Statistics in medicine
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Statistics in medicine · Sep 2015
Towards using a full spectrum of early clinical trial data: a retrospective analysis to compare potential longitudinal categorical models for molecular targeted therapies in oncology.
Following the pattern of phase I clinical trials for cytotoxic drugs, dose-finding clinical trials in oncology of molecularly targeted agents (MTA) aim at determining the maximum tolerated dose (MTD). In classical phase I clinical trials, MTD is generally defined by the number of patients with short-term major treatment toxicities (usually called dose-limiting toxicities, DLT), occurring during the first cycle of study treatment (e.g. within the first 3weeks of treatment). However, S. ⋯ We suggest approaches based on longitudinal models (Doussau 2013). We compare several models for longitudinal data, such as transitional or marginal models, to take into account all relevant toxicities occurring during the entire length of the patient treatment (and not just the events within a predefined short-term time-window). These models allow the statistician to benefit from a larger amount of safety data which could potentially improve that accuracy in MTD assessment.
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In the development of risk prediction models, predictors are often measured with error. In this paper, we investigate the impact of covariate measurement error on risk prediction. We compare the prediction performance using a costly variable measured without error, along with error-free covariates, to that of a model based on an inexpensive surrogate along with the error-free covariates. ⋯ We conclude that reducing measurement error in covariates will improve the ensuing risk prediction, unless the association between the error-free and error-prone covariates is very high. Finally, we demonstrate how a validation study can be used to assess the effect of mismeasured covariates on risk prediction. These concepts are illustrated in a breast cancer risk prediction model developed in the Nurses' Health Study.
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Statistics in medicine · Jun 2015
The impact of companion diagnostic device measurement performance on clinical validation of personalized medicine.
A key component of personalized medicine is companion diagnostics that measure biomarkers, for example, protein expression, gene amplification or specific mutations. Most of the recent attention concerning molecular cancer diagnostics has been focused on the biomarkers of response to therapy, such as V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in metastatic colorectal cancer, epidermal growth factor receptor mutations in metastatic malignant melanoma. The presence or absence of these markers is directly linked to the response rates of particular targeted therapies with small-molecule kinase inhibitors or antibodies. ⋯ The core capability of personalized medicine is the companion diagnostic devices' (CDx) ability to accurately and precisely stratify patients by their likelihood of benefit (or harm) from a particular therapy. There is no reference in the literature discussing the impact of device's measurement performance, for example, analytical accuracy and precision on treatment effects, variances, and sample sizes of clinical trial for the personalized medicine. In this paper, using both analytical and estimation method, we assessed the impact of CDx measurement performance as a function of positive and negative predictive values and imprecision (standard deviation) on treatment effects, variances of clinical outcome, and sample sizes for the clinical trials.
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Statistics in medicine · Apr 2015
A product of independent beta probabilities dose escalation design for dual-agent phase I trials.
Dual-agent trials are now increasingly common in oncology research, and many proposed dose-escalation designs are available in the statistical literature. Despite this, the translation from statistical design to practical application is slow, as has been highlighted in single-agent phase I trials, where a 3 + 3 rule-based design is often still used. To expedite this process, new dose-escalation designs need to be not only scientifically beneficial but also easy to understand and implement by clinicians. ⋯ Monotonicity is ensured by considering only a set of monotonic contours for the distribution of the maximum tolerated contour, which defines the dose-escalation decision process. Varied experimentation around the contour is achievable, and multiple dose combinations can be recommended to take forward to phase II. Code for R, Stata and Excel are available for implementation.
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Statistics in medicine · Mar 2015
Comparative StudySimulation study comparing exposure matching with regression adjustment in an observational safety setting with group sequential monitoring.
Sequential methods are well established for randomized clinical trials (RCTs), and their use in observational settings has increased with the development of national vaccine and drug safety surveillance systems that monitor large healthcare databases. Observational safety monitoring requires that sequential testing methods be better equipped to incorporate confounder adjustment and accommodate rare adverse events. New methods designed specifically for observational surveillance include a group sequential likelihood ratio test that uses exposure matching and generalized estimating equations approach that involves regression adjustment. ⋯ Method performance also depended on the distribution of information and extent of confounding by site. Our results suggest that choice of sequential method, especially the confounder control strategy, is critical in rare event observational settings. These findings provide guidance for choosing methods in this context and, in particular, suggest caution when conducting exposure matching.