Discussion on “Correct and logical causal inference for binary and time‐to‐event outcomes in randomized controlled trials” by Yi Liu, Bushi Wang, Miao Yang, Jianan Hui, Heng Xu, Siyoen Kil, and Jason C. Hsu

2020 ◽  
Author(s):  
Gene Pennello ◽  
Dandan Xu
Keyword(s):  
Causal Inference ◽  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Time To Event ◽  
Randomized Controlled

scholarly journals Best (but oft-forgotten) practices: missing data methods in randomized controlled nutrition trials

2019 ◽  
Vol 109 (3) ◽  
pp. 504-508 ◽  
Author(s):  
Peng Li ◽  
Elizabeth A Stuart
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Causal Inference ◽  
Randomized Controlled Trials ◽  
Multiple Imputation ◽  
Controlled Trials ◽  
Full Information ◽  
Complete Case ◽  
Full Information Maximum Likelihood ◽  
Randomized Controlled

ABSTRACT Missing data ubiquitously occur in randomized controlled trials and may compromise the causal inference if inappropriately handled. Some problematic missing data methods such as complete case (CC) analysis and last-observation-carried-forward (LOCF) are unfortunately still common in nutrition trials. This situation is partially caused by investigator confusion on missing data assumptions for different methods. In this statistical guidance, we provide a brief introduction of missing data mechanisms and the unreasonable assumptions that underlie CC and LOCF and recommend 2 appropriate missing data methods: multiple imputation and full information maximum likelihood.

Research Cycles: Adding More Substance to the Spin

2016 ◽  
Vol 14 (4) ◽  
pp. 1067-1070 ◽  
Author(s):  
Colin Elman ◽  
Colleen Dougherty Burton
Keyword(s):  
Causal Inference ◽  
Randomized Controlled Trials ◽  
Political Science ◽  
Biomedical Research ◽  
Controlled Trials ◽  
Natural Experiments ◽  
Journal Editors ◽  
Randomized Controlled ◽  
Research Activities ◽  
Research Cycle

In sciences such as biomedicine, researchers and journal editors are well aware that progress in answering difficult questions generally requires movement through a research cycle: Research on a topic or problem progresses from pure description, through correlational analyses and natural experiments, to phased randomized controlled trials (RCTs). In biomedical research all of these research activities are valued and find publication outlets in major journals. In political science, however, a growing emphasis on valid causal inference has led to the suppression of work early in the research cycle. The result of a potentially myopic emphasis on just one aspect of the cycle reduces incentives for discovery of new types of political phenomena, and more careful, efficient, transparent, and ethical research practices. Political science should recognize the significance of the research cycle and develop distinct criteria to evaluate work at each of its stages.

scholarly journals A survey of methodologies on causal inference methods in meta-analyses of randomized controlled trials

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Georgios Markozannes ◽  
Georgia Vourli ◽  
Evangelia Ntzani
Keyword(s):  
Causal Inference ◽  
Randomized Controlled Trials ◽  
Meta Analysis ◽  
Controlled Trials ◽  
Generation Process ◽  
Data Generation ◽  
Randomized Controlled ◽  
Data Generation Process ◽  
Meta Analyses ◽  
Inference Methods

Abstract Background Meta-analyses of randomized controlled trials (RCTs) have been considered as the highest level of evidence in the pyramid of the evidence-based medicine. However, the causal interpretation of such results is seldom studied. Methods We systematically searched for methodologies pertaining to the implementation of a causally explicit framework for meta-analysis of randomized controlled trials and discussed the interpretation and scientific relevance of such causal estimands. We performed a systematic search in four databases to identify relevant methodologies, supplemented with hand-search. We included methodologies that described causality under counterfactuals and potential outcomes framework. Results We only identified three efforts explicitly describing a causal framework on meta-analysis of RCTs. Two approaches required individual participant data, while for the last one, only summary data were required. All three approaches presented a sufficient framework under which a meta-analytical estimate is identifiable and estimable. However, several conceptual limitations remain, mainly in regard to the data generation process under which the selected RCTs rise. Conclusions We undertook a review of methodologies on causal inference methods in meta-analyses. Although all identified methodologies provide valid causal estimates, there are limitations in the assumptions regarding the data generation process and sampling of the potential RCTs to be included in the meta-analysis which pose challenges to the interpretation and scientific relevance of the identified causal effects. Despite both causal inference and meta-analysis being extensively studied in the literature, limited effort exists of combining those two frameworks.

scholarly journals An audit strategy for time-to-event outcomes measured with error: Application to five randomized controlled trials in oncology

2013 ◽  
Vol 10 (5) ◽  
pp. 754-760 ◽  
Author(s):  
Lori E Dodd ◽  
Edward L Korn ◽  
Boris Freidlin ◽  
Wenjuan Gu ◽  
Jeffrey S Abrams ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Time To Event ◽  
Randomized Controlled ◽  
Audit Strategy

scholarly journals Frequent fragility of randomized controlled trials for HCC treatment

2020 ◽  
Author(s):  
Zhang Hao ◽  
Li Jingtao ◽  
Wenting Zeng
Keyword(s):  
Systematic Review ◽  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Fragility Index ◽  
Time To Event ◽  
Phase 3 ◽  
Positive Effects ◽  
Randomized Controlled ◽  
Calculation Results ◽  
Positive Results

Abstract Background : The fragility index (FI) of trial results can provide a measure of confidence in the positive effects reported in randomized controlled trials (RCTs). The aim of this study was to calculate the FI of RCTs supporting HCC treatments. Methods : A methodological systematic review of RCTs in HCC treatments was conducted. Two-arm studies with randomized and positive results for a time-to-event outcome were eligible for the FI calculation. Results : A total of 6 trails were included in this analysis. The median FI was 0.5 (IQR 0-10). FI was ≤ 7 in 4 (66.7%) of 6 trials; in those trials the fragility quotient was ≤ 1%. Conclusion : Many phase 3 RCTs supporting HCC treatments have a low FI, which challenges the confidence in concluding the superiority of these drugs over control treatments.

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