Some new confidence intervals for Kaplan-Meier based estimators from one and two sample survival data

Survivability prognosis of lung cancer patients with comorbidities-a Gaussian Bayesian network model

Comorbidities are influencing factors that cause lung cancer. An accurate survivability prediction model is required considering these confounding factors (a variety of comorbidities and treatments). The study developed a conditional Gaussian Bayesian network (CGBN) model to predict the related survival time with likelihood under various conditions. The lung cancer patients were collected from the National Health Insurance Research Database in Taiwan. Six major chronic diseases (i.e., pulmonary tuberculosis, COPD, kidney failure, diabetes mellitus, stroke, and liver disease) are investigated. A total of 2875 lung cancer cases with key comorbidities were selected. This study examined three types of lung cancer treatment: surgery, chemotherapy, and targeted therapy. The study outcomes provided the likelihood of survival time occurrences. Survival analysis indicates that diabetes mellitus and liver disease are significantly riskier than the other comorbidities for lung cancer patients. The proposed CGBN model achieved high accuracy as compared to the existing literature. The proposed CGBN model is advantageous for modeling the relationship between numerical and categorical influencing factors and response variables for lung cancer with comorbidities. The proposed model facilitates the flexible and accurate estimation of various lung cancer-related queries.

Complex survival trial design by the product integration method

Nonproportional hazards (NPHs) are often observed in survival trials such as the immunotherapy cancer trials. Under NPH, the classical log-rank test can be inefficient, and the estimated hazards ratio from the Cox model is difficult to interpret. The weighted log-rank test, and the tests for comparing the restricted mean survival time or the milestone survival become increasingly popular in handling NPH. The sample size calculation for these tests may require high-dimensional numerical integration. We present a sample size determination method for survival trials via product integration on the basis of a continuous-time multistate Markov model. The main challenge of the method lies in the design of the multistate model under a complex NPH pattern, and this is illustrated for NPH induced by delayed effect with individual heterogeneity in the lag duration, cure fractions, and treatment switching due to disease progression or noncompliance. Numerical examples are presented to demonstrate the accuracy of the proposed method. We obtain the following findings. The powers of the tests for milestone survival and RMST depend on both the trial duration and milestone timepoint, and may not increase as the milestone timepoint increases. If the milestone timepoint is appropriately chosen, the RMST test can be more powerful than the conventional log-rank test in the presence of diminishing treatment effect or in the proportional hazards cure model. In general, the RMST test yields lower power than a proper Fleming-Harrington weighted log-rank test.

MOVER confidence intervals for a difference or ratio effect parameter under stratified sampling

Stratification is commonly employed in clinical trials to reduce the chance covariate imbalances and increase the precision of the treatment effect estimate. We propose a general framework for constructing the confidence interval (CI) for a difference or ratio effect parameter under stratified sampling by the method of variance estimates recovery (MOVER). We consider the additive variance and additive CI approaches for the difference, in which either the CI for the weighted difference, or the CI for the weighted effect in each group, or the variance for the weighted difference is calculated as the weighted sum of the corresponding stratum-specific statistics. The CI for the ratio is derived by the Fieller and log-ratio methods. The weights can be random quantities under the assumption of a constant effect across strata, but this assumption is not needed for fixed weights. These methods can be easily applied to different endpoints in that they require only the point estimate, CI, and variance estimate for the measure of interest in each group across strata. The methods are illustrated with two real examples. In one example, we derive the MOVER CIs for the risk difference and risk ratio for binary outcomes. In the other example, we compare the restricted mean survival time and milestone survival in stratified analysis of time-to-event outcomes. Simulations show that the proposed MOVER CIs generally outperform the standard large sample CIs, and that the additive CI approach performs better than the additive variance approach. Sample SAS code is provided in the Supplementary Material.