Restricted Net Treatment Benefit in oncology.

OBJECTIVES: The restricted Net Treatment Benefit (rNTB) is a clinically meaningful and tractable estimand of the overall treatment effect assessed in randomized trials when at least one survival endpoint with time restriction is used. Its interpretation does not rely on parametric assumptions such as proportional hazards, can be estimated without bias even in the presence of independent right-censoring, and can include a prespecified threshold of minimal clinically relevant difference. To demonstrate that the rNTB, corresponding to the NTB during a predefined time interval, is a meaningful and adaptable measure of treatment effect in clinical trials. METHODS: In this simulation study, we tested the impact on the rNTB value, estimation, and power of several factors including the presence of a delayed treatment effect, minimal clinically relevant difference threshold value, restriction time value, and the inclusion of both efficacy and toxicity in the rNTB definition. The impact of right censoring on rNTB was assessed in terms of bias. rNTB-derived statistical tests and log rank (LR) tests were compared in terms of power. RESULTS: RNTB estimates are unbiased even in case of right-censoring. rNTB may be used to estimate the benefit/risk ratio of a new treatment, for example, taking into account both survival and toxicity and include several prioritized outcomes. The estimated rNTB is much easier to interpret in this context compared to NTB in the presence of censoring since the latter is intrinsically dependent on the follow-up duration. Including toxicity increases the test power when the experimental treatment is less toxic. rNTB-derived test power increases when the experimental treatment is associated with longer survival and lower toxicity and might increase in the presence of a cure rate or a delayed treatment effect. Case applications on the PRODIGE, Checkmate-066, and Checkmate-067 trials are provided. CONCLUSIONS: RNTB is an interesting alternative to describe and test the treatment's effect in a clear and understandable way in case of restriction, particularly in scenarios with nonproportional hazards or when trying to balance benefit and safety. It can be tuned to take into consideration short- or long-term survival differences and one or more prioritized outcomes.

Sample size calculation for mixture model based on geometric average hazard ratio and its applications to nonproportional hazard.

With the advent of cancer immunotherapy, some special features including delayed treatment effect, cure rate, diminishing treatment effect and crossing survival are often observed in survival analysis. They violate the proportional hazard model assumption and pose a unique challenge for the conventional trial design and analysis strategies. Many methods like cure rate model have been developed based on mixture model to incorporate some of these features. In this work, we extend the mixture model to deal with multiple non-proportional patterns and develop its geometric average hazard ratio (gAHR) to quantify the treatment effect. We further derive a sample size and power formula based on the non-centrality parameter of the log-rank test and conduct a thorough analysis of the impact of each parameter on performance. Simulation studies showed a clear advantage of our new method over the proportional hazard based calculation across different non-proportional hazard scenarios. Moreover, the mixture modeling of two real trials demonstrates how to use the prior information on the survival distribution among patients with different biomarker and early efficacy results in practice. By comparison with a simulation-based design, the new method provided a more efficient way to compute the power and sample size with high accuracy of estimation. Overall, both theoretical derivation and empirical studies demonstrate the promise of the proposed method in powering future innovative trial designs.

Birth Spacing and Fertility in the Presence of Son Preference and Sex-Selective Abortions: India's Experience Over Four Decades.

Since the advent of prenatal sex-determination technologies in the mid-1980s, India has experienced an increasingly male-biased sex ratio at birth, presumably from sex-selective abortions. Abortions lengthen birth intervals, but we know little about how birth spacing has changed or the effects of these changes. I show that, although the overall length of birth intervals increased from 1970 to the mid-2010s, well-educated women with no sons had the most substantial lengthening, as well as the most male-biased sex ratios. Furthermore, most of these changes took place immediately after the introduction of prenatal sex-determination technologies. Consequently, some women without sons now have longer birth intervals than those with sons, reversing India's traditional spacing pattern. Women with low education continue short birth spacing when they have no sons, with only limited evidence of male-biased sex ratios. Because of the rapid lengthening of birth intervals, period fertility rates substantially overestimated how fast cohort fertility fell. Moreover, predicted cohort fertility is still 10%-20% above the period fertility rate. If the lengthening of birth intervals arises from repeated abortions, the associated short pregnancy spacing may counteract any positive effects of longer birth spacing. There is, however, no evidence of this effect on infant mortality. Judging from sex ratios, sex-selective abortion use is not declining.

A resampling-based test for two crossing survival curves.

The area between two survival curves is an intuitive test statistic for the classical two-sample testing problem. We propose a bootstrap version of it for assessing the overall homogeneity of these curves. Our approach allows ties in the data as well as independent right censoring, which may differ between the groups. The asymptotic distribution of the test statistic as well as of its bootstrap counterpart are derived under the null hypothesis, and their consistency is proven for general alternatives. We demonstrate the finite sample superiority of the proposed test over some existing methods in a simulation study and illustrate its application by a real-data example.

Some statistical considerations in the clinical development of cancer immunotherapies.

Immuno-oncology has emerged as an exciting new approach to cancer treatment. Common immunotherapy approaches include cancer vaccine, effector cell therapy, and T-cell-stimulating antibody. Checkpoint inhibitors such as cytotoxic T lymphocyte-associated antigen 4 and programmed death-1/L1 antagonists have shown promising results in multiple indications in solid tumors and hematology. However, the mechanisms of action of these novel drugs pose unique statistical challenges in the accurate evaluation of clinical safety and efficacy, including late-onset toxicity, dose optimization, evaluation of combination agents, pseudoprogression, and delayed and lasting clinical activity. Traditional statistical methods may not be the most accurate or efficient. It is highly desirable to develop the most suitable statistical methodologies and tools to efficiently investigate cancer immunotherapies. In this paper, we summarize these issues and discuss alternative methods to meet the challenges in the clinical development of these novel agents. For safety evaluation and dose-finding trials, we recommend the use of a time-to-event model-based design to handle late toxicities, a simple 3-step procedure for dose optimization, and flexible rule-based or model-based designs for combination agents. For efficacy evaluation, we discuss alternative endpoints/designs/tests including the time-specific probability endpoint, the restricted mean survival time, the generalized pairwise comparison method, the immune-related response criteria, and the weighted log-rank or weighted Kaplan-Meier test. The benefits and limitations of these methods are discussed, and some recommendations are provided for applied researchers to implement these methods in clinical practice.