Missing data in clinical trials: from clinical assumptions to statistical analysis using pattern mixture models

2013 ◽  
Vol 12 (6) ◽  
pp. 337-347 ◽  
Author(s):  
Bohdana Ratitch ◽  
Michael O'Kelly ◽  
Robert Tosiello
Author(s):  
Andrea B. Troxel ◽  
Diane L. Fairclough ◽  
Desmond Curran ◽  
Elizabeth A. Hahn

2020 ◽  
Author(s):  
Lida Feyz ◽  
Yale Wang ◽  
Atul Pathak ◽  
Manish Saxena ◽  
Felix Mahfoud ◽  
...  

BACKGROUND Great and costly efforts are required to recruit potential participants into clinical trials. Using social media may make the recruitment process more efficient. Merely 20% of clinical trials are completed on time, a finding mostly linked to challenges in patient recruitment [1]. Recruitment through social media is increasingly being recognized as a tool to efficiently identify eligible subjects at lower costs [2, 3]. One of the key reasons for its success is the strong adherence of users to specific social media platforms. Facebook for instance has over 2.38 billion active monthly users of which about 75% access the network on a daily basis [4]. As such, the platform and other like it offer great potential to quickly and affordably enroll patients into clinical trials and surveys [3, 5-7]. At present, little evidence is available on the efficacy of using social media to recruit patients into cardiovascular and hypertension trials [8]. The aim of the present study was to evaluate the efficacy of social media as an approach to recruit hypertensive patients into the RADIANCE-HTN SOLO trial. OBJECTIVE The aim of the present study was to evaluate the efficacy of social media as an approach to recruit hypertensive patients into the RADIANCE-HTN SOLO trial. METHODS The RADIANCE-HTN SOLO (NCT02649426) is a multicenter, randomised study that was designed to demonstrate the efficacy and safety of endovascular ultrasound renal denervation (RDN) to reduce ambulatory blood pressure at 2 months in patients with combined systolic–diastolic hypertension in the absence of medications. Between March 28, 2016, and Dec 28, 2017, 803 patients were screened for eligibility and 146 were randomised to undergo RDN (n=74) or a sham procedure (n=72) [9]. Key entry criteria included: age 18-75 years with essential hypertension using 0-2 antihypertensive drugs. Patients were recruited from 21 hospitals in the USA and 18 hospitals in Europe. The study was approved by local ethics committees or institutional review boards and was performed in accordance with the declaration of Helsinki. All participants provided written informed consent. All recruitment materials including social media campaigns was approved by local ethics committees of the involved sites. Recruitment strategies included social media (Facebook), conventional advertisements (ads) (magazine, brochure/poster, radio, newspaper), web search (the clinical website, craigslist and web-browsing), and physician referral. Both newspaper ads and posters contained brief information about study entry criteria. Newspapers were distributed at public transport places and posters were displayed in outpatient cardiology and hypertension clinics. Radio ads were run for 30 or 60 seconds providing a short summary of the study, entry criteria and contact information. Ads were run in major metropolitan areas on radio stations with large adult listener bases during popular days and times. Facebook ads were targeted towards subjects >45 years old within a certain distance from a recruitment site (range 20-50 miles). Criteria were modified over time in order to increase response rates [i.e. distance was increased or decreased, age was increased to >55 year]. Facebook ads referred to a dedicated study website translated into country specific languages. If interested, subjects could complete an anonymous online screening questionnaire which provided direct automatic feedback on study eligibility. Eligible subjects were asked to provide contact details (name and telephone number) to receive additional information, a process coordinated via a secure online portal (Galen Gateway Patient Recruitment Portal, Galen Patient Recruitment, Inc., Cumberland, RI). Study site were only able to contact potential candidates within their area. The study sponsor was not able to access any personal data. Trained local site personnel or contracted secondary screeners contacted candidates by phone to verify eligibility and answer potential questions. A subsequent outpatient clinic visit was scheduled during which the study was explained in greater detail and the informed consent form could be signed. Statistical analysis Categorical variables were expressed as percentages and counts. Continuous variables were described as mean  standard deviation (SD) when normally distributed, data was compared using an Independent-Samples or Paired-Samples T test to analyze the difference between recruitment methods. In case of non-normal distribution, median data was presented with the interquartile range [IQR]. All statistical tests are 2-tailed. A P-value <0.05 was considered statistically significant. Statistical analysis was performed using SPSS statistical analysis (version 24.0).   RESULTS Results Facebook ads were active during a 115-day recruitment period between August and November 2017. A total of 285 potential candidates were recruited by different recruitment strategies in this specific time period, of which 184 (65%) were consented through Facebook (Table 1). The average age of the subjects consented through Facebook was 59 ± 8 years and 51% were male (Table 2). Facebook reached 5.3 million people in 168 separate campaigns run in proximity to 19 sites in the US and 14 sites in Europe. The number of candidates per site was variable with a median of 23 [17 – 26] candidates per site that passed the questionnaire (Figure 1). A total of 27/184 subjects were eventually randomised. Total cost for the Facebook ads was $152,412; costing $907/campaign and $0.83/click. This resulted in a total cost of $828/consent. During the same recruitment period, 7-day radio spots were launched with a total cost of $2,870; resulting in 9 inquiries with eventually 5 potential candidates and 2 consents ($1,435/consent).   CONCLUSIONS Conclusion Targeted social media was a successful and efficient strategy to find potential candidates for a multicenter blood pressure clinical trial. Whether this approach can be replicated across other disease states or demographics remains to be studied.


Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.


2016 ◽  
Vol 8 (2) ◽  
pp. 124-135 ◽  
Author(s):  
Mi-Ok Kim ◽  
Xia Wang ◽  
Chunyan Liu ◽  
Kathleen Dorris ◽  
Maryam Fouladi ◽  
...  

1983 ◽  
Vol 21 (6) ◽  
pp. 24.2-24

Published clinical trials form the basis of our clinical information. We refer whenever possible only to trials of adequate size and design, and with appropriate statistical analysis. We prefer to cite articles published in journals which are accessible to our readers and which submit their articles to independent referees before publication. Sometimes the only studies available fall short of these standards. This is common in published proceedings of manufacturer-sponsored symposia (PPMSS). These often appear as supplements to learned journals but are seldom vetted as rigorously as articles published in the regular issues of the same journal. In future we will indicate any PPMSS publications we cite by an S before the reference number in the list of references.


Author(s):  
Zachary R. McCaw ◽  
Hanna Julienne ◽  
Hugues Aschard

AbstractAlthough missing data are prevalent in applications, existing implementations of Gaussian mixture models (GMMs) require complete data. Standard practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased and unstable parameter estimates. Here we present MGMM, an R package for fitting GMMs in the presence of missing data. Using three case studies on real and simulated data sets, we demonstrate that, when the underlying distribution is near-to a GMM, MGMM is more effective at recovering the true cluster assignments than state of the art imputation followed by standard GMM. Moreover, MGMM provides an accurate assessment of cluster assignment uncertainty even when the generative distribution is not a GMM. This assessment may be used to identify unassignable observations. MGMM is available as an R package on CRAN: https://CRAN.R-project.org/package=MGMM.


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