On the Prevention and Analysis of Missing Data in Randomized Clinical Trials: The State of the Art

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.

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Missing Data in Randomized Clinical Trials for Weight Loss: Scope of the Problem, State of the Field, and Performance of Statistical Methods

PLoS ONE ◽

10.1371/journal.pone.0006624 ◽

2009 ◽

Vol 4 (8) ◽

pp. e6624 ◽

Cited By ~ 101

Author(s):

Mai A. Elobeid ◽

Miguel A. Padilla ◽

Theresa McVie ◽

Olivia Thomas ◽

David W. Brock ◽

...

Keyword(s):

Weight Loss ◽

Clinical Trials ◽

Missing Data ◽

Statistical Methods ◽

Randomized Clinical Trials ◽

And Performance ◽

State Of The Field

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Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling

Computers ◽

10.3390/computers9020037 ◽

2020 ◽

Vol 9 (2) ◽

pp. 37 ◽

Cited By ~ 1

Author(s):

Luca Cappelletti ◽

Tommaso Fontana ◽

Guido Walter Di Donato ◽

Lorenzo Di Tucci ◽

Elena Casiraghi ◽

...

Keyword(s):

Deep Learning ◽

Missing Data ◽

State Of The Art ◽

The State ◽

Complex Data ◽

Data Imputation ◽

Genome Sequences ◽

Missing Data Imputation ◽

The Past ◽

Learning Techniques

Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle “complex data”, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences.

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Missing data: Our view of the state of the art.

Psychological Methods ◽

10.1037/1082-989x.7.2.147 ◽

2002 ◽

Vol 7 (2) ◽

pp. 147-177 ◽

Cited By ~ 5646

Author(s):

Joseph L. Schafer ◽

John W. Graham

Keyword(s):

Missing Data ◽

State Of The Art ◽

The State

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34: Improving The Quality of Patient Reported Outcomes in Randomized Clinical Trials by Minimizing Missing Data

Radiotherapy and Oncology ◽

10.1016/s0167-8140(20)30926-9 ◽

2020 ◽

Vol 150 ◽

pp. S18

Author(s):

Michael Palmer ◽

Harriet Richardson ◽

Michael Brundage

Keyword(s):

Clinical Trials ◽

Missing Data ◽

Patient Reported Outcomes ◽

Randomized Clinical Trials ◽

Patient Reported

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Systemic Sclerosis Pathogenesis and Emerging Therapies, beyond the Fibroblast

BioMed Research International ◽

10.1155/2019/4569826 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 16

Author(s):

Andrea Sierra-Sepúlveda ◽

Alexia Esquinca-González ◽

Sergio A. Benavides-Suárez ◽

Diego E. Sordo-Lima ◽

Adrián E. Caballero-Islas ◽

...

Keyword(s):

Clinical Trials ◽

Immune System ◽

Systemic Sclerosis ◽

Autoimmune Disease ◽

State Of The Art ◽

Therapeutic Targets ◽

Organ Damage ◽

The State ◽

Pathogenic Mechanism ◽

Emerging Therapies

Systemic sclerosis (SSc) is a complex rheumatologic autoimmune disease in which inflammation, fibrosis, and vasculopathy share several pathogenic pathways that lead to skin and internal organ damage. Recent findings regarding the participation and interaction of the innate and acquired immune system have led to a better understanding of the pathogenesis of the disease and to the identification of new therapeutic targets, many of which have been tested in preclinical and clinical trials with varying results. In this manuscript, we review the state of the art of the pathogenesis of this disease and discuss the main therapeutic targets related to each pathogenic mechanism that have been discovered so far.

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Next-Generation Gene Therapy for Parkinson’s Disease Using Engineered Viral Vectors

Journal of Parkinson s Disease ◽

10.3233/jpd-212674 ◽

2021 ◽

pp. 1-9

Author(s):

Tomas Björklund ◽

Marcus Davidsson

Keyword(s):

Parkinson’S Disease ◽

Gene Therapy ◽

Parkinson's Disease ◽

Clinical Trials ◽

Genome Editing ◽

Viral Vectors ◽

State Of The Art ◽

The State ◽

Next Generation ◽

Suitable Vector

Recent technological and conceptual advances have resulted in a plethora of exciting novel engineered adeno associated viral (AAV) vector variants. They all have unique characteristics and abilities. This review summarizes the development and their potential in treating Parkinson’s disease (PD). Clinical trials in PD have shown over the last decade that AAV is a safe and suitable vector for gene therapy but that it also is a vehicle that can benefit significantly from improvement in specificity and potency. This review provides a concise collection of the state-of-the-art for synthetic capsids and their utility in PD. We also summarize what therapeutical strategies may become feasible with novel engineered vectors, including genome editing and neuronal rejuvenation.

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Abstract TP33: Statewide Trends in Utilization of Endovascular Treatment and Outcomes: Analysis of Minnesota Hospital Association Data (2014 and 2015)

Stroke ◽

10.1161/str.48.suppl_1.tp33 ◽

2017 ◽

Vol 48 (suppl_1) ◽

Author(s):

Haitham M Hussein ◽

Muhammad A Saleem ◽

Adnan I Qureshi

Keyword(s):

Clinical Trials ◽

Endovascular Treatment ◽

Randomized Clinical Trials ◽

Endovascular Procedures ◽

Healthcare Quality ◽

The State ◽

Utilization Rate ◽

Association Data ◽

Clinical Trial Results ◽

Minnesota Hospital

Background: There is emphasis on understanding the implementation of clinical trial results for clinical practice. The aim of this study is to examine the changes in endovascular procedures utilization after the publication of the recent clinical trials showing benefit of such procedures in patients with acute ischemic stroke (AIS). Methods: Minnesota Hospital Association (MHA) database was used to calculate the state wide utilization rates for two periods: prior to (January 1st-December 31st, 2014) and after (January 1 st -December 31 st , 2015) publication of randomized clinical trials. MHA collects data from 137 member hospitals for monitoring and reporting healthcare quality and cost across the state. Patients with admitting diagnosis of AIS and those who received endovascular treatment were identified using ICD9 or ICD10 codes. Utilization rates for endovascular treatment were calculated monthly, quarterly, and annually. Discharge to home was considered as the measure of none to minimal disability. Results: Of the 13,043 patients admitted with the diagnosis of AIS, 434 patients (mean age 68.5± 25.5 years; 51.2% women) received endovascular treatment. The number of procedures increased from 194 in 2014 to 240 in 2015. Utilization rate was 3.4% in the first quarter of 2014, gradually declined to reach its lowest value (2.6%) in the last quarter of 2014, then steadily increased to reach its peak (4%) in the last quarter of 2015. Procedures performed at comprehensive stroke centers increased from 52% of total procedures in 2014 to 57.5% in 2015 while those performed at primary stroke centers decreased from 22.6% in 2014 to 19.5% in 2015. In 2015, fewer patients had hypertension (50.4% vs. 60.3%; p=0.039) and more patients had chronic kidney disease (28.3% vs. 15.5%; p=0.001). Intracranial hemorrhage and mortality rates were not significantly different between the two years. There was a trend towards higher rate of minimal or no disability at discharge among patients treated in 2015 compared with those treated in 2014 (22.5% versus 18.1%, p=0.25). Conclusion: We observed a state wide increase in rate of utilization of endovascular treatment of AIS patients coinciding with the publication of the results of new clinical trials.

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