PWEXP: An R Package Using Piecewise Exponential Model for Study Design and Event/Timeline Prediction (2404.17772v1)
Abstract: Parametric assumptions such as exponential distribution are commonly used in clinical trial design and analysis. However, violation of distribution assumptions can introduce biases in sample size and power calculations. Piecewise exponential (PWE) hazard model partitions the hazard function into segments each with constant hazards and is easy for interpretation and computation. Due to its piecewise property, PWE can fit a wide range of survival curves and accurately predict the future number of events and analysis time in event-driven clinical trials, thus enabling more flexible and reliable study designs. Compared with other existing approaches, the PWE model provides a superior balance of flexibility and robustness in model fitting and prediction. The proposed PWEXP package is designed for estimating and predicting PWE hazard models for right-censored data. By utilizing well-established criteria such as AIC, BIC, and cross-validation log-likelihood, the PWEXP package chooses the optimal number of change-points and determines the optimal position of change-points. With its particular goodness-of-fit, the PWEXP provides accurate and robust hazard estimation, which can be used for reliable power calculation at study design and timeline prediction at study conduct. The package also offers visualization functions to facilitate the interpretation of survival curve fitting results.
- Anderson, K. (2023). gsDesign: Group Sequential Design. R package version 3.6.0.
- Bagiella, E. and D. F. Heitjan (2001). Predicting analysis times in randomized clinical trials. Statistics in medicine 20(14), 2055–2063.
- Betensky, R. A. (2015). Measures of follow-pp in time-to-event studies: Why provide them and what should they be? Clinical Trials 12(4), 403–408.
- A novel bayesian continuous piecewise linear log-hazard model, with estimation and inference via reversible jump markov chain monte carlo. Statistics in Medicine 39(12), 1766–1780.
- Change-point detection for piecewise exponential models. arXiv preprint arXiv:2112.03962.
- Change-point detection for piecewise exponential models. arXiv, 1–21.
- Dupuy, J.-F. (2006). Estimation in a change-point hazard regression model. Statistics & Probability Letters 76(2), 182–190.
- Friedman, M. (1982). Piecewise exponential models for survival data with covariates. The Annals of Statistics 10(1), 101–113.
- Frumento, P. (2021). pch: Piecewise Constant Hazard Models for Censored and Truncated Data. R package version 2.0.
- Detecting multiple change points in piecewise constant hazard functions. Journal of Applied Statistics 38(11), 2523–2532.
- Henderson, R. (1990). A problem with the likelihood ratio test for a change-point hazard rate model. Biometrika 77(4), 835–843.
- muhaz: Hazard Function Estimation in Survival Analysis. R package version 1.2.6.4.
- Bayesian multiple change-points estimation for hazard with censored survival data from exponential distributions. Journal of the Korean Statistical Society 49, 15–31.
- Survival Analysis: Techniques for Censored and Truncated Data, Volume 1230. Springer.
- Küchenhoff, H. (1996). An exact algorithm for estimating breakpoints in segmented generalized linear models.
- Estimation in a change-point hazard regression model with long-term survivors. Statistics & Probability Letters 83(7), 1683–1691.
- Ipdfromkm: Reconstruct individual patient data from published kaplan-meier survival curves. BMC medical Research Methodology 21(1), 111.
- Loubert, S. K. (1986). Inference Procedures for the Piecewise Exponential Model When the Data Are Arbitrarily Censored. Iowa State University.
- Matthews, D. E. and V. T. Farewell (1982). On testing for a constant hazard against a change-point alternative. Biometrics, 463–468.
- Muggeo, V. M. (2003). Estimating regression models with unknown break-points. Statistics in Medicine 22(19), 3055–3071.
- Multiple change-point detection in piecewise exponential hazard regression models with long-term survivors and right censoring. In Contemporary Developments in Statistical Theory: A Festschrift for Hira Lal Koul, pp. 289–304. Springer.
- Rohatgi, A. (2024). Webplotdigitizer: Version 4.7.
- Rufibach, K. (2022). eventTrack: Event Prediction for Time-to-Event Endpoints. R package version 1.0.2.
- International prognostic indices in diffuse large b-cell lymphoma: A comparison of ipi, r-ipi, and nccn-ipi. Blood, The Journal of the American Society of Hematology 135(23), 2041–2048.
- Predicting analysis time in event-driven clinical trials with event-reporting lag. Statistics in Medicine 31(9), 801–811.
- rpact: Confirmatory Adaptive Clinical Trial Design and Analysis. R package version 3.5.1.
- Yao, Y.-C. (1986). Maximum likelihood estimation in hazard rate models with a change-point. Communications in Statistics-Theory and Methods 15(8), 2455–2466.
- Ying, G.-s. and D. F. Heitjan (2008). Weibull prediction of event times in clinical trials. Pharmaceutical Statistics: The Journal of Applied Statistics in the Pharmaceutical Industry 7(2), 107–120.
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