Summarising salient information on historical controls: A structured assessment of validity and comparability across studies

Background While placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions. Methods We discuss the original ‘Pocock’ criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer. Results A number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be. Conclusion Historical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly.

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Including historical data in the analysis of clinical trials: Is it worth the effort?

Statistical Methods in Medical Research ◽

10.1177/0962280217694506 ◽

2017 ◽

Vol 27 (10) ◽

pp. 3167-3182 ◽

Cited By ~ 18

Author(s):

Joost van Rosmalen ◽

David Dejardin ◽

Yvette van Norden ◽

Bob Löwenberg ◽

Emmanuel Lesaffre

Keyword(s):

Clinical Trials ◽

Bayesian Methods ◽

Prior Information ◽

Type I Error ◽

Historical Data ◽

Type I ◽

Data Set ◽

Current Trial ◽

Power Prior ◽

Randomised Controlled

Data of previous trials with a similar setting are often available in the analysis of clinical trials. Several Bayesian methods have been proposed for including historical data as prior information in the analysis of the current trial, such as the (modified) power prior, the (robust) meta-analytic-predictive prior, the commensurate prior and methods proposed by Pocock and Murray et al. We compared these methods and illustrated their use in a practical setting, including an assessment of the comparability of the current and the historical data. The motivating data set consists of randomised controlled trials for acute myeloid leukaemia. A simulation study was used to compare the methods in terms of bias, precision, power and type I error rate. Methods that estimate parameters for the between-trial heterogeneity generally offer the best trade-off of power, precision and type I error, with the meta-analytic-predictive prior being the most promising method. The results show that it can be feasible to include historical data in the analysis of clinical trials, if an appropriate method is used to estimate the heterogeneity between trials, and the historical data satisfy criteria for comparability.

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Rnf43 Mutation As A Biomarker For Immune Checkpoint Inhibitor Efficacy In Colorectal Cancer

10.21203/rs.3.rs-536739/v1 ◽

2021 ◽

Author(s):

Nan Zhang ◽

XinPeng Shi ◽

WenCui Ju ◽

YunFeng Lou ◽

XiaoYong Luo

Keyword(s):

Colorectal Cancer ◽

Microsatellite Instability ◽

Gene Mutation ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitors ◽

Immune Cell ◽

Checkpoint Inhibitors ◽

Data Set ◽

Cancer Data ◽

Low Efficiency

Abstract Background: Immune checkpoint inhibitors can prolong the survival of patients with advanced colorectal cancer and have been approved for the treatment of metastatic colorectal cancer patients with mismatch repair defects and high microsatellite instability (dMMR-MSI-H). However, there are still many deficiencies in their clinical application, such as their benefit in a limited population, low efficiency, and lack of accurate markers. Therefore, finding accurate biomarkers has become an urgent problem in the immunotherapy of colorectal cancer. Our research aimed to find biomarkers that can accurately predict the population with potential benefit.Methods: We analysed data from a colon cancer immunotherapy cohort (n = 110, ICI cohort), including mutation data and clinical data, to identify the mutated gene closely related to Immune Checkpoint Inhibitors (ICIs). Next, we further verified the relationship between gene mutation and clinical features, such as Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI) status in the TCGA colorectal cancer data set (non-ICI cohort). Then, CIBERSORT was used to analyse the relationships between gene mutation and both immune cell infiltration and immune genes, and GSEA was used to analyse the effect of gene mutation on pathway activation levels. In addition, we analysed the Whole-exome Sequencing (WES) and drug sensitivity data of colorectal cancer cell lines in the GDSC database.Conclusions: Our results show that Rnf43 mut can be used as a biomarker to predict the efficacy of ICI for colorectal cancer, and it can be used for clinical screening of patients who benefit from ICI for colorectal cancer.

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A new survival model with surviving fraction: An application to colorectal cancer data

Statistical Methods in Medical Research ◽

10.1177/0962280218786053 ◽

2018 ◽

Vol 28 (9) ◽

pp. 2665-2680

Author(s):

Gladys DC Barriga ◽

Vicente G Cancho ◽

Daniel V Garibay ◽

Gauss M Cordeiro ◽

Edwin MM Ortega

Keyword(s):

Colorectal Cancer ◽

Maximum Likelihood ◽

Expectation Maximization Algorithm ◽

Survival Model ◽

Maximum Likelihood Estimates ◽

Model Parameters ◽

Ratio Test ◽

Data Set ◽

Cancer Data ◽

Proposed Model

We propose a new survival model for lifetime data in the presence of surviving fraction and obtain some of its properties. Its genesis is based on extensions of the promotion time cure model, where an extra parameter controls the heterogeneity or dependence of an unobserved number of lifetimes. We construct a regression model to evaluate the effects of covariates in the cured fraction. We discuss inference aspects for the proposed model in a classical approach, where some maximum likelihood tools are explored. Further, an expectation maximization algorithm is developed to calculate the maximum likelihood estimates of the model parameters. We also perform an empirical study of the likelihood ratio test in order to compare the promotion time cure and the proposed models. We illustrate the usefulness of the new model by means of a colorectal cancer data set.

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Effect of platelet inhibition on the risk of development of metastatic disease in colorectal cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2011.29.4_suppl.470 ◽

2011 ◽

Vol 29 (4_suppl) ◽

pp. 470-470

Author(s):

D. Pulte ◽

S. B. Chalasani ◽

M. Bryan ◽

L. F. Pliner

Keyword(s):

Colorectal Cancer ◽

Metastatic Disease ◽

Statistical Significance ◽

Prospective Trial ◽

Patient Characteristics ◽

Platelet Inhibition ◽

Stage Ii ◽

Recurrence Time ◽

Cancer Data ◽

Number Of Patients

470 Background: Platelet inhibition has been shown to decrease the risk of metastatic disease in numerous animal models. The use of aspirin to decrease the risk of colorectal cancer (CRC) has been documented in several large studies. However, to date, no studies of whether the use of aspirin or other platelet inhibiting agent (PIA) will reduce the risk of recurrence in patients who develop CRC have been performed. Methods: In this study, we reviewed the charts of all patients treated at the University of Medicine and Dentistry of New Jersey at Newark for stage II or III colon or rectal cancer. Data extracted included use of PIA, stage, treatment, recurrence, time of last contact, and patient characteristics. Patients were defined as having taken a PIA if any evidence of use could be documented. Statistical significance of differences observed was determined using a Fisher's exact test. Results: 111 charts were initially identified. Use of PIA could not be determined for 6, six had metastatic disease at presentation, and one was stage I, leaving 98 patients for analysis. Of these, 35 had evidence of having used PIA, including 20 patients who were taking aspirin. Forty patients without history of use of a PIA had no metastatic disease, compared to 23 patients who developed metastatic disease. For patients taking PIA, 6 developed metastatic disease (Table). Conclusions: Our results suggest that PIA may help decrease the likelihood of the development of metastatic disease in patients who are diagnosed with stage II or III CRC. The study was limited by the number of patients available, which was not large enough to allow consideration of all possible confounders. Further study of the effects of PIA in a translational and clinical setting including a larger, prospective trial of PIA in stage II/III CRC may lead to further insight into this question. [Table: see text] No significant financial relationships to disclose.

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