A guide to handling missing data in cost-effectiveness analysis conducted within randomised controlled trials.
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ABSTRACT:
SUBMITTER: Faria R
PROVIDER: S-EPMC4244574 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:AimsThe analysis of randomized controlled trials with incomplete binary outcome data is challenging. We develop a general method for exploring the impact of missing data in such trials, with a focus on abstinence outcomes.DesignWe propose a sensitivity analysis where standard analyses, which could include 'missing = smoking' and 'last observation carried forward', are embedded in a wider class of models.SettingWe apply our general method to data from two smoking cessation trials.ParticipantsA total of 489 and 1758 participants from two smoking cessation trials.MeasurementsThe abstinence outcomes were obtained using telephone interviews.FindingsThe estimated intervention effects from both trials depend on the sensitivity parameters used. The findings differ considerably in magnitude and statistical significance under quite extreme assumptions about the missing data, but are reasonably consistent under more moderate assumptions.ConclusionsA new method for undertaking sensitivity analyses when handling missing data in trials with binary outcomes allows a wide range of assumptions about the missing data to be assessed. In two smoking cessation trials the results were insensitive to all but extreme assumptions.
| S-EPMC4241048 | biostudies-literature
Project description:To evaluate the clinical and cost-effectiveness of non-medical prescribing (NMP).Systematic review. Two reviewers independently completed searches, eligibility assessment and assessment of risk of bias.Pre-defined search terms/combinations were utilised to search electronic databases. In addition, hand searches of reference lists, key journals and grey literature were employed alongside consultation with authors/experts.Randomised controlled trials (RCTs) evaluating clinical or cost-effectiveness of NMP. Measurements reported on one or more outcome(s) of: pain, function, disability, health, social impact, patient-safety, costs-analysis, quality adjusted life years (QALYs), patient satisfaction, clinician perception of clinical and functional outcomes.Three RCTs from two countries were included (n = 932 participants) across primary and tertiary care settings. One RCT was assessed as low risk of bias, one as high risk of bias and one as unclear risk of bias. All RCTs evaluated clinical effectiveness with one also evaluating cost-effectiveness. Clinical effectiveness was evaluated using a range of safety and patient-reported outcome measures. Participants demonstrated significant improvement in outcomes when receiving NMP compared to treatment as usual (TAU) in all RCTs. An associated cost analysis showed NMP to be more expensive than TAU (regression coefficient p = 0.0000), however experimental groups generated increased QALYs compared to TAU.Limited evidence with overall unclear risk of bias exists evaluating clinical and cost-effectiveness of NMP across all professions and clinical settings. GRADE assessment revealed moderate quality evidence. Evidence suggests that NMP is safe and can provide beneficial clinical outcomes. Benefits to the health economy remain unclear, with the cost-effectiveness of NMP assessed by a single pilot RCT of low risk of bias. Adequately powered low risk of bias RCTs evaluating clinical and cost effectiveness are required to evaluate NMP across clinical specialities, professions and settings.PROSPERO (CRD42015017212).
| S-EPMC5839564 | biostudies-literature
Project description:Accelerometers and other wearable devices are increasingly being used in clinical trials to provide an objective measure of the impact of an intervention on physical activity. Missing data are ubiquitous in this setting, typically for one of two reasons: patients may not wear the device as per protocol, and/or the device may fail to collect data (e.g. flat battery, water damage). However, it is not always possible to distinguish whether the participant stopped wearing the device, or if the participant is wearing the device but staying still. Further, a lack of consensus in the literature on how to aggregate the data before analysis (hourly, daily, weekly) leads to a lack of consensus in how to define a "missing" outcome. Different trials have adopted different definitions (ranging from having insufficient step counts in a day, through to missing a certain number of days in a week). We propose an analysis framework that uses wear time to define missingness on the epoch and day level, and propose a multiple imputation approach, at the day level, which treats partially observed daily step counts as right censored. This flexible approach allows the inclusion of auxiliary variables, and is consistent with almost all the primary analysis models described in the literature, and readily allows sensitivity analysis (to the missing at random assumption) to be performed. Having presented our framework, we illustrate its application to the analysis of the 2019 MOVE-IT trial of motivational interviewing to increase exercise.
| S-EPMC8178870 | biostudies-literature
Project description:IntroductionCohort studies generate and collect longitudinal data for a variety of research purposes. Randomised controlled trials (RCTs) increasingly use cohort studies as data infrastructures to help identify and recruit trial participants and assess outcomes.ObjectiveTo examine the extent, range and nature of research using cohorts for RCTs and describe the varied definitions and conceptual boundaries for RCTs using cohorts.DesignScoping review.Data sourcesSearches were undertaken in January 2021 in MEDLINE (Ovid) and EBM Reviews-Cochrane Methodology Registry (Final issue, third Quarter 2012).Eligibility criteriaReports published between January 2007 and December 2021 of (a) cohorts used or planned to be used, to conduct RCTs, or (b) RCTs which use cohorts to recruit participants and/or collect trial outcomes, or (c) methodological studies discussing the use of cohorts for RCTs.Data extraction and synthesisData were extracted on the condition being studied, age group, setting, country/continent, intervention(s) and comparators planned or received, unit of randomisation, timing of randomisation, approach to informed consent, study design and terminology.ResultsA total of 175 full-text articles were assessed for eligibility. We identified 61 protocols, 9 descriptions of stand-alone cohorts intended to be used for future RCTs, 39 RCTs using cohorts and 34 methodological papers.The use and scope of this approach is growing. The thematics of study are far-ranging, including population health, oncology, mental and behavioural disorders, and musculoskeletal conditions.Authors reported that this approach can lead to more efficient recruitment, more representative samples, and lessen disappointment bias and crossovers.ConclusionThis review outlines the development of cohorts to conduct RCTs including the range of use and innovative changes and adaptations. Inconsistencies in the use of terminology and concepts are highlighted. Guidance now needs to be developed to support the design and reporting of RCTs conducted using cohorts.
| S-EPMC10928784 | biostudies-literature
Project description:Missing data are a ubiquitous problem in longitudinal substance use disorder (SUD) clinical trials. In particular, the rates of missingness are often high and study participants often intermittently skip their scheduled outcome assessments, leading to so-called "non-monotone" missing data patterns. Moreover, when the primary outcome is a measure of substance use, study investigators often have strong prior beliefs based on their clinical experience that those participants with missing data are more likely to be using substances at those occasions, i.e., data are missing not at random (MNAR). Although approaches for handling missing data are well-developed when the missing data patterns are monotone, arising primarily from study participants withdrawing from the trial prematurely, fewer methods are available for non-monotone missingness. In this paper we review some conventional, as well as more novel, methods for handling non-monotone missingness in SUD trials when the repeatedly measured outcome variable is binary (e.g., denoting presence/absence of substance use). We compare and contrast the different approaches using data from a longitudinal clinical trial of four psychosocial treatments from the Collaborative Cocaine Treatment Study. We conclude by making some recommendations to the SUD research community concerning how more principled methods for handling missing data can be incorporated in the analysis and reporting of trial results.
| S-EPMC10528893 | biostudies-literature
Project description:The use of real-world evidence (RWE) studies, including pragmatic randomised controlled trials (RCTs; randomised RWE studies), to aid the development of treatment guidelines, is gradually becoming a mainstay within clinical practice. RWE is an integral part of patient-driven decision-making and offers important value to add complimentary evidence to traditional RCTs; these provide a more well-rounded view of the benefits to patient-reported outcomes and improve the external validity of a given treatment versus findings from traditional RCTs alone. Discussions in recent scientific workshops explored the importance of pragmatic RCTs in optimising guideline development and patient care in chronic obstructive pulmonary disease (COPD) and asthma. The Salford Lung Study in patients with COPD (NCT01551758) and asthma (NCT01706198) were the world's first prelicence pragmatic RCTs that compared novel investigational treatments with existing COPD and asthma treatments and, more recently (2021), RWE studies have been used by the American Thoracic Society and the US Food and Drug Administration to support the approval of an immunosuppressant drug in patients receiving lung transplants. This highlights the importance of RWE data in supporting clinical guideline development and emphasises the advantages for the use of pragmatic RCTs in guiding clinical practice.
| S-EPMC9528570 | biostudies-literature
Project description:BackgroundMissing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs.MethodsA targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared.ResultsA total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based (n = 9) or reference-based approach (n = 7). Controlled MI was mostly used in sensitivity analysis (n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods.ConclusionsControlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters.
| S-EPMC8048273 | biostudies-literature
Project description:The aim of this review was to establish the frequency with which trials take into account missingness, and to discover what methods trialists use for adjustment in randomised controlled trials with longitudinal measurements. Failing to address the problems that can arise from missing outcome data can result in misleading conclusions. Missing data should be addressed as a means of a sensitivity analysis of the complete case analysis results. One hundred publications of randomised controlled trials with longitudinal measurements were selected randomly from trial publications from the years 2005 to 2012. Information was extracted from these trials, including whether reasons for dropout were reported, what methods were used for handing the missing data, whether there was any explanation of the methods for missing data handling, and whether a statistician was involved in the analysis. The main focus of the review was on missing data post dropout rather than missing interim data. Of all the papers in the study, 9 (9%) had no missing data. More than half of the papers included in the study failed to make any attempt to explain the reasons for their choice of missing data handling method. Of the papers with clear missing data handling methods, 44 papers (50%) used adequate methods of missing data handling, whereas 30 (34%) of the papers used missing data methods which may not have been appropriate. In the remaining 17 papers (19%), it was difficult to assess the validity of the methods used. An imputation method was used in 18 papers (20%). Multiple imputation methods were introduced in 1987 and are an efficient way of accounting for missing data in general, and yet only 4 papers used these methods. Out of the 18 papers which used imputation, only 7 displayed the results as a sensitivity analysis of the complete case analysis results. 61% of the papers that used an imputation explained the reasons for their chosen method. Just under a third of the papers made no reference to reasons for missing outcome data. There was little consistency in reporting of missing data within longitudinal trials.
| S-EPMC4087243 | biostudies-literature
Project description:BackgroundHistorically, a priori power and sample size calculations have not been routinely performed cost-effectiveness analyses (CEA), partly because the absence of published cost and effectiveness correlation and variance data, which are essential for power and sample size calculations. Importantly, the empirical correlation between cost and effectiveness has not been examined with respect to the estimation of value-for-money in clinical literature. Therefore, it is not well established if cost-effectiveness studies embedded within randomized-controlled-trials (RCTs) are under- or over-powered to detect changes in value-for-money. However, recently guidelines (such as those from ISPOR) and funding agencies have suggested sample size and power calculations should be considered in CEAs embedded in clinical trials.MethodsWe examined all RCTs conducted by the Canadian Cancer Trials Group with an embedded cost-effectiveness analysis. Variance and correlation of effectiveness and costs were derived from original-trial data. The incremental net benefit method was used to calculate the power of the cost-effectiveness analysis, with exploration of alternative correlation and willingness-to-pay values.ResultsWe identified four trials for inclusion. We observed that a hypothetical scenario of correlation coefficient of zero between cost and effectiveness led to a conservative estimate of sample size. The cost-effectiveness analysis was under-powered to detect changes in value-for-money in two trials, at willingness-to-pay of $100,000. Based on our observations, we present six considerations for future economic evaluations, and an online program to help analysts include a priori sample size and power calculations in future clinical trials.ConclusionThe correlation between cost and effectiveness had a potentially meaningful impact on the power and variance of value-for-money estimates in the examined cost-effectiveness analyses. Therefore, the six considerations and online program, may facilitate a priori power calculations in embedded cost-effectiveness analyses in future clinical trials.
| S-EPMC10398980 | biostudies-literature
Project description:BackgroundA three-arm parallel group, randomised controlled trial set in general dental practices in England, Scotland, and Wales was undertaken to evaluate three strategies to manage dental caries in primary teeth. Children, with at least one primary molar with caries into dentine, were randomised to receive Conventional with best practice prevention (C?+?P), Biological with best practice prevention (B?+?P), or best practice Prevention Alone (PA).MethodsData on costs were collected via case report forms completed by clinical staff at every visit. The co-primary outcomes were incidence of, and number of episodes of, dental pain and/or infection avoided. The three strategies were ranked in order of mean cost and a more costly strategy was compared with a less costly strategy in terms of incremental cost-effectiveness. Costs and outcomes were discounted at 3.5%.ResultsA total of 1144 children were randomised with data on 1058 children (C?+?P n?=?352, B?+?P n?=?352, PA n?=?354) used in the analysis. On average, it costs £230 to manage dental caries in primary teeth over a period of up to 36?months. Managing children in PA was, on average, £19 (97.5% CI: -£18 to £55) less costly than managing those in B?+?P. In terms of effectiveness, on average, there were fewer incidences of, (-?0.06; 97.5% CI: -?0.14 to 0.02) and fewer episodes of dental pain and/or infection (-?0.14; 97.5% CI: -?0.29 to 0.71) in B?+?P compared to PA. C?+?P was unlikely to be considered cost-effective, as it was more costly and less effective than B?+?P.ConclusionsThe mean cost of a child avoiding any dental pain and/or infection (incidence) was £330 and the mean cost per episode of dental pain and/or infection avoided was £130. At these thresholds B?+?P has the highest probability of being considered cost-effective. Over the willingness to pay thresholds considered, the probability of B?+?P being considered cost-effective never exceeded 75%.Trial registrationThe trial was prospectively registered with the ISRCTN (reference number ISRCTN77044005) on the 26th January 2009 and East of Scotland Research Ethics Committee provided ethical approved (REC reference: 12/ES/0047).
| S-EPMC7011536 | biostudies-literature