighting and instrumental variable estimation. We found that the probability of concluding non-inferiority when the experimental treatment is actually inferior depends on whether non-adherence is due to confounding or non-confounding factors, and the actual treatments received by the non-adherent participants. With non-adherence, intention-to-treat analysis has a higher tendency to conclude non-inferiority when the experimental treatment is actually inferior under most patterns of non-adherence. This probability of concluding non-inferiority can be increased to as high as 0.1 from 0.025 when the adherence is relatively high at 90%. The direction of bias for the per-protocol analysis depends on the directions of influence the confounders have on adherence and probability of outcome. The inverse probability weighting approach can reduce bias but will only eliminate it if all confounders can be measured without error and are appropriately adjusted for. Instrumental variable estimation overcomes this limitation and gives unbiased estimates even when confounders are not known, but typically requires large sample sizes to achieve acceptable power. Investigators need to consider patterns of non-adherence and potential confounders in trial designs. Adjusted analysis of the per-protocol population with sensitivity analyses on confounders and other approaches, such as instrumental variable estimation, should be considered when non-compliance is anticipated. We provide an online power calculator allowing for various patterns of non-adherence using the above methods.
| S-EPMC7205408 | biostudies-literature