COMPARISON OF COX PROPORTIONAL HAZARD MODEL AND ACCELERATED FAILURE TIME (AFT) MODELS: AN APPLICATION TO UNDER-FIVE MORTALITY IN UTTAR PRADESH

The statistical field of survival analysis focuses on the examination of time-to-event data. The proportional hazards (PH) model is the most widely used in multivariate survival analysis to examine the effects of various factors on survival time. The statistics, however, do not always support the PH models assumption of constant hazards. The power of the associated statistical tests is reduced when the PH assumption is broken, which leads to incorrect interpretation of the estimation results. The accelerated failure time (AFT) models, on the other hand, do not, like the PH model, assume constant hazards in the survival data. Additionally, the AFT models can be employed in place of the PH model if the constant hazards assumption violated. This study set out to examine how well the PH model and the AFT models performed when it came to identifying the proximate variables influencing under – five mortality from National Family Health Survey data in Uttar Pradesh. Three AFT models that were based on the Weibull, exponential, and log-normal distributions were the only ones discussed in this article. The research employing a graphical technique and a statistical test revealed that the NFHS-5 data set has non-proportional hazards. The log-normal AFT model was the most acceptable model among the ones studied, according to the Akaike information criterion (AIC).

Comparison of Cox proportional hazards model, Cox proportional hazards with time-varying coefficients model, and lognormal accelerated failure time model: Application in time to event analysis of melioidosis patients

Objective: To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time (AFT), Cox proportional hazards (PH), and Cox PH with timevarying coefficient (TVC) models. Methods: A retrospective study was conducted from 2014 to 2019 among 453 patients who were admitted to Hospital Sultanah Bahiyah, Kedah and Hospital Tuanku Fauziah, Perlis in Northern Malaysia due to confirmed-cultured melioidosis. The prognostic factors of mortality from melioidosis were obtained from AFT survival analysis, and Cox s models and the findings were compared by using the goodness of fit methods. The analyses were done by using Stata SE version 14.0. Results: A total of 242 patients (53.4%) survived. In this study, the median survival time of melioidosis patients was 30.0 days (95% CI 0.0-60.9). Six significant prognostic factors were identified in the Cox PH model and Cox PH-TVC model. In AFT survival analysis, a total of seven significant prognostic factors were identified. The results were found to be only a slight difference between the identified prognostic factors among the models. AFT survival showed better results compared to Cox's models, with the lowest Akaike information criteria and best fitted Cox-snell residuals. Conclusions: AFT survival analysis provides more reliable results and can be used as an alternative statistical analysis for determining the prognostic factors of mortality in melioidosis patients in certain situations.

Two-stage estimation on adjustment for cross-over in oncology trials / 中国临床药理学与治疗学

AIM: To investigate the application of two-stage estimation (TSE) on adjustment for treatment switch in oncology trials. METHODS: The theory and implementation of TSE method was described, and was applied to adjust the data from a two-arm randomized controlled trial of anti-tumor drugs. The changes of survival curves and hazard ratio of two groups after adjustment for cross-over were evaluated. In addition, the results of two-stage estimation and rank preserving structural failure time model (RPSFT) were compared. RESULTS: After adjustment for cross-over using TSE methods, the results showed that the median survival time of control group was shorter than the original one, and the hazard ratio was lower than the observed value. Moreover, TSE method showed similar results to rank preserving structural failure time model. CONCLUSION: The TSE method is relatively simple to use, reliable and has a good practice property in cross-over analysis of oncology trials. At the same time, it is necessary to pay attention to its application scopes.

Application of conditional inference forest in time-to-event data analysis / 南方医科大学学报

OBJECTIVE@#To explore the application and advantages of conditional inference forest in survival analysis.@*METHODS@#We used simulated experiment and actual data to compare the predictive performance of 4 models, including Coxproportional hazards model, accelerated failure time model, random survival forest model and conditional inference forest model based on their Brier scores.@*RESULTS@#Simulation experiment suggested that both of the two forest models had more accurate and robust predictive performance than the other two regression models. Conditional inference forest model was superior to the other models in analyzing time-to-event data with polytomous covariates, collinearity or interaction, especially for a large sample size and a high censoring rate. The results of actual data analysis demonstrated that conditional inference forest model had the best predictive performance among the 4 models.@*CONCLUSIONS@#Compared with the commonly used survival analysis methods, conditional inference forest model performs better especially when the data contain polytomous covariates with collinearity and interaction.

Subgroup identification based on an accelerated failure time model combined with adaptive elastic net / 南方医科大学学报

OBJECTIVE@#We propose a strategy for identifying subgroups with the treatment effect from the survival data of a randomized clinical trial based on accelerated failure time (AFT) model.@*METHODS@#We applied adaptive elastic net to the AFT model (designated as the penalized model) and identified the candidate covariates based on covariate-treatment interactions. To classify the patient subgroups, we utilized a likelihood-based change-point algorithm to determine the threshold cutoff point. A two-stage adaptive design was adopted to verify if the treatment effect existed within the identified subgroups.@*RESULTS@#The penalized model with the main effect of the covariates considerably outperformed the univariate model without the main effect for the trial data with a small sample size, a high censoring rate, a small subgroup size, or a sample size that did not exceed the number of covariates; in other scenarios, the latter model showed better performances. Compared with the traditional design, the adaptive design improved the power for detecting the treatment effect where subgroup effect exists with a well-controlled type Ⅰ error.@*CONCLUSIONS@#The penalized AFT model with the main effect of the covariates has advantages in subgroup identification from the survival data of clinical trials. Compared with the traditional design, the two-stage adaptive design has better performance in evaluation of the treatment effect when a subgroup effect exists.

Study on the failure time of the vaccine vial monitor used for oral poliovirus vaccine at 25 ℃ and 37 ℃ / 中华预防医学杂志

Objective@#To evaluate the failure time of vaccine vial monitor (VVM) used for oral poliovirus vaccine (OPV) at 25 ℃ and 37 ℃.@*Methods@#160 copies of VVM were produced by a company, the model was QM5D37A, samples were taken from different batches by using the method of random number table . 100 bottles of vaccine were produced by a domestic company, and samples were taken from different batches by using the method of random number table. 160 copies of labels were placed in the incubator at 25 ℃ and 37 ℃, which were used to measure the mutative color of the active region. When the values of color were equal to 40, the color of active region was the same with the reference color, and the VVM was failed. 100 bottles of vaccine were placed in the incubator at 25 ℃ and 37 ℃, which were used to measure the vaccine titer. When total vaccine titer was less than 6.12 CCID50 or vaccine titer of typeⅠ was less than 6.0 CCID50 or vaccine titer of type Ⅲ was less than 5.5 CCID50, the vaccine was failed. We drew the graph of mutative color to calculate the failure time range of VVM According to the graph , we can determine that whether the failure time of VVM was later than the time of vaccines by the data of OPV .@*Results@#The earliest failure time of OPV was 21 days at 25 ℃, and the number of samples was one; The earliest Failure time of VVM was 12.5 days at 25 ℃, and it was less than the earliest failure time of OPV. The earliest failure time of OPV was 4.0 days at 37 ℃, and the number of samples was one; The earliest Failure time of VVM was 3.1 days at 37 ℃, and it was equal to the earliest failure time of OPV.@*Conclusion@#We could know that the failure time of VVM was always earlier than the failure time of vaccines at the same temperatures . The latest failure time of VVM was equal to the earliest failure time of vaccines at 37 ℃. All of the failure times of samples were earlier than that of vaccines at 25 ℃.

Gene-Gene Interaction Analysis for the Accelerated Failure Time Model Using a Unified Model-Based Multifactor Dimensionality Reduction Method

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.