Novel statistical approaches and applications in leveraging real-world data in regulatory clinical studies

Li, Heng; Chen, Wei Chen; Lu, Nelson; Wang, Chenguang; Tiwari, Ram C.; Xu, Yunling; Yue, Lilly Q.

doi:10.1007/s10742-020-00218-4

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…Besides suboptimal interim decision‐making, other potential disadvantages of adaptive designs are an increase in false‐positive findings and bias as well as a negative impact on trial integrity and interpretation of trial results. As our interim decision rule does not use any outcome data (showing some similarities to recent proposals for regulatory clinical studies using external data 26 ) the result of the interim analysis does not provide a negative impact. However, there is an interim analysis and our assumption is that populations are comparable between both stages.…”

Section: Discussionmentioning

confidence: 76%

An adaptive design for early clinical development including interim decision for single‐arm trial with external controls or randomized trial

Götte

Kirchner

Krisam

et al. 2022

Pharmaceutical Statistics

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In early clinical development, randomized controlled trials (RCT) or single‐arm trials with external controls (SATwEC) are design options, which allow adjustment for confounding: RCT via design, SATwEC via analysis using propensity score methods. SATwEC requires less investment than RCT. However, if the confounder space substantially differs between the experimental and external control group, the SATwEC might lead to inappropriate decisions for further development. We develop an adaptive two‐stage design (ATD) for early clinical development that reduces the risk of unreliable decision‐making at the end of a SATwEC. In Stage I, subjects are solely assigned to the experimental group. If at the interim the propensity score distributions of internal and external data are comparable based on the preference score, the subjects in stage II will again be solely assigned to the experimental arm; if not, a randomized stage II will be conducted. In a simulation study guided by a motivating example, data is generated using a time‐to‐event model with observable and unobservable confounders. The confounder space is varied to investigate the impact on false go/stop probabilities as well as a loss function, which reflects the quality of treatment effect estimates and decision‐making. The proposed ATD provides a compromise between optimizing quality (as expressed by false go/stop probabilities and the loss function) and investment (defined by sample size and trial duration).

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Section: Discussionmentioning

confidence: 76%

An adaptive design for early clinical development including interim decision for single‐arm trial with external controls or randomized trial

Götte

Kirchner

Krisam

et al. 2022

Pharmaceutical Statistics

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show abstract

“…Regulatory agencies are well aware of the relevance of the propensity weighting methodology for insuring comparability of treatment arms, mainly in the analysis of observational studies [ 34 , 35 ]. On this basis, we believe that there are valid methodological reasons for the regulatory agencies to consider the extension of the propensity methodology in RCTs in CNS as a reference analysis suitable to control the unknown potential baseline unbalance in the distribution of the propensity to non-specific placebo response.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo

Goméni¹,

Bressolle-Gomeni²,

Fava

2023

Transl Psychiatry

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Treatment effect in clinical trials for major depressive disorders (RCT) can be viewed as the resultant of treatment specific and non-specific effects. Baseline individual propensity to respond non-specifically to any treatment or intervention can be considered as a major non-specific confounding effect. The greater is the baseline propensity, the lower will be the chance to detect any treatment-specific effect. The statistical methodologies currently applied for analyzing RCTs doesn’t account for potential unbalance in the allocation of subjects to treatment arms due to heterogenous distributions of propensity. Hence, the groups to be compared may be imbalanced, and thus incomparable. Propensity weighting methodology was used to reduce baseline imbalances between arms. A randomized, double-blind, placebo controlled, three arms, parallel group, 8-week, fixed-dose study to evaluate efficacy of paroxetine CR 12.5 and 25 mg/day is presented as a cases study. An artificial intelligence model was developed to predict placebo response at week 8 in subjects assigned to placebo arm using changes from screening to baseline of individual Hamilton Depression Rating Scale items. This model was used to predict the probability to respond to placebo in each subject. The inverse of the probability was used as weight in the mixed-effects model applied to assess treatment effect. The analysis with and without propensity weight indicated that the weighted analysis provided an estimate of treatment effect and effect-size about twice larger than the non-weighted analysis. Propensity weighting provides an unbiased strategy to account for heterogeneous and uncontrolled placebo effect making patients’ data comparable across treatment arms.

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“…An example of the vital role played by statisticians in harnessing the power of RWD and RWE is displayed in the work of Li et al ( 2020 ), who discuss propensity score-based approaches for leveraging patients from a RWD source to construct a control group for a nonrandomized comparative study or to augment a single-arm or randomized prospective clinical study. The use of propensity score methodology is key here, since it separates the study design from the outcome analysis, which is essential in regulatory settings.…”

Section: Real-world Data and Real-world Evidencementioning

confidence: 99%

Guest Editorial: Articles selected from the 2020 International Conference on Health Policy Statistics

Crespi¹,

Harel

2021

Health Serv Outcomes Res Method

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With the theme "Leveraging Data to Shape the Future", the conference featured an ambitious program covering a broad range of topics (https ://ww2.amsta t.org/meeti ngs/ichps /2020/onlin eprog ram/index .cfm). Conference speakers were invited to submit manuscripts for selection into a special issue of Health Services and Outcomes Research Methodology, with us (OH and CC) serving as guest coeditors. After a peer review process, fifteen articles were selected and are appearing in the December 2020 and March 2021 issues of the journal. These articles showcase the people, methods and applications that characterize the ICHPS conference series.

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Novel statistical approaches and applications in leveraging real-world data in regulatory clinical studies

Cited by 9 publications

References 18 publications

An adaptive design for early clinical development including interim decision for single‐arm trial with external controls or randomized trial

An adaptive design for early clinical development including interim decision for single‐arm trial with external controls or randomized trial

Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo

Guest Editorial: Articles selected from the 2020 International Conference on Health Policy Statistics

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