Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals
Amardeep Legha, Joie Ensor, Rebecca Whittle, Lucinda Archer, Ben Van Calster, Evangelia Christodoulou, Kym I. E. Snell, Mohsen Sadatsafavi, Gary S. Collins, Richard D. Riley
When prospectively developing a new clinical prediction model (CPM), fixed sample size calculations are typically conducted before data collection based on sensible assumptions. But if the assumptions are inaccurate the actual sample size required to develop a reliable model may be very different. To safeguard against this, adaptive sample size approaches have been proposed, based on sequential evaluation of a models predictive performance. Aim: illustrate and extend sequential sample size calculations for CPM development by (i) proposing stopping rules based on minimising uncertainty (instability) and misclassification of individual-level predictions, and (ii) showcasing how it safeguards against inaccurate fixed sample size calculations. Using the sequential approach repeats the pre-defined model development strategy every time a chosen number (e.g., 100) of participants are recruited and adequately followed up. At each stage, CPM performance is evaluated using bootstrapping, leading to prediction and classification stability statistics and plots, alongside optimism-adjusted measures of calibration and discrimination. Our approach is illustrated for development of acute kidney injury using logistic regression CPMs. The fixed sample size calculation, based on perceived sensible assumptions suggests recruiting 342 patients to minimise overfitting; however, the sequential approach reveals that a much larger sample size of 1100 is required to minimise overfitting (targeting population-level stability). If the stopping rule criteria also target small uncertainty and misclassification probability of individual predictions, the sequential approach suggests an even larger sample size (n=1800). Our sequential sample size approach allows users to dynamically monitor individual-level prediction and classification instability and safeguard against using inaccurate assumptions.
Confirmatory adaptive group sequential designs for clinical trials with multiple time-to-event outcomes in Markov models
Institute of Biostatistics and Clinical Research, University of Münster（明斯特大学生物统计与临床研究研究所）；Department of Pediatric Oncology and Hematology, University Hospital Cologne（科隆大学医院儿科肿瘤与血液科）
Sequential Advantage Selection for Optimal Treatment Regimes
临床试验或观察性研究中最佳治疗方案的变量选择越来越受到关注。现有的大多数变量选择技术集中于选择对预测重要的变量，因此，一些预测性差但对决策至关重要的变量可能会被忽略。当处理效应随着变量值的变化而改变方向时，变量与处理的定性相互作用就产生了。定性的相互作用表明了这个变量对决策的重要性。Gunter et al.（2011）提出了S评分，用于表征每个变量与单独治疗的定性相互作用的程度。在本文中，我们开发了一个顺序优势选择方法的基础上修改的S-评分。我们的方法选择定性相互作用的变量顺序，因此排除了边际重要，但共同不重要的变量{反之亦然}。通过联合模型选择的变量所得到的最优治疗方案更全面、更可靠。利用所提出的停止准则，我们的方法可以处理大量的协变量，即使样本量很小。仿真结果表明，我们的方法在实际环境中表现良好。我们进一步将我们的方法应用于抑郁症临床试验的数据。
