Unbiased assessment of treatment effects on disease recurrence and survival in clinical trials

1983 ◽  
Vol 2 (1) ◽  
pp. 41-58 ◽  
Author(s):  
Richard Kay ◽  
Martin Schumacher
Keyword(s):  
Clinical Trials ◽  
Treatment Effects ◽  
Disease Recurrence ◽  
Assessment Of Treatment Effects

Empirical shrinkage estimator for consistency assessment of treatment effects in multi-regional clinical trials

2012 ◽  
Vol 32 (10) ◽  
pp. 1691-1706 ◽  
Author(s):  
Hui Quan ◽  
Mingyu Li ◽  
Weichung Joe Shih ◽  
Soo Peter Ouyang ◽  
Joshua Chen ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Treatment Effects ◽  
Shrinkage Estimator ◽  
Assessment Of Treatment Effects

scholarly journals Beyond “Intent‐to‐treat” and “Per protocol”: Improving assessment of treatment effects in clinical trials through the specification of an estimand

2020 ◽  
Vol 86 (7) ◽  
pp. 1235-1239 ◽  
Author(s):  
Frank Pétavy ◽  
Lorenzo Guizzaro ◽  
Inês Antunes dos Reis ◽  
Steven Teerenstra ◽  
Kit C.B. Roes
Keyword(s):  
Clinical Trials ◽  
Treatment Effects ◽  
Intent To Treat ◽  
Assessment Of Treatment Effects

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

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.

scholarly journals GCT-61. CORRELATION OF PATTERNS OF DISEASE RECURRENCE WITH RADIOTHERAPY TECHNIQUES AND DOSE IN INTRACRANIAL GERM CELL TUMOURS (icGCT): LESSONS FROM THE UK COHORT OF SIOP GCT96 STUDY

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii340-iii340
Author(s):  
Thankamma Ajithkumar ◽  
Henry Mandeville ◽  
Fernando Carceller ◽  
Milind Ronghe ◽  
Tina Foord ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Disease Recurrence ◽  
Germ Cell Tumours ◽  
Prospective Data ◽  
Data Collection And Analysis ◽  
Radiotherapy Techniques ◽  
The Uk ◽  
A Prospective Study ◽  
Patterns Of Recurrence

Abstract BACKGROUND There are global variations in radiotherapy approaches for icGCT. An understanding of patterns of disease recurrence correlated with radiation techniques and doses is important in standardising and improving the quality of radiotherapy using high-precision techniques. METHODS AND RESULTS Data from 20 patients with tumour recurrence after treatment within the SIOP GCT96 study in the UK were analysed. Seven (35%) patients had germinoma and 13 (65%) had non-germinoma. Twelve patients had local recurrence, 5 had metastatic and 3 had local and metastatic disease. Radiotherapy details were retrieved in only 8 patients (40%). Six patients had received focal radiotherapy and two craniospinal radiotherapy. Of the patients who received focal radiotherapy, 4 had recurrence within the radiation portal, one had periventricular recurrence and one had marker-positive recurrence with no radiological lesions. Both patients who received CSI recurred within the CSF space. The main reasons for poor retrieval of treatment details were difficulty in retrieving archived information and that the study was conducted during a period before PACS or electronic radiotherapy records. CONCLUSION This study highlights the importance prospective data collection and analysis to understand the patterns of recurrence in icGCT. Even within a prospective study, radiotherapy techniques varied between centres. There is therefore an urgent need for centralised radiological review and prospective radiotherapy quality assurance measures in future clinical trials.

Testing hypotheses under adaptive randomization with continuous covariates in clinical trials

2018 ◽  
Vol 28 (6) ◽  
pp. 1609-1621
Author(s):  
Xiaoming Li ◽  
Jianhui Zhou ◽  
Feifang Hu
Keyword(s):  
Clinical Trials ◽  
Hypothesis Testing ◽  
Treatment Effects ◽  
Linear Models ◽  
Adaptive Designs ◽  
Cumulative Distribution ◽  
Type I ◽  
Percentile Method ◽  
Continuous Covariates ◽  
Leibler Divergence

Covariate-adaptive designs are widely used to balance covariates and maintain randomization in clinical trials. Adaptive designs for discrete covariates and their asymptotic properties have been well studied in the literature. However, important continuous covariates are often involved in clinical studies. Simply discretizing or categorizing continuous covariates can result in loss of information. The current understanding of adaptive designs with continuous covariates lacks a theoretical foundation as the existing works are entirely based on simulations. Consequently, conventional hypothesis testing in clinical trials using continuous covariates is still not well understood. In this paper, we establish a theoretical framework for hypothesis testing on adaptive designs with continuous covariates based on linear models. For testing treatment effects and significance of covariates, we obtain the asymptotic distributions of the test statistic under null and alternative hypotheses. Simulation studies are conducted under a class of covariate-adaptive designs, including the p-value-based method, the Su’s percentile method, the empirical cumulative-distribution method, the Kullback–Leibler divergence method, and the kernel-density method. Key findings about adaptive designs with independent covariates based on linear models are (1) hypothesis testing that compares treatment effects are conservative in terms of smaller type I error, (2) hypothesis testing using adaptive designs outperforms complete randomization method in terms of power, and (3) testing on significance of covariates is still valid.

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