Dataset Information

Missing tumor measurement (TM) data in the search for alternative TM-based endpoints in cancer clinical trials.

ABSTRACT:

Purpose

Missing data commonly occur in cancer clinical trials (CCT) and may hinder the search for alternative trial endpoints. We consider reasons for missing tumor measurement (TM) data in CCT and how missing TM data are typically handled. We explore the potential impact of missing TM data on predictive ability of a set of TM-based endpoints.

Methods

Literature review identifies reasons for and approaches to handling missing TM data. Data from 3 actual clinical trials were used for illustration. A sensitivity analysis of the potential impact of missing TM data was performed by comparing overall survival (OS) predictive ability of alternative endpoints using observed and imputed data.

Results

Reasons for missing TM data in CCT are presented, based on the literature review and the three trials. Although missing TM data impacted individual objective status (e.g. 12-week status changed for 53% of patients in one imputation set), it surprisingly only minimally impacted endpoint predictive ability (e.g. median c-indices of 500 imputed datasets ranged from 0.566 to 0.570 for N9741, 0.592-0.616 for N9841, and 0.542-0.624 for N0026).

Conclusion

By understanding the reasons for missingness, we can better anticipate them and minimize their occurrence. Our preliminary analysis suggests missing TM data may not impact endpoint predictive ability, but could impact objective response status classification; however these findings require further validation. With response status accepted as an important phase II endpoint in the development of new cancer therapies (including immunotherapy), we urge that in CCT complete TM data collection and adherence to protocol-defined disease evaluation as closely as possible be a priority.

SUBMITTER: An MW

PROVIDER: S-EPMC6909186 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

Missing tumor measurement (TM) data in the search for alternative TM-based endpoints in cancer clinical trials.

An Ming-Wen MW Tang Jun J Grothey Axel A Sargent Daniel J DJ Ou Fang-Shu FS Mandrekar Sumithra J SJ

Contemporary clinical trials communications 20191122

<h4>Purpose</h4>Missing data commonly occur in cancer clinical trials (CCT) and may hinder the search for alternative trial endpoints. We consider reasons for missing tumor measurement (TM) data in CCT and how missing TM data are typically handled. We explore the potential impact of missing TM data on predictive ability of a set of TM-based endpoints.<h4>Methods</h4>Literature review identifies reasons for and approaches to handling missing TM data. Data from 3 actual clinical trials were used f ...[more]

PMID: 31872158

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Project description:IntroductionLongitudinal tumor measurements (TM) are commonly recorded in cancer clinical trials of solid tumors. To define patient response to treatment, the Response Evaluation Criteria in Solid Tumors (RECIST) categorizes the otherwise continuous measurements, which results in substantial information loss. We investigated two modeling approaches to incorporate all available cycle-by-cycle (continuous) TM to predict overall survival (OS) and compare the predictive accuracy of these two approaches to RECIST.Material and methodsJoint modeling (JM) for longitudinal TM and OS and two-stage modeling with potential time-varying coefficients were utilized to predict OS using data from three trials with cycle-by-cycle TM. The JM approach incorporates TM data collected throughout the course of the clinical trial. The two-stage modeling approach incorporates information from early assessments (before 12 weeks) to predict subsequent OS outcome. The predictive accuracy was quantified by c-indices.ResultsData from 577, 337, and 126 patients were included for the analysis (from two stage IV colorectal cancer trials (N9741, N9841) and an advanced non-small cell lung cancer trial (N0026), respectively). Both the JM and two-stage modeling reached a similar conclusion, i.e. the baseline covariates (age, gender, and race) were mostly not predictive of OS (p-value > 0.05). Quantities derived from TM were strong predictors of OS in the two colorectal cancer trials (p < 0.001 for both association in JM and two-stage modeling parameters); but less so in the lung cancer trial (p = 0.053 for association in JM and p = 0.024 and 0.160 for two-stage modeling parameters). The c-indices from the two-stage modeling were higher than those from a model using RECIST (range: 0.611-0.633 versus 0.586-0.590). The dynamic c-indices from the JM were in the range of 0.627-0.683 indicating good predictive accuracy.ConclusionBoth modeling approaches provide highly interpretable and clinical meaningful results; the improved predictive performance compared with RECIST indicates the possibility of deriving better trial endpoints from these approaches.

Project description:The aim of this review was to establish the frequency with which trials take into account missingness, and to discover what methods trialists use for adjustment in randomised controlled trials with longitudinal measurements. Failing to address the problems that can arise from missing outcome data can result in misleading conclusions. Missing data should be addressed as a means of a sensitivity analysis of the complete case analysis results. One hundred publications of randomised controlled trials with longitudinal measurements were selected randomly from trial publications from the years 2005 to 2012. Information was extracted from these trials, including whether reasons for dropout were reported, what methods were used for handing the missing data, whether there was any explanation of the methods for missing data handling, and whether a statistician was involved in the analysis. The main focus of the review was on missing data post dropout rather than missing interim data. Of all the papers in the study, 9 (9%) had no missing data. More than half of the papers included in the study failed to make any attempt to explain the reasons for their choice of missing data handling method. Of the papers with clear missing data handling methods, 44 papers (50%) used adequate methods of missing data handling, whereas 30 (34%) of the papers used missing data methods which may not have been appropriate. In the remaining 17 papers (19%), it was difficult to assess the validity of the methods used. An imputation method was used in 18 papers (20%). Multiple imputation methods were introduced in 1987 and are an efficient way of accounting for missing data in general, and yet only 4 papers used these methods. Out of the 18 papers which used imputation, only 7 displayed the results as a sensitivity analysis of the complete case analysis results. 61% of the papers that used an imputation explained the reasons for their chosen method. Just under a third of the papers made no reference to reasons for missing outcome data. There was little consistency in reporting of missing data within longitudinal trials.

Project description:Frontotemporal dementia refers to a group of progressive neurodegenerative syndromes usually caused by the accumulation of pathological tau or TDP-43 proteins. The effects of these proteins in the brain are complex, and each can present with several different clinical syndromes. Clinical efficacy trials of drugs targeting these proteins must use endpoints that are meaningful to all participants despite the variability in symptoms across patients. There are many candidate clinical measures, including neuropsychological scores and functional measures. Brain imaging is another potentially attractive outcome that can be precisely quantified and provides evidence of disease modification. Most imaging studies in frontotemporal dementia have been cross-sectional, and few have compared longitudinal changes in cortical volume with changes in other measures such as perfusion and white matter integrity. The current study characterized longitudinal changes in 161 patients with three frontotemporal dementia syndromes: behavioural variant frontotemporal dementia (n = 77) and the semantic (n = 45) and non-fluent (n = 39) variants of primary progressive aphasia. Visits included comprehensive neuropsychological and functional assessment, structural MRI (3 T), diffusion tensor imaging, and arterial spin labelled perfusion imaging. The goal was to identify measures that are appropriate as clinical trial outcomes for each group, as well as those that might be appropriate for trials that would include more than one of these groups. Linear mixed effects models were used to estimate changes in each measure, and to examine the correlation between imaging and clinical changes. Sample sizes were estimated based on the observed effects for theoretical clinical trials using bootstrapping techniques to provide 95% confidence intervals for these estimates. Declines in functional and neuropsychological measures, as well as frontal and temporal cortical volumes and white matter microstructure were detected in all groups. Imaging changes were statistically significantly correlated with, and explained a substantial portion of variance in, the change in most clinical measures. Perfusion and diffusion tensor imaging accounted for variation in clinical decline beyond volume alone. Sample size estimates for atrophy and diffusion imaging were comparable to clinical measures. Corpus callosal fractional anisotropy led to the lowest sample size estimates for all three syndromes. These findings provide further guidance on selection of trial endpoints for studies in frontotemporal dementia and support the use of neuroimaging, particularly structural and diffusion weighted imaging, as biomarkers. Diffusion and perfusion imaging appear to offer additional utility for explaining clinical change beyond the variance explained by volume alone, arguing for considering multimodal imaging in treatment trials.

Dataset Information

Missing tumor measurement (TM) data in the search for alternative TM-based endpoints in cancer clinical trials.

Purpose

Methods

Results

Conclusion

Publications

Missing tumor measurement (TM) data in the search for alternative TM-based endpoints in cancer clinical trials.

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