Statistics in medicine
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Statistics in medicine · Apr 2021
Assessing vaccine durability in randomized trials following placebo crossover.
Randomized vaccine trials are used to assess vaccine efficacy (VE) and to characterize the durability of vaccine-induced protection. If efficacy is demonstrated, the treatment of placebo volunteers becomes an issue. For COVID-19 vaccine trials, there is broad consensus that placebo volunteers should be offered a vaccine once efficacy has been established. ⋯ We only require that the VE profile applies to the newly vaccinated irrespective of the timing of vaccination. We develop different methods to estimate efficacy within the context of a proportional hazards regression model and explore via simulation the implications of placebo crossover for estimation of VE under different efficacy dynamics and study designs. We apply our methods to simulated COVID-19 vaccine trials with durable and waning VE and a total follow-up of 2 years.
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Statistics in medicine · Dec 2019
A utility-based Bayesian optimal interval (U-BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies.
In the era of targeted therapy and immunotherapy, the objective of dose finding is often to identify the optimal biological dose (OBD), rather than the maximum tolerated dose. We develop a utility-based Bayesian optimal interval (U-BOIN) phase I/II design to find the OBD. We jointly model toxicity and efficacy using a multinomial-Dirichlet model, and employ a utility function to measure dose risk-benefit trade-off. ⋯ Our simulation study shows that, despite its simplicity, the U-BOIN design is robust and has high accuracy to identify the OBD. We extend the design to accommodate delayed efficacy by leveraging the short-term endpoint (eg, immune activity or other biological activity of targeted agents), and using it to predict the delayed efficacy outcome to facilitate real-time decision making. A user-friendly software to implement the U-BOIN is freely available at www.trialdesign.org.
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Statistics in medicine · Sep 2019
Participants' outcomes gone missing within a network of interventions: Bayesian modeling strategies.
To investigate the implications of addressing informative missing binary outcome data (MOD) on network meta-analysis (NMA) estimates while applying the missing at random (MAR) assumption under different prior structures of the missingness parameter. ⋯ Analyzing informative MOD assuming MAR with different prior structures of log IMOR affected mainly the precision of NMA estimates. Reviewers should decide in advance on the prior structure of log IMOR that best aligns with the condition and interventions investigated.
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Statistics in medicine · Aug 2019
Impact of predictor measurement heterogeneity across settings on the performance of prediction models: A measurement error perspective.
It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived. Out-of-sample performance can be hampered when predictors are measured differently at derivation and external validation. This may occur, for instance, when predictors are measured using different measurement protocols or when tests are produced by different manufacturers. ⋯ The results of our simulations indicate that predictor measurement heterogeneity can induce miscalibration of prediction and affects discrimination and overall predictive accuracy, to extents that the prediction model may no longer be considered clinically useful. The measurement error taxonomy was found to be helpful in identifying and predicting effects of heterogeneous predictor measurements between settings of prediction model derivation and validation. Our work indicates that homogeneity of measurement strategies across settings is of paramount importance in prediction research.