Choosing sensitivity analyses for randomised trials: principles

Evaluating whether the proportional odds models to analyse ordinal outcomes in COVID-19 clinical trials is providing clinically interpretable treatment effects: A systematic review

BACKGROUND

After an initial recommendation from the World Health Organisation, trials of patients hospitalised with COVID-19 often include an ordinal clinical status outcome, which comprises a series of ordered categorical variables, typically ranging from 'Alive and discharged from hospital' to 'Dead'. These ordinal outcomes are often analysed using a proportional odds model, which provides a common odds ratio as an overall measure of effect, which is generally interpreted as the odds ratio for being in a higher category. The common odds ratio relies on the assumption of proportional odds, which implies an identical odds ratio across all ordinal categories; however, there is generally no statistical or biological basis for which this assumption should hold; and when violated, the common odds ratio may be a biased representation of the odds ratios for particular categories within the ordinal outcome. In this study, we aimed to evaluate to what extent the common odds ratio in published COVID-19 trials differed to simple binary odds ratios for clinically important outcomes.

METHODS

We conducted a systematic review of randomised trials evaluating interventions for patients hospitalised with COVID-19, which used a proportional odds model to analyse an ordinal clinical status outcome, published between January 2020 and May 2021. We assessed agreement between the common odds ratio and the odds ratio from a standard logistic regression model for three clinically important binary outcomes: 'Alive', 'Alive without mechanical ventilation', and 'Alive and discharged from hospital'.

RESULTS

Sixteen randomised clinical trials, comprising 38 individual comparisons, were included in this study; of these, only 6 trials (38%) formally assessed the proportional odds assumption. The common odds ratio differed by more than 25% compared to the binary odds ratios in 55% of comparisons for the outcome 'Alive', 37% for 'Alive without mechanical ventilation', and 24% for 'Alive and discharged from hospital'. In addition, the common odds ratio systematically underestimated the odds ratio for the outcome 'Alive' by -16.8% (95% confidence interval: -28.7% to -2.9%, p = 0.02), though differences for the other outcomes were smaller and not statistically significant (-8.4% for 'Alive without mechanical ventilation' and 3.6% for 'Alive and discharged from hospital'). The common odds ratio was statistically significant for 18% of comparisons, while the binary odds ratio was significant in 5%, 16%, and 3% of comparisons for the outcomes 'Alive', 'Alive without mechanical ventilation', and 'Alive and discharged from hospital', respectively.

CONCLUSION

The common odds ratio from proportional odds models often differs substantially to odds ratios from clinically important binary outcomes, and similar to composite outcomes, a beneficial common OR from a proportional odds model does not necessarily indicate a beneficial effect on the most important categories within the ordinal outcome.

Comparison of the Working Alliance in Blended Cognitive Behavioral Therapy and Treatment as Usual for Depression in Europe: Secondary Data Analysis of the E-COMPARED Randomized Controlled Trial

BACKGROUND

Increasing interest has centered on the psychotherapeutic working alliance as a means of understanding clinical change in digital mental health interventions in recent years. However, little is understood about how and to what extent a digital mental health program can have an impact on the working alliance and clinical outcomes in a blended (therapist plus digital program) cognitive behavioral therapy (bCBT) intervention for depression.

OBJECTIVE

This study aimed to test the difference in working alliance scores between bCBT and treatment as usual (TAU), examine the association between working alliance and depression severity scores in both arms, and test for an interaction between system usability and working alliance with regard to the association between working alliance and depression scores in bCBT at 3-month assessments.

METHODS

We conducted a secondary data analysis of the E-COMPARED (European Comparative Effectiveness Research on Blended Depression Treatment versus Treatment-as-usual) trial, which compared bCBT with TAU across 9 European countries. Data were collected in primary care and specialized services between April 2015 and December 2017. Eligible participants aged 18 years or older and diagnosed with major depressive disorder were randomized to either bCBT (n=476) or TAU (n=467). bCBT consisted of 6-20 sessions of bCBT (involving face-to-face sessions with a therapist and an internet-based program). TAU consisted of usual care for depression. The main outcomes were scores of the working alliance (Working Alliance Inventory-Short Revised-Client [WAI-SR-C]) and depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]) at 3 months after randomization. Other variables included system usability scores (System Usability Scale-Client [SUS-C]) at 3 months and baseline demographic information. Data from baseline and 3-month assessments were analyzed using linear regression models that adjusted for a set of baseline variables.

RESULTS

Of the 945 included participants, 644 (68.2%) were female, and the mean age was 38.96 years (IQR 38). bCBT was associated with higher composite WAI-SR-C scores compared to TAU (B=5.67, 95% CI 4.48-6.86). There was an inverse association between WAI-SR-C and PHQ-9 in bCBT (B=-0.12, 95% CI -0.17 to -0.06) and TAU (B=-0.06, 95% CI -0.11 to -0.02), in which as WAI-SR-C scores increased, PHQ-9 scores decreased. Finally, there was a significant interaction between SUS-C and WAI-SR-C with regard to an inverse association between higher WAI-SR-C scores and lower PHQ-9 scores in bCBT (b=-0.030, 95% CI -0.05 to -0.01; P=.005).

CONCLUSIONS

To our knowledge, this is the first study to show that bCBT may enhance the client working alliance when compared to evidence-based routine care for depression that services reported offering. The working alliance in bCBT was also associated with clinical improvements that appear to be enhanced by good program usability. Our findings add further weight to the view that the addition of internet-delivered CBT to face-to-face CBT may positively augment experiences of the working alliance.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02542891, https://clinicaltrials.gov/study/NCT02542891; German Clinical Trials Register DRKS00006866, https://drks.de/search/en/trial/DRKS00006866; Netherlands Trials Register NTR4962, https://www.onderzoekmetmensen.nl/en/trial/25452; ClinicalTrials.Gov NCT02389660, https://clinicaltrials.gov/study/NCT02389660; ClinicalTrials.gov NCT02361684, https://clinicaltrials.gov/study/NCT02361684; ClinicalTrials.gov NCT02449447, https://clinicaltrials.gov/study/NCT02449447; ClinicalTrials.gov NCT02410616, https://clinicaltrials.gov/study/NCT02410616; ISRCTN Registry ISRCTN12388725, https://www.isrctn.com/ISRCTN12388725?q=ISRCTN12388725&filters=&sort=&offset=1&totalResults=1&page=1&pageSize=10; ClinicalTrials.gov NCT02796573, https://classic.clinicaltrials.gov/ct2/show/NCT02796573.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s13063-016-1511-1.

Palliative clinical trials in advanced chronic liver disease: Challenges and opportunities

Patients with advanced chronic liver disease have a complex symptom burden and many are not candidates for curative therapy. Despite this, provision of palliative interventions remains woefully inadequate, with an insufficient evidence base being a contributory factor. Designing and conducting palliative interventional trials in advanced chronic liver disease remains challenging for a multitude of reasons. In this manuscript we review past and ongoing palliative interventional trials. We identify barriers and facilitators and offer guidance on addressing these challenges. We hope that this will reduce the inequity in palliative care provision in advanced chronic liver disease.

Handling intercurrent events and missing data in non-inferiority trials using the estimand framework: A tuberculosis case study

INTRODUCTION

The ICH E9 addendum outlining the estimand framework for clinical trials was published in 2019 but provides limited guidance around how to handle intercurrent events for non-inferiority studies. Once an estimand is defined, it is also unclear how to deal with missing values using principled analyses for non-inferiority studies.

METHODS

Using a tuberculosis clinical trial as a case study, we propose a primary estimand, and an additional estimand suitable for non-inferiority studies. For estimation, multiple imputation methods that align with the estimands for both primary and sensitivity analysis are proposed. We demonstrate estimation methods using the twofold fully conditional specification multiple imputation algorithm and then extend and use reference-based multiple imputation for a binary outcome to target the relevant estimands, proposing sensitivity analyses under each. We compare the results from using these multiple imputation methods with those from the original study.

RESULTS

Consistent with the ICH E9 addendum, estimands can be constructed for a non-inferiority trial which improves on the per-protocol/intention-to-treat-type analysis population previously advocated, involving respectively a hypothetical or treatment policy strategy to handle relevant intercurrent events. Results from using the 'twofold' multiple imputation approach to estimate the primary hypothetical estimand, and using reference-based methods for an additional treatment policy estimand, including sensitivity analyses to handle the missing data, were consistent with the original study's reported per-protocol and intention-to-treat analysis in failing to demonstrate non-inferiority.

CONCLUSIONS

Using carefully constructed estimands and appropriate primary and sensitivity estimators, using all the information available, results in a more principled and statistically rigorous approach to analysis. Doing so provides an accurate interpretation of the estimand.

Sensitivity analyses for data missing at random versus missing not at random using latent growth modelling: a practical guide for randomised controlled trials

BACKGROUND

Missing data are ubiquitous in randomised controlled trials. Although sensitivity analyses for different missing data mechanisms (missing at random vs. missing not at random) are widely recommended, they are rarely conducted in practice. The aim of the present study was to demonstrate sensitivity analyses for different assumptions regarding the missing data mechanism for randomised controlled trials using latent growth modelling (LGM).

METHODS

Data from a randomised controlled brief alcohol intervention trial was used. The sample included 1646 adults (56% female; mean age = 31.0 years) from the general population who had received up to three individualized alcohol feedback letters or assessment-only. Follow-up interviews were conducted after 12 and 36 months via telephone. The main outcome for the analysis was change in alcohol use over time. A three-step LGM approach was used. First, evidence about the process that generated the missing data was accumulated by analysing the extent of missing values in both study conditions, missing data patterns, and baseline variables that predicted participation in the two follow-up assessments using logistic regression. Second, growth models were calculated to analyse intervention effects over time. These models assumed that data were missing at random and applied full-information maximum likelihood estimation. Third, the findings were safeguarded by incorporating model components to account for the possibility that data were missing not at random. For that purpose, Diggle-Kenward selection, Wu-Carroll shared parameter and pattern mixture models were implemented.

RESULTS

Although the true data generating process remained unknown, the evidence was unequivocal: both the intervention and control group reduced their alcohol use over time, but no significant group differences emerged. There was no clear evidence for intervention efficacy, neither in the growth models that assumed the missing data to be at random nor those that assumed the missing data to be not at random.

CONCLUSION

The illustrated approach allows the assessment of how sensitive conclusions about the efficacy of an intervention are to different assumptions regarding the missing data mechanism. For researchers familiar with LGM, it is a valuable statistical supplement to safeguard their findings against the possibility of nonignorable missingness.

TRIAL REGISTRATION

The PRINT trial was prospectively registered at the German Clinical Trials Register (DRKS00014274, date of registration: 12th March 2018).

Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data

Causal discovery algorithms estimate causal graphs from observational data. This can provide a valuable complement to analyses focusing on the causal relation between individual treatment-outcome pairs. Constraint-based causal discovery algorithms rely on conditional independence testing when building the graph. Until recently, these algorithms have been unable to handle missing values. In this article, we investigate two alternative solutions: test-wise deletion and multiple imputation. We establish necessary and sufficient conditions for the recoverability of causal structures under test-wise deletion, and argue that multiple imputation is more challenging in the context of causal discovery than for estimation. We conduct an extensive comparison by simulating from benchmark causal graphs: as one might expect, we find that test-wise deletion and multiple imputation both clearly outperform list-wise deletion and single imputation. Crucially, our results further suggest that multiple imputation is especially useful in settings with a small number of either Gaussian or discrete variables, but when the dataset contains a mix of both neither method is uniformly best. The methods we compare include random forest imputation and a hybrid procedure combining test-wise deletion and multiple imputation. An application to data from the IDEFICS cohort study on diet- and lifestyle-related diseases in European children serves as an illustrating example.

Estimators for handling COVID-19-related intercurrent events with a hypothetical strategy

The COVID-19 pandemic has affected clinical trials across disease areas, raising the questions how interpretable results can be obtained from impacted studies. Applying the estimands framework, analyses may seek to estimate the treatment effect in the hypothetical absence of such impact. However, no established estimators exist. This simulation study, based on an ongoing clinical trial in patients with Tourette syndrome, compares the performance of candidate estimators for estimands including either a continuous or binary variable and applying a hypothetical strategy for COVID-19-related intercurrent events (IE). The performance is investigated in a wide range of scenarios, under the null and the alternative hypotheses, including different modeling assumptions for the effect of the IE and proportions of affected patients ranging from 10% to 80%. Bias and type I error inflation were minimal or absent for most estimators under most scenarios, with only multiple imputation- and weighting-based methods displaying a type I error inflation in some scenarios. Of more concern, all methods that discarded post-IE data displayed a sharp decrease of power proportional to the proportion of affected patients, corresponding to both a reduced precision of estimation and larger confidence intervals. The simulation study shows that de-mediation via g-estimation is a promising approach. Besides showing the best performance in our simulation study, these approaches allow to estimate the effect of the IE on the outcome and cross-compare between different studies affected by similar IEs. Importantly, the results can be extrapolated to IEs not related to COVID-19 that follow a similar causal structure.

Sensitivity Analysis: A Method to Promote Certainty and Transparency in Nursing and Health Research

Nursing and health researchers may be presented with uncertainty regarding the utilization or legitimacy of methodological or analytic decisions. Sensitivity analyses are purposed to gain insight and certainty about the validity of research findings reported. Reporting guidelines and health research methodologists have emphasized the importance of utilizing and reporting sensitivity analyses in clinical research. However, sensitivity analyses are underreported in nursing and health research. The aim of this methodological overview is to provide an introduction to the purpose, conduct, interpretation, and reporting of sensitivity analyses, using a series of simulated and contemporary case examples.

Using independent cross-sectional survey data to predict post-migration health trajectories among refugees by estimating transition probabilities and their variances

Health research is often concerned with the transition of health conditions and their relation with given exposures, therefore requiring longitudinal data. However, such data is not always available and resource-intensive to collect. Our aim is to use a pseudo-panel of independent cross-sectional data (e.g., data of T 0 $T_0$ and T 1 $T_1$ ) to extrapolate and approximate longitudinal health trajectories ( T 0 $T_0$ - T 1 $T_1$ ). Methods will be illustrated by examples of studying contextual effects on health among refugees by calculating transition probabilities with associated variances. The data consist of two cross-sectional health surveys among randomly selected refugee samples in reception ( T 0 $T_0$ ) and accommodation centers ( T 1 $T_1$ ) located in Germany's third-largest federal state. Self-reported measures of physical and mental health, health-related quality of life, health care access, and unmet medical needs of 560 refugees were collected. Missing data were imputed by multiple imputation. For each imputed data set, transition probabilities were calculated based on (i) probabilistic discrete event systems with Moore-Penrose generalized inverse matrix method (PDES-MP) and (ii) propensity score matching (PSM). By application of sampling approaches, exploiting the fact that status membership is multinomially distributed, results of both methods were pooled by Rubin's Rule, accounting for within and between-imputation variance. Most of the analyzed estimates of the transition probabilities and their variances are comparable between both methods. However, it seems that they handle sparse cells differently: either assigning an average value for the transition probability for all states with high certainty (i) or assigning a more extreme value for the transition probability with large variance estimate (ii).

Planning a method for covariate adjustment in individually randomised trials: a practical guide

BACKGROUND

It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them.

METHODS

Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting.

RESULTS

The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually transmitted infection testing and results service.

CONCLUSIONS

No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely.

Independence estimators for re-randomisation trials in multi-episode settings: a simulation study

BACKGROUND

Re-randomisation trials involve re-enrolling and re-randomising patients for each new treatment episode they experience. They are often used when interest lies in the average effect of an intervention across all the episodes for which it would be used in practice. Re-randomisation trials are often analysed using independence estimators, where a working independence correlation structure is used. However, research into independence estimators in the context of re-randomisation has been limited.

METHODS

We performed a simulation study to evaluate the use of independence estimators in re-randomisation trials. We focussed on a continuous outcome, and the setting where treatment allocation does not affect occurrence of subsequent episodes. We evaluated different treatment effect mechanisms (e.g. by allowing the treatment effect to vary across episodes, or to become less effective on re-use, etc), and different non-enrolment mechanisms (e.g. where patients who experience a poor outcome are less likely to re-enrol for their second episode). We evaluated four different independence estimators, each corresponding to a different estimand (per-episode and per-patient approaches, and added-benefit and policy-benefit approaches).

RESULTS

We found that independence estimators were unbiased for the per-episode added-benefit estimand in all scenarios we considered. We found independence estimators targeting other estimands (per-patient or policy-benefit) were unbiased, except when there was differential non-enrolment between treatment groups (i.e. when different types of patients from each treatment group decide to re-enrol for subsequent episodes). We found the use of robust standard errors provided close to nominal coverage in all settings where the estimator was unbiased.

CONCLUSIONS

Careful choice of estimand can ensure re-randomisation trials are addressing clinically relevant questions. Independence estimators are a useful approach, and should be considered as the default estimator until the statistical properties of alternative estimators are thoroughly evaluated.

Statistical analysis plan for the Early Youth Engagement in first episode psychosis (EYE-2) study: a pragmatic cluster randomised controlled trial of implementation, effectiveness and cost-effectiveness of a team-based motivational engagement intervention to improve engagement

BACKGROUND

Early Intervention in Psychosis (EIP) services improve health outcomes for young people with psychosis in the medium-long term, but 25% of young people disengage in the first 12 months with costs to their mental health, families, society and health services. This study will evaluate the effectiveness of a team-based motivational engagement intervention, the Early Youth Engagement (EYE-2) intervention.

METHODS AND DESIGN

The EYE-2 trial is a cluster randomised controlled trial comparing the EYE-2 intervention plus standardised EIP service to standardised EIP service alone, with randomisation at the clinical team (cluster) level. The study aimed to enrol 950 young people (aged 14-35 years) with first episode psychosis in 10 teams per arm.

RESULTS

The primary outcome is time to disengagement: days from the date of allocation to care coordinator to date of the last contact following either refusal to engage with an EIP team or lack of response to EIP contact for 3 consecutive months which will be analysed using a shared frailty model. Secondary outcomes are Health of the Nation Outcome Scale (HoNOS), Process of Recovery Questionnaire (QPR), DIALOG (a service user-reported measure of quality of life and treatment satisfaction) and service use outcomes which will be analysed using mixed effects regression models.

DISCUSSION

This paper is the detailed statistical analysis plan for the EYE-2 trial. Any changes to, or deviations from, this plan will be described and justified in the final trial report.

TRIAL REGISTRATION

ISRCTN 51629746 . Prospectively registered on 7 May 2019. Date assigned 10 May 2019.

Therapeutic challenges at the preclinical level for targeted drug development for Opisthorchis viverrini-associated cholangiocarcinoma

INTRODUCTION

Cholangiocarcinoma (CCA) is a malignant tumor of bile duct epithelium with the highest incidence found in Thailand. Some patients are considered suitable for adjuvant therapy and surgical resection is currently the curative treatment for CCA patients. Tumor recurrence is still a hurdle after treatment; hence, finding novel therapeutic strategies to combat CCA is necessary for improving outcome for patients.

AREAS COVERED

We discuss targeted therapies and other novel treatment approaches which include protein kinase inhibitors, natural products, amino acid transporter-based inhibitors, immunotherapy, and drug repurposing. We also examine the challenges of tumor heterogeneity, cancer stem cells (CSCs), the tumor microenvironment, exosomes, multiomics studies, and the potential of precision medicine.

EXPERT OPINION

Because CCA is difficult to diagnose at the early stage, the traditional treatment approaches are not effective for many patients and most tumors recur. Consequently, researchers are exploring multi-aspect molecular carcinogenesis to uncover molecular targets for further development of novel targeted drugs.

Predicting Adaptations to Resistance Training Plus Overfeeding Using Bayesian Regression: A Preliminary Investigation

Relatively few investigations have reported purposeful overfeeding in resistance-trained adults. This preliminary study examined potential predictors of resistance training (RT) adaptations during a period of purposeful overfeeding and RT. Resistance-trained males (n = 28; n = 21 completers) were assigned to 6 weeks of supervised RT and daily consumption of a high-calorie protein/carbohydrate supplement with a target body mass (BM) gain of ≥0.45 kg·wk-1. At baseline and post-intervention, body composition was evaluated via 4-component (4C) model and ultrasonography. Additional assessments of resting metabolism and muscular performance were performed. Accelerometry and automated dietary interviews estimated physical activity levels and nutrient intake before and during the intervention. Bayesian regression methods were employed to examine potential predictors of changes in body composition, muscular performance, and metabolism. A simplified regression model with only rate of BM gain as a predictor was also developed. Increases in 4C whole-body fat-free mass (FFM; (mean ± SD) 4.8 ± 2.6%), muscle thickness (4.5 ± 5.9% for elbow flexors; 7.4 ± 8.4% for knee extensors), and muscular performance were observed in nearly all individuals. However, changes in outcome variables could generally not be predicted with precision. Bayes R2 values for the models ranged from 0.18 to 0.40, and other metrics also indicated relatively poor predictive performance. On average, a BM gain of ~0.55%/week corresponded with a body composition score ((∆FFM/∆BM)*100) of 100, indicative of all BM gained as FFM. However, meaningful variability around this estimate was observed. This study offers insight regarding the complex interactions between the RT stimulus, overfeeding, and putative predictors of RT adaptations.

Response heterogeneity in a randomized controlled trial of telerehabilitation interventions among adults with multiple sclerosis

INTRODUCTION

Telerehabilitation may be effective on average but is not equally effective among all people with multiple sclerosis (MS). Thus, the purpose of this secondary analysis of a randomized controlled trial was to explore whether baseline characteristics of participants with MS influence fatigue and physical activity outcomes of three telerehabilitation interventions.

METHODS

Participants were randomized to contact-control intervention (CC), physical activity-only intervention (PA-only), and physical activity plus fatigue self-management intervention (FM+). The 12-week interventions were delivered over the phone. Sociodemographic (age and income), clinical (comorbidities, mental function and physical function), psychosocial (self-efficacy, outcome expectations and goal-setting), and behavioural baseline characteristics (step count and fatigue self-management behaviors) were used in a moderated regression analysis and a responder analysis to examine their influence on the Fatigue Impact Scale (FIS) and Godin Leisure-Time Exercise Questionnaire (GLTEQ) at post-test (i.e. immediately post-interventions).

RESULTS

No interactions terms were statistically significant in the moderation analysis. However, the responder analysis showed that baseline psychosocial characteristics and mental function were significantly different (p < 0.05) between responders and non-responders. Specifically, non-responders on the FIS at post-test in the PA-only intervention had significantly lower baseline scores in goal setting for engaging in fatigue self-management behaviours. Also, non-responders on the GLTEQ at post-test in the FM+ intervention had significantly worse baseline scores in mental function.

DISCUSSION

Further research is needed to understand the complex relationship among baseline characteristics, telerehabilitation and response heterogeneity. We discuss how research on examining response heterogeneity may be advanced by conducting mega-clinical trials, secondary analyses of big data, meta-analyses and employing non-traditional research designs.

TRIAL REGISTRATION

Clinicaltrials.gov (NCT01572714).

Thrombolysis with alteplase 3-4.5 hours after acute ischaemic stroke: trial reanalysis adjusted for baseline imbalances

OBJECTIVES

Alteplase is commonly recommended for acute ischaemic stroke within 4.5 hours after stroke onset. The Third European Cooperative Acute Stroke Study (ECASS III) is the only trial reporting statistically significant efficacy for clinical outcomes for alteplase use 3-4.5 hours after stroke onset. However, baseline imbalances in history of prior stroke and stroke severity score may confound this apparent finding of efficacy. We reanalysed the ECASS III trial data adjusting for baseline imbalances to determine the robustness or sensitivity of the efficacy estimates.

DESIGN

Reanalysis of randomised placebo-controlled trial. We obtained access to the ECASS III trial data and replicated the previously reported analyses to confirm our understanding of the data. We adjusted for baseline imbalances using multivariable analyses and stratified analyses and performed sensitivity analysis for missing data.

SETTING

Emergency care.

PARTICIPANTS

821 adults with acute ischaemic stroke who could be treated 3-4.5 hours after symptom onset.

INTERVENTIONS

Intravenous alteplase (0.9 mg/kg of body weight) or placebo.

MAIN OUTCOME MEASURES

The original primary efficacy outcome was modified Rankin Scale (mRS) score 0 or 1 (ie, being alive without any disability) and the original secondary efficacy outcome was a global outcome based on a composite of functional end points, both at 90 days. Adjusted analyses were only reported for the primary efficacy outcome and the original study protocol did not specify methods for adjusted analyses. Our adjusted reanalysis included these outcomes, symptom-free status (mRS 0), dependence-free status (mRS 0-2), mortality (mRS 6) and change across the mRS 0-6 spectrum at 90 days; and mortality and symptomatic intracranial haemorrhage at 7 days.

RESULTS

We replicated previously reported unadjusted analyses but discovered they were based on a modified interpretation of the National Institutes of Health Stroke Scale (NIHSS) score. The secondary efficacy outcome was no longer significant using the original NIHSS score. Previously reported adjusted analyses could only be replicated with significant effects for the primary efficacy outcome by using statistical approaches not reported in the trial protocol or statistical analysis plan. In analyses adjusting for baseline imbalances, all efficacy outcomes were not significant, but increases in symptomatic intracranial haemorrhage remained significant.

CONCLUSIONS

Reanalysis of the ECASS III trial data with multiple approaches adjusting for baseline imbalances does not support any significant benefits and continues to support harms for the use of alteplase 3-4.5 hours after stroke onset. Clinicians, patients and policymakers should reconsider interpretations and decisions regarding management of acute ischaemic stroke that were based on ECASS III results.

TRIAL REGISTRATION NUMBER

NCT00153036.

How to design a pre-specified statistical analysis approach to limit p-hacking in clinical trials: the Pre-SPEC framework

Results from clinical trials can be susceptible to bias if investigators choose their analysis approach after seeing trial data, as this can allow them to perform multiple analyses and then choose the method that provides the most favourable result (commonly referred to as 'p-hacking'). Pre-specification of the planned analysis approach is essential to help reduce such bias, as it ensures analytical methods are chosen in advance of seeing the trial data. For this reason, guidelines such as SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and ICH-E9 (International Conference for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) require the statistical methods for a trial's primary outcome be pre-specified in the trial protocol. However, pre-specification is only effective if done in a way that does not allow p-hacking. For example, investigators may pre-specify a certain statistical method such as multiple imputation, but give little detail on how it will be implemented. Because there are many different ways to perform multiple imputation, this approach to pre-specification is ineffective, as it still allows investigators to analyse the data in different ways before deciding on a final approach. In this article, we describe a five-point framework (the Pre-SPEC framework) for designing a pre-specified analysis approach that does not allow p-hacking. This framework was designed based on the principles in the SPIRIT and ICH-E9 guidelines and is intended to be used in conjunction with these guidelines to help investigators design the statistical analysis strategy for the trial's primary outcome in the trial protocol.

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

BACKGROUND

The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking.

METHODS

We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a 'pandemic-free world' and 'world including a pandemic' are of interest.

RESULTS

In any trial, investigators should; (1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic; (2) Establish what data are missing for the chosen estimand; (3) Perform primary analysis under the most plausible missing data assumptions followed by; (4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a 'pandemic-free world', participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the 'world including a pandemic', all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumption - potentially incorporating a pandemic time-period indicator and participant infection status - or a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses.

CONCLUSIONS

Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest.

Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide

Missing data due to loss to follow-up or intercurrent events are unintended, but unfortunately inevitable in clinical trials. Since the true values of missing data are never known, it is necessary to assess the impact of untestable and unavoidable assumptions about any unobserved data in sensitivity analysis. This tutorial provides an overview of controlled multiple imputation (MI) techniques and a practical guide to their use for sensitivity analysis of trials with missing continuous outcome data. These include δ- and reference-based MI procedures. In δ-based imputation, an offset term, δ, is typically added to the expected value of the missing data to assess the impact of unobserved participants having a worse or better response than those observed. Reference-based imputation draws imputed values with some reference to observed data in other groups of the trial, typically in other treatment arms. We illustrate the accessibility of these methods using data from a pediatric eczema trial and a chronic headache trial and provide Stata code to facilitate adoption. We discuss issues surrounding the choice of δ in δ-based sensitivity analysis. We also review the debate on variance estimation within reference-based analysis and justify the use of Rubin's variance estimator in this setting, since as we further elaborate on within, it provides information anchored inference.

Internet-delivered attentional bias modification training (iABMT) for the management of chronic musculoskeletal pain: a protocol for a randomised controlled trial

INTRODUCTION

Chronic musculoskeletal pain is a complex medical condition that can significantly impact quality of life. Patients with chronic pain demonstrate attentional biases towards pain-related information. The therapeutic benefits of modifying attentional biases by implicitly training attention away from pain-related information towards neutral information have been supported in a small number of published studies. Limited research however has explored the efficacy of modifying pain-related biases via the internet. This protocol describes a randomised, double-blind, internet-delivered attentional bias modification intervention, aimed to evaluate the efficacy of the intervention on reducing pain interference. Secondary outcomes are pain intensity, state and trait anxiety, depression, pain-related fear, and sleep impairment. This study will also explore the effects of training intensity on these outcomes, along with participants' perceptions about the therapy.

METHODS AND ANALYSIS

The study is a double-blind, randomised controlled trial with four arms exploring the efficacy of online attentional bias modification training versus placebo training theorised to offer no specific therapeutic benefit. Participants with chronic musculoskeletal pain will be randomised to one of four groups: (1) 10-session attentional modification group; (2) 10-session placebo training group; (3) 18-session attentional modification group; or (4) 18-session placebo training group. In the attentional modification groups, the probe-classification version of the visual-probe task will be used to implicitly train attention away from threatening information towards neutral information. Following the intervention, participants will complete a short interview exploring their perceptions about the online training. In addition, a subgroup analysis for participants aged 16-24 and 25-60 will be undertaken.

ETHICS AND DISSEMINATION

This study has been approved by the University of Southampton Research Ethics Committee. Results will be published in peer-reviewed journals, academic conferences, and in lay reports for pain charities and patient support groups.

TRIAL REGISTRATION NUMBER

NCT02232100; Pre-results.

Reference-based multiple imputation for missing data sensitivity analyses in trial-based cost-effectiveness analysis

Missing data are a common issue in cost-effectiveness analysis (CEA) alongside randomised trials and are often addressed assuming the data are 'missing at random'. However, this assumption is often questionable, and sensitivity analyses are required to assess the implications of departures from missing at random. Reference-based multiple imputation provides an attractive approach for conducting such sensitivity analyses, because missing data assumptions are framed in an intuitive way by making reference to other trial arms. For example, a plausible not at random mechanism in a placebo-controlled trial would be to assume that participants in the experimental arm who dropped out stop taking their treatment and have similar outcomes to those in the placebo arm. Drawing on the increasing use of this approach in other areas, this paper aims to extend and illustrate the reference-based multiple imputation approach in CEA. It introduces the principles of reference-based imputation and proposes an extension to the CEA context. The method is illustrated in the CEA of the CoBalT trial evaluating cognitive behavioural therapy for treatment-resistant depression. Stata code is provided. We find that reference-based multiple imputation provides a relevant and accessible framework for assessing the robustness of CEA conclusions to different missing data assumptions.

Effect of Thoracoscopic Talc Poudrage vs Talc Slurry via Chest Tube on Pleurodesis Failure Rate Among Patients With Malignant Pleural Effusions: A Randomized Clinical Trial

IMPORTANCE

Malignant pleural effusion (MPE) is challenging to manage. Talc pleurodesis is a common and effective treatment. There are no reliable data, however, regarding the optimal method for talc delivery, leading to differences in practice and recommendations.

OBJECTIVE

To test the hypothesis that administration of talc poudrage during thoracoscopy with local anesthesia is more effective than talc slurry delivered via chest tube in successfully inducing pleurodesis.

DESIGN, SETTING, AND PARTICIPANTS

Open-label, randomized clinical trial conducted at 17 UK hospitals. A total of 330 participants were enrolled from August 2012 to April 2018 and followed up until October 2018. Patients were eligible if they were older than 18 years, had a confirmed diagnosis of MPE, and could undergo thoracoscopy with local anesthesia. Patients were excluded if they required a thoracoscopy for diagnostic purposes or had evidence of nonexpandable lung.

INTERVENTIONS

Patients randomized to the talc poudrage group (n = 166) received 4 g of talc poudrage during thoracoscopy while under moderate sedation, while patients randomized to the control group (n = 164) underwent bedside chest tube insertion with local anesthesia followed by administration of 4 g of sterile talc slurry.

MAIN OUTCOMES AND MEASURES

The primary outcome was pleurodesis failure up to 90 days after randomization. Secondary outcomes included pleurodesis failure at 30 and 180 days; time to pleurodesis failure; number of nights spent in the hospital over 90 days; patient-reported thoracic pain and dyspnea at 7, 30, 90, and 180 days; health-related quality of life at 30, 90, and 180 days; all-cause mortality; and percentage of opacification on chest radiograph at drain removal and at 30, 90, and 180 days.

RESULTS

Among 330 patients who were randomized (mean age, 68 years; 181 [55%] women), 320 (97%) were included in the primary outcome analysis. At 90 days, the pleurodesis failure rate was 36 of 161 patients (22%) in the talc poudrage group and 38 of 159 (24%) in the talc slurry group (adjusted odds ratio, 0.91 [95% CI, 0.54-1.55]; P = .74; difference, -1.8% [95% CI, -10.7% to 7.2%]). No statistically significant differences were noted in any of the 24 prespecified secondary outcomes.

CONCLUSIONS AND RELEVANCE

Among patients with malignant pleural effusion, thoracoscopic talc poudrage, compared with talc slurry delivered via chest tube, resulted in no significant difference in the rate of pleurodesis failure at 90 days. However, the study may have been underpowered to detect small but potentially important differences.

TRIAL REGISTRATION

ISRCTN Identifier: ISRCTN47845793.

Reporting of randomized factorial trials was frequently inadequate

OBJECTIVES

Factorial designs can allow efficient evaluation of multiple treatments within a single trial. We evaluated the design, analysis, and reporting in a sample of factorial trials.

STUDY DESIGN AND SETTING

Review of 2 × 2 factorial trials evaluating health-related interventions and outcomes in humans. Using Medline, we identified articles published between January 2015 and March 2018. We randomly selected 100 articles for inclusion.

RESULTS

Most trials (78%) did not provide a rationale for using a factorial design. Only 63 trials (63%) assessed the interaction for the primary outcome, and 39/63 (62%) made a further assessment for at least one secondary outcome. 12/63 trials (19%) identified a significant interaction for the primary outcome and 16/39 trials (41%) for at least one secondary outcome. Inappropriate methods of analysis to protect against potential negative effects from interactions were common, with 18 trials (18%) choosing the analysis method based on a preliminary test for interaction, and 13% (n = 10/75) of those conducting a factorial analysis including an interaction term in the model.

CONCLUSION

Reporting of factorial trials was often suboptimal, and assessment of interactions was poor. Investigators often used inappropriate methods of analysis to try to protect against adverse effects of interactions.

A Phase 2a, Multicenter, Randomized, Double-Blind, Parallel-Group, Placebo-Controlled Trial of IBD98-M Delayed-Release Capsules to Induce Remission in Patients with Active and Mild to Moderate Ulcerative Colitis

IBD98-M is a delayed-release formulation of mesalamine (mesalazine) and SH with a potential therapeutic role in ulcerative colitis (UC). A total of 51 patients with a modified Ulcerative Colitis Disease Activity Index (UCDAI) score of ≥4 and ≤10, and a modified UCDAI endoscopy subscore ≥1 were randomized for 6 weeks of double-blind treatment with IBD98 0.8 g/day or IBD 1.2 g/day or placebo. The efficacy and safety of IBD98-M in mild to moderate active UC were primarily evaluated. At week 6, 1 (5.9%), 2 (12.5%), and 2 (11.1%) patients receiving IBD98-M 0.8 g, IBD98-M 1.2 g, and placebo, respectively, (p > 0.999) achieved clinical remission. Higher clinical response was seen in IBD98-M 1.2 g (31.3%) versus placebo (16.7%) and endoscopic improvement in IBD98-M 0.8 g (29.4%) versus placebo (22.2%) was seen. Fecal calprotectin levels were reduced in IBD98-M groups versus placebo (p > 0.05). IBD98-M patients achieved significant improvement in physical health summary score component of the SF-36 (p = 0.01 and p = 0.03 respectively) compared to placebo. IBD98-M did not meet the primary end point but had higher clinical response (1.2 g/day) and endoscopic improvement (0.8 g/day) compared to placebo. The safety result shown that IBD98-M treatment was safe and well tolerated in this patient population. No new safety signals or unexpected safety findings were observed during the study. Further trials with different stratification and longer follow-up may be needed to evaluate the efficacy.

Sleep, nutrition, and physical activity interventions to prevent obesity in infancy: follow-up of the Prevention of Overweight in Infancy (POI) randomized controlled trial at ages 3.5 and 5 y

Background

Our Prevention of Overweight in Infancy (POI) study suggested that a brief sleep intervention in infancy reduced the risk of obesity at age 2 y. In contrast, we observed no benefit from the nutrition and activity intervention.

Objective

The objective of the study was to determine how these interventions influenced growth at ages 3.5 and 5 y compared with usual care (Control).

Design

A follow-up of a parallel, 4-arm, single-blind, 2-y, randomized controlled trial in 802 women (86% European, 48% primiparous) recruited in pregnancy (58% response rate) was undertaken. All groups received standard Well-Child care with additional support for 3 intervention groups: FAB (promotion of breastfeeding, healthy eating, physical activity: 8 contacts, antenatal, 18 mo); Sleep (prevention of sleep problems: antenatal, 3 wk); Combination (both interventions). Follow-up measures were collected by staff blinded to group allocation. The primary outcome was child body mass index (BMI) z score, and secondary outcomes were prevalence of obesity (BMI ≥95th percentile), self-regulation (psychological measures), sleep, physical activity (accelerometry, questionnaires), and dietary intake (food-frequency questionnaire). Analyses were conducted through the use of multiple imputation.

Results

Retention was 77% at age 3.5 y and 69% at age 5 y. Children in the FAB group had significantly higher BMI z scores than did Controls at age 5 y (adjusted difference: 0.25; 95% CI: 0.04, 0.47) but not at age 3.5 y (0.15; 95% CI: -0.04, 0.34). Children who received the Sleep intervention (Sleep and Combination groups) had significantly lower BMI z scores at age 3.5 y (-0.24; 95% CI: -0.38, -0.10) and at age 5 y (-0.23; 95% CI: -0.38, -0.07) than children who did not (Control and FAB groups).

Conclusions

A conventional intervention had unexpected adverse long-term weight outcomes, whereas positive outcomes from a less conventional sleep intervention remained promising at age 5 y. More intensive or extended sleep intervention might have larger or longer-lasting effects and should be investigated. This trial was registered at clinicaltrials.gov as NCT00892983.

Randomized clinical trials and observational studies in the assessment of drug safety

Randomized clinical trials are considered as the preferred design to assess the potential causal relationships between drugs or other medical interventions and intended effects. For this reason, randomized clinical trials are generally the basis of development programs in the life cycle of drugs and the cornerstone of evidence-based medicine. Instead, randomized clinical trials are not the design of choice for the detection and assessment of rare, delayed and/or unexpected effects related to drug safety. Moreover, the highly homogeneous populations resulting from restrictive eligibility criteria make randomized clinical trials inappropriate to describe comprehensively the safety profile of drugs. In that context, observational studies have a key added value when evaluating the benefit-risk balance of the drugs. However, observational studies are more prone to bias than randomized clinical trials and they have to be designed, conducted and reported judiciously. In this article, we discuss the strengths and limitations of randomized clinical trials and of observational studies, more particularly regarding their contribution to the knowledge of medicines' safety profile. In addition, we present general recommendations for the sensible use of observational data.

When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts

Background

Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the missingness. Therefore, the analysis of trial data with missing values requires careful planning and attention.

Methods

The authors had several meetings and discussions considering optimal ways of handling missing data to minimise the bias potential. We also searched PubMed (key words: missing data; randomi*; statistical analysis) and reference lists of known studies for papers (theoretical papers; empirical studies; simulation studies; etc.) on how to deal with missing data when analysing randomised clinical trials.

Results

Handling missing data is an important, yet difficult and complex task when analysing results of randomised clinical trials. We consider how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommend analytical approaches which may prevent bias caused by unavoidable missing data. We consider the strengths and limitations of using of best-worst and worst-best sensitivity analyses, multiple imputation, and full information maximum likelihood. We also present practical flowcharts on how to deal with missing data and an overview of the steps that always need to be considered during the analysis stage of a trial.

Conclusions

We present a practical guide and flowcharts describing when and how multiple imputation should be used to handle missing data in randomised clinical.

Electronic supplementary material

The online version of this article (10.1186/s12874-017-0442-1) contains supplementary material, which is available to authorized users.

Non-inferiority trials: are they inferior? A systematic review of reporting in major medical journals

OBJECTIVE

To assess the adequacy of reporting of non-inferiority trials alongside the consistency and utility of current recommended analyses and guidelines.

DESIGN

Review of randomised clinical trials that used a non-inferiority design published between January 2010 and May 2015 in medical journals that had an impact factor >10 (JAMA Internal Medicine, Archives Internal Medicine, PLOS Medicine, Annals of Internal Medicine, BMJ, JAMA, Lancet and New England Journal of Medicine).

DATA SOURCES

Ovid (MEDLINE).

METHODS

We searched for non-inferiority trials and assessed the following: choice of non-inferiority margin and justification of margin; power and significance level for sample size; patient population used and how this was defined; any missing data methods used and assumptions declared and any sensitivity analyses used.

RESULTS

A total of 168 trial publications were included. Most trials concluded non-inferiority (132; 79%). The non-inferiority margin was reported for 98% (164), but less than half reported any justification for the margin (77; 46%). While most chose two different analyses (91; 54%) the most common being intention-to-treat (ITT) or modified ITT and per-protocol, a large number of articles only chose to conduct and report one analysis (65; 39%), most commonly the ITT analysis. There was lack of clarity or inconsistency between the type I error rate and corresponding CIs for 73 (43%) articles. Missing data were rarely considered with (99; 59%) not declaring whether imputation techniques were used.

CONCLUSIONS

Reporting and conduct of non-inferiority trials is inconsistent and does not follow the recommendations in available statistical guidelines, which are not wholly consistent themselves. Authors should clearly describe the methods used and provide clear descriptions of and justifications for their design and primary analysis. Failure to do this risks misleading conclusions being drawn, with consequent effects on clinical practice.

Improving the self-management of chronic pain: COping with persistent Pain, Effectiveness Research in Self-management (COPERS)

Background

Chronic musculoskeletal pain is a common problem that is difficult to treat. Self-management support interventions may help people to manage this condition better; however, there is limited evidence showing that they improve clinical outcomes. Our overarching research question was ‘Does a self-management support programme improve outcomes for people living with chronic musculoskeletal pain?’.

Aim

To develop, evaluate and test the clinical effectiveness and cost-effectiveness of a theoretically grounded self-management support intervention for people living with chronic musculoskeletal pain.

Methods

In phase 1 we carried out two systematic reviews to synthesise the evidence base for self-management course content and delivery styles likely to help those with chronic pain. We also considered the psychological theories that might underpin behaviour change and pain management principles. Informed by these data we developed the Coping with persistent Pain, Evaluation Research in Self-management (COPERS) intervention, a group intervention delivered over 3 days with a top-up session after 2 weeks. It was led by two trained facilitators: a health-care professional and a layperson with experience of chronic pain. To ensure that we measured the most appropriate outcomes we reviewed the literature on potential outcome domains and measures and consulted widely with patients, tutors and experts. In a feasibility study we demonstrated that we could deliver the COPERS intervention in English and, to increase the generalisability of our findings, also in Sylheti for the Bangladeshi community. In phase 2 we ran a randomised controlled trial to test the clinical effectiveness and cost-effectiveness of adding the COPERS intervention to a best usual care package (usual care plus a relaxation CD and a pain toolkit leaflet). We recruited adults with chronic musculoskeletal pain largely from primary care and musculoskeletal physiotherapy services in two localities: east London and Coventry/Warwickshire. We collected follow-up data at 12 weeks (self-efficacy only) and 6 and 12 months. Our primary outcome was pain-related disability (Chronic Pain Grade disability subscale) at 12 months. We also measured costs, health utility (European Quality of Life-5 Dimensions), anxiety, depression [Hospital Anxiety and Depression Scale (HADS)], coping, pain acceptance and social integration. Data on the use of NHS services by participants were extracted from NHS electronic records.

Results

We recruited 703 participants with a mean age of 60 years (range 19–94 years); 81% were white and 67% were female. Depression and anxiety symptoms were common, with mean HADS depression and anxiety scores of 7.4 [standard deviation (SD) 4.1] and 9.2 (SD 4.6), respectively. Intervention participants received 85% of the course content. At 12 months there was no difference between treatment groups in our primary outcome of pain-related disability [difference –1.0 intervention vs. control, 95% confidence interval (CI) –4.9 to 3.0]. However, self-efficacy, anxiety, depression, pain acceptance and social integration all improved more in the intervention group at 6 months. At 1 year these differences remained for depression (–0.7, 95% CI –1.2 to –0.2) and social integration (0.8, 95% CI, 0.4 to 1.2). The COPERS intervention had a high probability (87%) of being cost-effective compared with usual care at a threshold of £30,000 per quality-adjusted life-year.

Conclusions

Although the COPERS intervention did not affect our primary outcome of pain-related disability, it improved psychological well-being and is likely to be cost-effective according to current National Institute for Health and Care Excellence criteria. The COPERS intervention could be used as a substitute for less well-evidenced (and more expensive) pain self-management programmes. Effective interventions to improve hard outcomes in chronic pain patients, such as disability, are still needed.

Trial registration

Current Controlled Trials ISRCTN22714229.

Funding

The project was funded by the National Institute for Health Research Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 4, No. 14. See the NIHR Journals Library website for further project information.

Comments on the three papers by the FDA/CDER research team on the regulatory perspective of the missing data problem

This communication comments on the three papers by the FDA CDER research team on the regulatory perspective of the missing data problem. The focus is on two topics: causal estimand and sensitivity analysis. Copyright © 2016 John Wiley & Sons, Ltd.

Best practice for analysis of shared clinical trial data

Background

Greater transparency, including sharing of patient-level data for further research, is an increasingly important topic for organisations who sponsor, fund and conduct clinical trials. This is a major paradigm shift with the aim of maximising the value of patient-level data from clinical trials for the benefit of future patients and society. We consider the analysis of shared clinical trial data in three broad categories: (1) reanalysis - further investigation of the efficacy and safety of the randomized intervention, (2) meta-analysis, and (3) supplemental analysis for a research question that is not directly assessing the randomized intervention.

Discussion

In order to support appropriate interpretation and limit the risk of misleading findings, analysis of shared clinical trial data should have a pre-specified analysis plan. However, it is not generally possible to limit bias and control multiplicity to the extent that is possible in the original trial design, conduct and analysis, and this should be acknowledged and taken into account when interpreting results. We highlight a number of areas where specific considerations arise in planning, conducting, interpreting and reporting analyses of shared clinical trial data. A key issue is that that these analyses essentially share many of the limitations of any post hoc analyses beyond the original specified analyses. The use of individual patient data in meta-analysis can provide increased precision and reduce bias. Supplemental analyses are subject to many of the same issues that arise in broader epidemiological analyses. Specific discussion topics are addressed within each of these areas.

Summary

Increased provision of patient-level data from industry and academic-led clinical trials for secondary research can benefit future patients and society. Responsible data sharing, including transparency of the research objectives, analysis plans and of the results will support appropriate interpretation and help to address the risk of misleading results and avoid unfounded health scares.

Bias was reduced in an open-label trial through the removal of subjective elements from the outcome definition

OBJECTIVE

To determine whether modifying an outcome definition to remove subjective elements reduced bias in a trial that could not use blinded outcome assessment.

STUDY DESIGN AND SETTING

Reanalysis of an open-label trial comparing a restrictive vs. liberal transfusion strategy for gastrointestinal bleeding. The usual definition of the primary outcome, further bleeding, allows subjective clinical symptoms to be used alone for diagnosis, whereas the definition used in the trial required more objective confirmation by endoscopy. We compared treatment effect estimates for these two definitions.

RESULTS

Fewer subjective symptom-identified events were confirmed using more objective methods in the restrictive arm (18%) than in the liberal arm (56%), indicating differential assessment between arms. An analysis using all events (both subjective and more objective) led to an odds ratio of 0.83 (95% confidence interval [CI]: 0.50-1.37). When only events confirmed using more objective methods were included, the odds ratio was 0.50 (95% CI: 0.32-0.78). The ratio of the odds ratios was 1.66, indicating that including unconfirmed events in the definition biased the treatment effect upward by 66%.

CONCLUSION

Modifying the outcome definition to exclude subjective elements substantially reduced bias. This may be a useful strategy for reducing bias in trials that cannot blind outcome assessment.

Strength-balance supplemented with computerized cognitive training to improve dual task gait and divided attention in older adults: a multicenter randomized-controlled trial

BACKGROUND

Exercise interventions often do not combine physical and cognitive training. However, this combination is assumed to be more beneficial in improving walking and cognitive functioning compared to isolated cognitive or physical training.

METHODS

A multicenter parallel randomized controlled trial was conducted to compare a motor to a cognitive-motor exercise program. A total of 182 eligible residents of homes-for-the-aged (n = 159) or elderly living in the vicinity of the homes (n = 23) were randomly assigned to either strength-balance (SB) or strength-balance-cognitive (SBC) training. Both groups conducted similar strength-balance training during 12 weeks. SBC additionally absolved computerized cognitive training. Outcomes were dual task costs of walking, physical performance, simple reaction time, executive functions, divided attention, fear of falling and fall rate. Participants were analysed with an intention to treat approach.

RESULTS

The 182 participants (mean age ± SD: 81.5 ± 7.3 years) were allocated to either SB (n = 98) or SBC (n = 84). The attrition rate was 14.3%. Interaction effects were observed for dual task costs of step length (preferred walking speed: F(1,174) = 4.94, p = 0.028, η2 = 0.027, fast walking speed: F(1,166) = 6.14, p = 0.009, η2 = 0.040) and dual task costs of the standard deviation of step length (F(1,166) = 6.14, p = 0.014, η2 = 0.036), in favor of SBC. Significant interactions in favor of SBC revealed for in gait initiation (F(1,166) = 9.16, p = 0.003, η2 = 0.052), 'reaction time' (F(1,180) = 5.243, p = 0.023, η² = 0.028) & 'missed answers' (F(1,180) = 11.839, p = 0.001, η² = 0.062) as part of the test for divided attention. Within-group comparison revealed significant improvements in dual task costs of walking (preferred speed; velocity (p = 0.002), step time (p = 0.018), step length (p = 0.028), fast speed; velocity (p < 0.001), step time (p = 0.035), step length (p = 0.001)), simple reaction time (p < 0.001), executive functioning (Trail making test B; p < 0.001), divided attention (p < 0.001), fear of falling (p < 0.001), and fall rate (p < 0.001).

CONCLUSIONS

Combining strength-balance training with specific cognitive training has a positive additional effect on dual task costs of walking, gait initiation, and divided attention. The findings further confirm previous research showing that strength-balance training improves executive functions and reduces falls.

TRIAL REGISTRATION

This trial has been registered under ISRCTN75134517.

Detailed statistical analysis plan for the Danish Palliative Care Trial (DanPaCT)

BACKGROUND

Advanced cancer patients experience considerable symptoms, problems, and needs. Early referral of these patients to specialized palliative care (SPC) could offer improvements. The Danish Palliative Care Trial (DanPaCT) investigates whether patients with metastatic cancer will benefit from being referred to 'early SPC'. DanPaCT is a multicenter, parallel-group, superiority clinical trial with 1:1 randomization. The planned sample size was 300 patients. The primary data collection for DanPaCT is finished. To prevent outcome reporting bias, selective reporting, and data-driven results, we present a detailed statistical analysis plan (SAP) for DanPaCT here.

RESULTS

This SAP provides detailed descriptions of the statistical analyses of the primary and secondary outcomes in DanPaCT. The primary outcome is the change in the patient's 'primary need'. The 'primary need' is a patient-individualised outcome representing the score of the symptom or problem that had the highest intensity out of seven at baseline assessed with the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Secondary outcomes are the seven scales that are represented in the primary outcome, but each scale evaluated individually for all patients, and survival. The detailed description includes chosen significance levels, models for multiple imputations, sensitivity analyses and blinding. In addition, we discuss the patient-individualized primary outcome, blinding, missing data, multiplicity and the risk of bias.

CONCLUSIONS

Only few trials have investigated the effects of SPC. To our knowledge DanPaCT is the first trial to investigate screening based 'early SPC' for patients with metastatic cancer from a broad spectrum of cancer diagnosis.

TRIAL REGISTRATION

Clinicaltrials.gov identifier: NCT01348048 (May 2011).

Coping with persistent pain, effectiveness research into self-management (COPERS): statistical analysis plan for a randomised controlled trial

BACKGROUND

The Coping with Persistent Pain, Effectiveness Research into Self-management (COPERS) trial assessed whether a group-based self-management course is effective in reducing pain-related disability in participants with chronic musculoskeletal pain. This article describes the statistical analysis plan for the COPERS trial.

METHODS AND DESIGN

COPERS was a pragmatic, multicentre, unmasked, parallel group, randomised controlled trial. This article describes (a) the overall analysis principles (including which participants will be included in each analysis, how results will be presented, which covariates will be adjusted for, and how we will account for clustering in the intervention group); (b) the primary and secondary outcomes, and how each outcome will be analysed; (c) sensitivity analyses; (d) subgroup analyses; and (e) adherence-adjusted analyses.

TRIAL REGISTRATION

ISRCTN24426731.