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Objectives: Immuno-oncology (IO) therapies are often associated with delayed responses that are deep and durable, manifesting as long-term survival benefits in patients with metastatic cancer. Complex hazard functions arising from IO treatments may limit the accuracy of extrapolations from standard parametric models (SPMs). We evaluated the ability of flexible parametric models (FPMs) to improve survival extrapolations using data from 2 trials involving patients with non–small-cell lung cancer (NSCLC). Methods: Our analyses used consecutive database locks (DBLs) at 2-, 3-, and 5-y minimum follow-up from trials evaluating nivolumab versus docetaxel in patients with pretreated metastatic squamous (CheckMate-017) and nonsquamous (CheckMate-057) NSCLC. For each DBL, SPMs, as well as 3 FPMs—landmark response models (LRMs), mixture cure models (MCMs), and Bayesian multiparameter evidence synthesis (B-MPES)—were estimated on nivolumab overall survival (OS). The performance of each parametric model was assessed by comparing milestone restricted mean survival times (RMSTs) and survival probabilities with results obtained from externally validated SPMs. Results: For the 2- and 3-y DBLs of both trials, all models tended to underestimate 5-y OS. Predictions from nonvalidated SPMs fitted to the 2-y DBLs were highly unreliable, whereas extrapolations from FPMs were much more consistent between models fitted to successive DBLs. For CheckMate-017, in which an apparent survival plateau emerges in the 3-y DBL, MCMs fitted to this DBL estimated 5-y OS most accurately (11.6% v. 12.3% observed), and long-term predictions were similar to those from the 5-y validated SPM (20-y RMST: 30.2 v. 30.5 mo). For CheckMate-057, where there is no clear evidence of a survival plateau in the early DBLs, only B-MPES was able to accurately predict 5-y OS (14.1% v. 14.0% observed [3-y DBL]). Conclusions: We demonstrate that the use of FPMs for modeling OS in NSCLC patients from early follow-up data can yield accurate estimates for RMST observed with longer follow-up and provide similar long-term extrapolations to externally validated SPMs based on later data cuts. B-MPES generated reasonable predictions even when fitted to the 2-y DBLs of the studies, whereas MCMs were more reliant on longer-term data to estimate a plateau and therefore performed better from 3 y. Generally, LRM extrapolations were less reliable than those from alternative FPMs and validated SPMs but remained superior to nonvalidated SPMs. Our work demonstrates the potential benefits of using advanced parametric models that incorporate external data sources, such as B-MPES and MCMs, to allow for accurate evaluation of treatment clinical and cost-effectiveness from trial data with limited follow-up. Highlights: Flexible advanced parametric modeling methods can provide improved survival extrapolations for immuno-oncology cost-effectiveness in health technology assessments from early clinical trial data that better anticipate extended follow-up. Advantages include leveraging additional observable trial data, the systematic integration of external data, and more detailed modeling of underlying processes. Bayesian multiparameter evidence synthesis performed particularly well, with well-matched external data. Mixture cure models also performed well but may require relatively longer follow-up to identify an emergent plateau, depending on the specific setting. Landmark response models offered marginal benefits in this scenario and may require greater numbers in each response group and/or increased follow-up to support improved extrapolation within each subgroup. [ABSTRACT FROM AUTHOR]

Overall prevalence of Malignant Haematopoietic Cancers(MHC) have increased exponentially over the years i.e. 104.88/100,000 in 1980 to 448.72/100,000 in 2016 and the number of cases diagnosed each year has also been trending upwards. Nordic registry data were examined to estimate the incidence of MHC and to predict the trends in these new cases of MHC in order to assess the future burden of the disease in the Nordic region. [Extracted from the article]

Network meta-analysis (NMA) of time-to-event outcomes often relies on pooling of hazard ratios (HR). We performed a multidimensional NMA of trials comparing PD-1/PD-L1 inhibitor plus chemotherapy with chemotherapy in the first line of treatment for advanced squamous non-small cell lung cancer. The PH assumption between the survival curves was violated, and a comparison of constant HRs seems inappropriate. [Extracted from the article]

The study evaluated the prediction accuracy of breast cancer using the K-nearest neighbor (k-NN) classifier algorithm. Methods The breast cancer dataset (containing 569 records and 32 attributes) was obtained from University of California Irvine (UCI) machine learning repository. Data were segmented into a training dataset containing the first 469 observations to build the k-NN model and a testing dataset containing the remaining observations was used to simulate new patients. [Extracted from the article]

Overall prevalence of salivary gland cancer (SGC) have increased over the years (11.55/100,000 in 1998 to 13.62/100,000 in 2015) and the number of cases diagnosed each year has also been trending upwards. Nordic registry data were examined to estimate the incidence of SGC and to predict the trends in these new cases of SGC in order to assess the future burden of the disease in the Nordics. [Extracted from the article]