An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

Background Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial’s follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. Methods We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. Results There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily included any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. Conclusions We present a straightforward regression-based method for computing the EVSI of extending an existing trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. Highlights Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we have developed new methods for computing the EVSI of extending a trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations. The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.

Analyzing Infant Mortality in Rural Bangladesh: A Frailty Modeling Approach

In practice, it may happen that data may arise from a hierarchical structure i.e., a cluster is nested within another cluster. In this case, nested frailty model is appropriate to analyze survival data to obtain optimal estimates of the parameters of interest. To identify significant determinants of infant mortality in rural Bangladesh, survival data have been extracted from Bangladesh Demographic and Health Survey (BDHS), 2014. Because of the presence of two-level clustering in data, nested frailty model has been employed for the purpose of analysis. Recommendations have been suggested based on the results obtained from the survival model to reduce the infant mortality in rural Bangladesh to a great extent. Dhaka Univ. J. Sci. 69(2): 63-69, 2021 (July)

Cost-Effectiveness Analysis of Pembrolizumab Plus Pemetrexed and Platinum Versus Chemotherapy Alone as First-Line Treatment in Metastatic Non-Squamous Non–Small Cell Lung Cancer: A Reconstruction of Partitioned Survival Model Based on Time Dependent Pricing Mechanism of Patient Assistance Program

ObjectivesA new patient assistance program (PAP) for pembrolizumab was started in China in 2021. The researchers aimed to evaluate the economic outcomes of pembrolizumab plus pemetrexed and platinum versus chemotherapy alone in the first-line treatment of patients with metastatic non-squamous non-small cell lung cancer, based on the pricing mechanism of PAP.Material and MethodsSurvival analysis and partitioned survival model were performed to evaluate the incremental cost-effectiveness ratio (ICER) in the pembrolizumab group compared with the chemotherapy group. Survival probabilities were extracted from the data of the KEYNOTE-189 trial. Cost and utility data were gathered from published literature. The pricing mechanism of PAP was set in each cycle in the partitioned survival model, according to the progression-free survival (PFS) data of the KEYNOTE-189 trial, which included PFS-1 and PFS-2. Deterministic sensitivity analysis and probabilistic sensitivity analysis were conducted.ResultsThe ICER of the pembrolizumab group versus chemotherapy group was $65,272/quality-adjusted life year (QALY), which still exceeded the willingness-to-pay threshold of three times per capita gross domestic product (GDP) of China ($33,581.22), although PAP was calculated. Sensitivity analysis implied that the price of chemotherapeutic drugs combined with pembrolizumab was one of the main influencing factors of ICER.ConclusionsDue to various prices set by PAP and the payment for combined chemotherapy, the economic advantage of pembrolizumab plus chemotherapy in the first-line treatment of non-small cell lung cancer (NSCLC) is still not achieved in China.

Construction of a single nucleotide variant score-related gene-based prognostic model in hepatocellular carcinoma: analysis of multi-independent databases and validation in vitro

Background The accumulation of single nucleotide variants (SNVs) and the emergence of neoantigens can affect tumour proliferation and the immune microenvironment. However, the SNV-related immune microenvironment characteristics and key genes involved in hepatocellular carcinoma (HCC) are still unclear. We aimed to evaluate differences in the SNV-related immune microenvironment, construct a prognostic model and validate the key genes in vitro. Methods The categories of samples were defined by the expression of SNV score-related genes to evaluate the differences in mutational features, immune environment and prognosis. The survival model was constructed with survival-associated genes and verified in two independent test datasets. RCAN2, the key gene screened out for biofunction, was validated in vitro. Results IC2, among the three integrated clusters (IC1, IC2, IC3) classified by the 82 SNV score-related genes, was distinct from the rest in SNV score and immune cell infiltration, showing a better prognosis. Seven prognostic markers, HTRA3, GGT5, RCAN2, LGALS3, CXCL1, CLEC3B, and CTHRC1, were screened to construct a prognostic model. The survival model distinguished high-risk patients with poor prognoses in three independent datasets (log-rank P < 0.0001, 0.011, and 0.0068, respectively) with acceptable sensitivity and specificity. RCAN2 was inversely correlated with NK cell infiltration, and knockdown of RCAN2 promoted proliferation in HCC. Conclusions This study revealed the characteristics of the HCC SNV-associated subgroup and screened seven latent markers for their accuracy of prognosis. Additionally, RCAN2 was preliminarily proven to influence proliferation in HCC and it had a close relationship with NK cell infiltration in vitro. With the capability to predict HCC outcomes, the model constructed with seven key differentially expressed genes offers new insights into individual therapy.

Fatigue in Incident Peritoneal Dialysis and Mortality: A Real-World Side-by-Side Study in Brazil and the United States

Background: We tested if fatigue in incident Peritoneal Dialysis associated with an increased risk for mortality, independently from main confounders. Methods: We conducted a side-by-side study from two of incident PD patients in Brazil and the United States. We used the same code to independently analyze data in both countries during 2004 to 2011. We included data from adults who completed KDQOL-SF vitality subscale within 90 days after starting PD. Vitality score was categorized in four groups: >50 (high vitality), >=40 to <=50 (moderate vitality), >35 to <40 (moderate fatigue), <=35 (high fatigue; reference group). In each country's cohort, we built four distinct models to estimate the associations between vitality (exposure) and all-cause mortality (outcome): (i) Cox regression model; (ii) competitive risk model accounting for technique failure events; (iii) multilevel survival model of clinic-level clusters; (iv) multivariate regression model with smoothing splines treating vitality as a continuous measure. Analyses were adjusted for age, comorbidities, PD modality, hemoglobin, and albumin. A mixed-effects meta-analysis was used to pool hazard ratios (HRs) from both cohorts to model mortality risk for each 10-unit increase in vitality. Results: We used data from 4,285 PD patients (Brazil n=1,388 and United States n=2,897). Model estimates showed lower vitality levels within 90 days of starting PD were associated with a higher risk of mortality, which was consistent in Brazil and the United States cohorts. In the multivariate survival model, each 10-unit increase in vitality score was associated with lower risk of all-cause mortality in both cohorts (Brazil HR=0.79 [95%CI 0.70 to 0.90] and United States HR=0.90 [95%CI 0.88 to 0.93], pooled HR=0.86 [95%CI 0.75 to 0.98]). Results for all models provided consistent effect estimates. Conclusions: Among patients in Brazil and the United States, lower vitality score in the initial months of PD was independently associated with all-cause mortality.