A randomized, placebo-controlled trial of patient education for acute low back pain (PREVENT Trial): statistical analysis plan.
ABSTRACT: Statistical analysis plans increase the transparency of decisions made in the analysis of clinical trial results. The purpose of this paper is to detail the planned analyses for the PREVENT trial, a randomized, placebo-controlled trial of patient education for acute low back pain.We report the pre-specified principles, methods, and procedures to be adhered to in the main analysis of the PREVENT trial data. The primary outcome analysis will be based on Mixed Models for Repeated Measures (MMRM), which can test treatment effects at specific time points, and the assumptions of this analysis are outlined. We also outline the treatment of secondary outcomes and planned sensitivity analyses. We provide decisions regarding the treatment of missing data, handling of descriptive and process measure data, and blinded review procedures.Making public the pre-specified statistical analysis plan for the PREVENT trial minimizes the potential for bias in the analysis of trial data, and in the interpretation and reporting of trial results.ACTRN12612001180808 (https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12612001180808).
Project description:<h4>Background</h4>Statistical analysis plans describe the planned data management and analysis for clinical trials. This supports transparent reporting and interpretation of clinical trial results. This paper reports the statistical analysis plan for the RESOLVE clinical trial. The RESOLVE trial assigned participants with chronic low back pain to graded sensory-motor precision training or sham-control.<h4>Results</h4>We report the planned data management and analysis for the primary and secondary outcomes. The primary outcome is pain intensity at 18-weeks post randomization. We will use mixed-effects models to analyze the primary and secondary outcomes by intention-to-treat. We will report adverse effects in full. We also describe analyses if there is non-adherence to the interventions, data management procedures, and our planned reporting of results.<h4>Conclusion</h4>This statistical analysis plan will minimize the potential for bias in the analysis and reporting of results from the RESOLVE trial.<h4>Trial registration</h4>ACTRN12615000610538 (https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368619).
Project description:BACKGROUND:Choosing or altering the planned statistical analysis approach after examination of trial data (often referred to as 'p-hacking') can bias the results of randomised trials. However, the extent of this issue in practice is currently unclear. We conducted a review of published randomised trials to evaluate how often a pre-specified analysis approach is publicly available, and how often the planned analysis is changed. METHODS:A review of randomised trials published between January and April 2018 in six leading general medical journals. For each trial, we established whether a pre-specified analysis approach was publicly available in a protocol or statistical analysis plan and compared this to the trial publication. RESULTS:Overall, 89 of 101 eligible trials (88%) had a publicly available pre-specified analysis approach. Only 22/89 trials (25%) had no unexplained discrepancies between the pre-specified and conducted analysis. Fifty-four trials (61%) had one or more unexplained discrepancies, and in 13 trials (15%), it was impossible to ascertain whether any unexplained discrepancies occurred due to incomplete reporting of the statistical methods. Unexplained discrepancies were most common for the analysis model (n?=?31, 35%) and analysis population (n?=?28, 31%), followed by the use of covariates (n?=?23, 26%) and the approach for handling missing data (n?=?16, 18%). Many protocols or statistical analysis plans were dated after the trial had begun, so earlier discrepancies may have been missed. CONCLUSIONS:Unexplained discrepancies in the statistical methods of randomised trials are common. Increased transparency is required for proper evaluation of results.
Project description:BACKGROUND:Patients with ulcerative colitis (UC) often face complex treatment decisions. Although shared decision making (SDM) is considered important, tools to facilitate this are currently lacking for UC. A recent pilot study of a novel Web-based decision aid (DA), my Actively Informed Decision (myAID), has suggested its acceptability and feasibility for informing treatment decisions and facilitating SDM in clinical practice. OBJECTIVE:This paper describes the study protocol of the myAID study to assess the clinical impact of systematic implementation of myAID in routine UC management. METHODS:The myAID study is a multicenter, cluster randomized controlled trial (CRCT) involving 22 Australian sites that will assess the clinical efficacy of routine use of myAID (intervention) against usual care without access to myAID (control) for UC patients. Participating sites (clusters) will be randomly allocated in a 1:1 ratio between the 2 arms. Patients making a new treatment decision beyond 5-aminosalicylate agents will be eligible to participate. Patients allocated to the intervention arm will view myAID at the time of recruitment and have free access to it throughout the study period. The effect of the myAID intervention will be assessed using the results of serial Web-based questionnaires and fecal calprotectin at baseline, 2 months, 6 months, and 12 months. A Web-based questionnaire within 2-4 weeks of referral will determine early change in quality of decision making and anxiety (both arms) and intervention acceptability (intervention arm only). RESULTS:Study recruitment and funding began in October 2016, and recruitment will continue through 2020, for a minimum of 300 study participants at baseline at the current projection. The primary outcome will be health-related quality of life (Assessment of Quality of Life-8D), and secondary outcomes will include patient empowerment, quality of decision making, anxiety, work productivity and activity impairment, and disease activity. In addition, we aim to determine the predictors of UC treatment decisions and outcomes and the cost-effectiveness of implementing myAID in routine practice. Feedback obtained about myAID will be used to determine areas for improvement and barriers to its implementation. Completion of data collection and publication of study results are anticipated in 2021. CONCLUSIONS:myAID is a novel Web-based DA designed to facilitate SDM in UC management. The results of this CRCT will contribute new evidence to the literature in comparing outcomes between patients who routinely access such decision support intervention versus those who do not, across multiple large inflammatory bowel disease centers as well as community-based private practices in Australia. TRIAL REGISTRATION:Australian New Zealand Clinical Trial Registry ACTRN12617001246370 http://anzctr.org.au/Trial/ Registration/TrialReview.aspx?ACTRN=12617001246370. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):DERR1-10.2196/15994.
Project description:The Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial (ART) is an international multicenter randomized pragmatic controlled trial with allocation concealment involving 120 intensive care units in Brazil, Argentina, Colombia, Italy, Poland, Portugal, Malaysia, Spain, and Uruguay. The primary objective of ART is to determine whether maximum stepwise alveolar recruitment associated with PEEP titration, adjusted according to the static compliance of the respiratory system (ART strategy), is able to increase 28-day survival in patients with acute respiratory distress syndrome compared to conventional treatment (ARDSNet strategy).To describe the data management process and statistical analysis plan.The statistical analysis plan was designed by the trial executive committee and reviewed and approved by the trial steering committee. We provide an overview of the trial design with a special focus on describing the primary (28-day survival) and secondary outcomes. We describe our data management process, data monitoring committee, interim analyses, and sample size calculation. We describe our planned statistical analyses for primary and secondary outcomes as well as pre-specified subgroup analyses. We also provide details for presenting results, including mock tables for baseline characteristics, adherence to the protocol and effect on clinical outcomes.According to best trial practice, we report our statistical analysis plan and data management plan prior to locking the database and beginning analyses. We anticipate that this document will prevent analysis bias and enhance the utility of the reported results.ClinicalTrials.gov number, NCT01374022.
Project description:BACKGROUND:Interventions to improve the nutritional status of older adults and the integration of formal and family care systems are critical research areas to improve the independence and health of aging communities and are particularly relevant in the rehabilitation setting. OBJECTIVE:The primary outcome aimed to determine if the FREER (Family in Rehabilitation: EmpowERing Carers for improved malnutrition outcomes) intervention in malnourished older adults during and postrehabilitation improve nutritional status, physical function, quality of life, service satisfaction, and hospital and aged care admission rates up to 3 months postdischarge, compared with usual care. Secondary outcomes evaluated include family carer burden, carer services satisfaction, and patient and carer experiences. This pilot study will also assess feasibility and intervention fidelity to inform a larger randomized controlled trial. METHODS:This protocol is for a mixed-methods two-arm historically-controlled prospective pilot study intervention. The historical control group has 30 participants, and the pilot intervention group aims to recruit 30 patient-carer pairs. The FREER intervention delivers nutrition counseling during rehabilitation, 3 months of postdischarge telehealth follow-up, and provides supportive resources using a novel model of patient-centered and carer-centered nutrition care. The primary outcome is nutritional status measured by the Scored Patient-Generated Subjective Global Assessment Score. Qualitative outcomes such as experiences and perceptions of value will be measured using semistructured interviews followed by thematic analysis. The process evaluation addresses intervention fidelity and feasibility. RESULTS:Recruitment commenced on July 4, 2018, and is ongoing with eight patient-carer pairs recruited at the time of manuscript submission. CONCLUSIONS:This research will inform a larger randomized controlled trial, with potential for translation to health service policies and new models of dietetic care to support the optimization of nutritional status across a continuum of nutrition care from rehabilitation to home. TRIAL REGISTRATION:Australian New Zealand Clinical Trials Registry Number (ACTRN) 12618000338268; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374608&isReview=true (Archived by WebCite at http://www.webcitation.org/74gtZplU2). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):DERR1-10.2196/12647.
Project description:BACKGROUND:Cluster randomized trials (CRTs) are a design used to test interventions where individual randomization is not appropriate. The mixed model for repeated measures (MMRM) is a popular choice for individually randomized trials with longitudinal continuous outcomes. This model's appeal is due to avoidance of model misspecification and its unbiasedness for data missing completely at random or at random. METHODS:We extended the MMRM to cluster randomized trials by adding a random intercept for the cluster and undertook a simulation experiment to investigate statistical properties when data are missing at random. We simulated cluster randomized trial data where the outcome was continuous and measured at baseline and three post-intervention time points. We varied the number of clusters, the cluster size, the intra-cluster correlation, missingness and the data-generation models. We demonstrate the MMRM-CRT with an example of a cluster randomized trial on cardiovascular disease prevention among diabetics. RESULTS:When simulating a treatment effect at the final time point we found that estimates were unbiased when data were complete and when data were missing at random. Variance components were also largely unbiased. When simulating under the null, we found that type I error was largely nominal, although for a few specific cases it was as high as 0.081. CONCLUSIONS:Although there have been assertions that this model is inappropriate when there are more than two repeated measures on subjects, we found evidence to the contrary. We conclude that the MMRM for CRTs is a good analytic choice for cluster randomized trials with a continuous outcome measured longitudinally. TRIAL REGISTRATION:ClinicalTrials.gov, ID: NCT02804698.
Project description:Abstract The objective of the SCALE IBT trial (NCT02963935) was to compare the weight loss of liraglutide 3.0 mg, a medication approved by the Food and Drug Administration for chronic weight management, to placebo, both in combination with 56 weeks of intensive behavior therapy (IBT) (i.e. reduced calorie intake, increased physical activity [max target: 250 mins/week], and 23 counseling sessions). The primary outcomes of the study were assessed in the intention-to-treat sample, regardless of individuals’ medication adherence. The weight loss estimated in the primary analysis, regardless of drug adherence, was 7.5% versus 4.0% for liraglutide 3.0 mg and placebo, respectively, reflecting a treatment difference favoring liraglutide 3.0 mg of 3.5% (95% CI: 1.6%; 5.3%; p=0.0003). In this pre-specified secondary analysis, we sought to determine the expected effect of liraglutide 3.0 mg on weight loss, as compared to placebo, if all randomized individuals had adhered to study drug for 56 weeks. A total of 282 individuals with obesity (BMI ?30 kg/m2) were randomized in a 1:1 ratio to 56 weeks of IBT combined with daily injections of either liraglutide 3.0 mg or placebo. The weight loss, based on the assumption that all individuals adhered to the medication, was estimated using two different approaches. The first approach (mixed model repeated measures; MMRM) estimated the weight loss that would have been achieved if all individuals adhered to the trial drug by utilizing information from individuals still on drug after the point of a given individual’s discontinuation to provide a (counter-factual) weight change as if the individual in question had not discontinued the drug. The second (covariate) approach used a regression model to calculate the weight change of individuals with full adherence to trial drug by including adherence as a moderator of the effect of treatment condition on weight change. The MMRM approach yielded a weight loss difference of 4.6% (95% CI: 2.6%; 6.5%; p<0.0001), and the covariate approach yielded a weight loss difference of 4.6% (95% CI: 2.8%; 6.5%; p<0.0001), with both estimates favoring liraglutide 3.0 mg. As such, there was good agreement between the two statistical approaches for estimating the effect of liraglutide 3.0 mg versus placebo for individuals who adhere to trial product for 56 weeks. The estimated placebo-subtracted weight loss for liraglutide at week 56 of approximately 4.6% in medication-adherent individuals therefore indicates that underlying assumptions are robust. We believe this finding is an important supplement to the study’s primary outcome and can inform practitioners’ expectations when prescribing liraglutide 3.0 mg in combination with IBT for 56 weeks. Supported by Novo Nordisk.
Project description:<h4>Purpose</h4>In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations.<h4>Methods</h4>The new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape. Dose-response modeling, on the other hand, utilizes information across dose arms and describes the drug effect as a function of dose. Drug development in chronic kidney disease (CKD) is complicated by many factors, primarily by the slow progression of the disease and lack of predictive biomarkers. Recently, new approaches and biomarkers are being explored to improve efficiency in CKD drug development. Proteinuria, i.e. urinary albumin-to-creatinine ratio (UACR) is increasingly used in dose finding trials in patients with CKD. We use proteinuria to illustrate the benefits of DR-MMRM.<h4>Results</h4>The DR-MMRM had higher precision than conventional MMRM and less bias than a dose-response model on UACR change from baseline to end-of-study (DR-EOS).<h4>Conclusions</h4>DR-MMRM is a promising method for dose-response analysis.
Project description:We develop a transport formula for predicting overall cumulative vaccine efficacy through time t (VE(t)) to prevent clinically significant infection with a genetically diverse pathogen (e.g., HIV infection) in a new setting for which a Phase III preventive vaccine efficacy trial that would directly estimate VE(t) has not yet been conducted. The formula integrates data from (1) a previous Phase III trial, (2) a Phase I/II immune response biomarker endpoint trial in the new setting where a follow-up Phase III trial is planned, (3) epidemiological data on background HIV infection incidence in the new setting; and (4) genomic epidemiological data on HIV sequence distributions in the previous and new settings. For (1), the randomized vaccine versus placebo Phase III trial yields estimates of vaccine efficacy to prevent particular genotypes of HIV in participant subgroups defined by baseline covariates X and immune responses to vaccination S(1) measured at a fixed time point ? (potential outcomes if assigned vaccine); often one or more immune responses to vaccination are available that modify genotype-specific vaccine efficacy. The formula focuses on subgroups defined by X and S(1) and being at-risk for HIV infection at ? under both the vaccine and placebo treatment assignments. For (2), the Phase I/II trial tests the same vaccine in a new setting, or a refined new vaccine in the same or new setting, and measures the same baseline covariates and immune responses as the original Phase III trial. For (3), epidemiological data in the new setting are used to project overall background HIV infection rates in the baseline covariate subgroups in the planned Phase III trial, hence re-calibrating for HIV incidence differences in the two settings; whereas for (4), data bases of HIV sequences measured from HIV infected individuals are used to re-calibrate for differences in the distributions of the circulating HIV genotypes in the two settings. The transport formula incorporates a user-specified bridging assumption function that measures differences in HIV genotype-specific conditional biological-susceptibility vaccine efficacies in the two settings, facilitating a sensitivity analysis. We illustrate the transport formula with application to HIV Vaccine Trials Network (HVTN) research. One application of the transport formula is to use predicted VE(t) as a rational criterion for ranking a set of candidate vaccines being studied in Phase I/II trials for their priority for down-selection into the follow-up Phase III trial.
Project description:BACKGROUND:Emerging evidence suggests that several factors can impact disease progression in transthyretin amyloid polyneuropathy (ATTR-PN). The present analysis used longitudinal data from Val30Met patients participating in the tafamidis (selective TTR stabilizer) clinical development program to evaluate the impact of baseline neurologic severity on disease progression in ATTR-PN. METHODS:A linear mixed-effects model for repeated measures (MMRM) was constructed using tafamidis and placebo data from the intent-to-treat Val30Met population of the original registration study as well as tafamidis data from the two consecutive open-label extension studies. The second extension study is ongoing, but a prospectively-planned interim analysis involving a cleaned and locked database was conducted (cut-off: December 31, 2014). Val30Met patients are presented by treatment groups as those who received tafamidis during the registration and open-label studies (T-T group), or who received placebo during the registration study and were switched to tafamidis in the open-label studies (P-T group). Neurologic functioning was assessed at baseline and subsequent visits using the Neuropathy Impairment Score-Lower Limbs (NIS-LL). The analysis focused on the disease trajectory over the first 18 months of treatment. RESULTS:The T-T (n?=?64) and P-T (n?=?61) cohorts were predominantly Caucasian and presented with early-stage neurologic disease (mean [standard deviation] baseline NIS-LL values were 8.4 [11.4] and 11.4 [13.5], respectively). The MMRM analysis demonstrated that baseline severity is an independent significant predictor of disease progression in addition to the treatment effect: patients with a lower baseline NIS-LL showed less progression than those with a higher baseline NIS-LL (p?<?0.0001). Neurologic progression in the T-T group was less than in the P-T group across all levels of baseline NIS-LL (p?=?0.0088), and the degree of separation increased over the 18-month period. Similar results were seen with the NIS-LL muscle weakness subscale. CONCLUSIONS:This analysis of patients with Val30Met ATTR-PN demonstrates that neurologic disease progression strongly depends on baseline neurologic severity and illustrates the disease-modifying effect of tafamidis relative to placebo across a range of baseline levels of neurologic severity and treatment durations. These data also underscore the benefit of early diagnosis and treatment with tafamidis in delaying disease progression in ATTR-PN. TRIAL REGISTRATION:NCT00409175 , NCT00791492 and NCT00925002 registered 08 December 2006, 14 November 2008 (retrospectively registered), and 19 June 2009, respectively.