Statistics in medicine
-
Statistics in medicine · Feb 2019
Allowing for uncertainty due to missing and LOCF imputed outcomes in meta-analysis.
The use of the last observation carried forward (LOCF) method for imputing missing outcome data in randomized clinical trials has been much criticized and its shortcomings are well understood. However, only recently have published studies widely started using more appropriate imputation methods. Consequently, meta-analyses often include several studies reporting their results according to LOCF. ⋯ The extended model includes an extra parameter that reflects the level of prior confidence in the appropriateness of the LOCF imputation scheme. Neither parameter can be informed by the data and we resort to expert opinion and sensitivity analysis. We illustrate the methodology using two meta-analyses of pharmacological interventions for depression.
-
Statistics in medicine · Dec 2018
Quantifying and presenting overall evidence in network meta-analysis.
Network meta-analysis (NMA) has become an increasingly used tool to compare multiple treatments simultaneously by synthesizing direct and indirect evidence in clinical research. However, many existing studies did not properly report the evidence of treatment comparisons and show the comparison structure to audience. In addition, nearly all treatment networks presented only direct evidence, not overall evidence that can reflect the benefit of performing NMAs. ⋯ The proposed measures provide clear information about overall evidence of all treatment comparisons, and they also imply the additional number of studies, sample size, and precision obtained from indirect evidence. Some comparisons may benefit little from NMAs. Researchers are encouraged to present overall evidence of all treatment comparisons, so that audience can preliminarily evaluate the quality of NMAs.
-
Statistics in medicine · Nov 2017
STEIN: A simple toxicity and efficacy interval design for seamless phase I/II clinical trials.
Seamless phase I/II dose-finding trials are attracting increasing attention nowadays in early-phase drug development for oncology. Most existing phase I/II dose-finding methods use sophisticated yet untestable models to quantify dose-toxicity and dose-efficacy relationships, which always renders them difficult to implement in practice. To simplify the practical implementation, we extend the Bayesian optimal interval design from maximum tolerated dose finding to optimal biological dose finding in phase I/II trials. ⋯ The proposed interval design is model-free, thus is suitable for various dose-response relationships. We conduct extensive simulation studies to demonstrate the small- and large-sample performance of the proposed method under various scenarios. Compared to existing phase I/II dose-finding designs, not only is our interval design easy to implement in practice, but it also possesses desirable and robust operating characteristics.
-
Statistics in medicine · Sep 2017
Comparative StudyModeling event count data in the presence of informative dropout with application to bleeding and transfusion events in myelodysplastic syndrome.
In many biomedical studies, it is often of interest to model event count data over the study period. For some patients, we may not follow up them for the entire study period owing to informative dropout. The dropout time can potentially provide valuable insight on the rate of the events. ⋯ Extensive simulation studies demonstrate that the proposed methods perform well in practice. We illustrate the proposed methods through an application to a clinical trial for bleeding and transfusion events in myelodysplastic syndrome. Copyright © 2017 John Wiley & Sons, Ltd.
-
Statistics in medicine · Sep 2017
Information criteria for Firth's penalized partial likelihood approach in Cox regression models.
In the estimation of Cox regression models, maximum partial likelihood estimates might be infinite in a monotone likelihood setting, where partial likelihood converges to a finite value and parameter estimates converge to infinite values. To address monotone likelihood, previous studies have applied Firth's bias correction method to Cox regression models. However, while the model selection criteria for Firth's penalized partial likelihood approach have not yet been studied, a heuristic AIC-type information criterion can be used in a statistical package. ⋯ Further, the presented simulation results confirm that the proposed criteria performed well in a monotone likelihood setting. The proposed AIC-type criterion was applied to prospective observational study data. Copyright © 2017 John Wiley & Sons, Ltd.