Lognormal and log-logistic distribution mixture analysis and application

In this paper, we analyze a mixture of Lognormal and Log-Logistic distribution. We estimate the parameters of the introduced distribution by using the expectation-maximization (EM) algorithm. Various phenomena in the field of medicine and economy could be modeled by this mixture. In this paper, it is used to construct new mortality model for determining the unisex premium rates in life insurance. The application of the model is illustrated in the case of Serbian population and its advantages are presented in the context of life insurance premium calculation.

90-Day all-cause mortality can be predicted following a total knee replacement: an international, network study to develop and validate a prediction model

Purpose The purpose of this study was to develop and validate a prediction model for 90-day mortality following a total knee replacement (TKR). TKR is a safe and cost-effective surgical procedure for treating severe knee osteoarthritis (OA). Although complications following surgery are rare, prediction tools could help identify high-risk patients who could be targeted with preventative interventions. The aim was to develop and validate a simple model to help inform treatment choices. Methods A mortality prediction model for knee OA patients following TKR was developed and externally validated using a US claims database and a UK general practice database. The target population consisted of patients undergoing a primary TKR for knee OA, aged ≥ 40 years and registered for ≥ 1 year before surgery. LASSO logistic regression models were developed for post-operative (90-day) mortality. A second mortality model was developed with a reduced feature set to increase interpretability and usability. Results A total of 193,615 patients were included, with 40,950 in The Health Improvement Network (THIN) database and 152,665 in Optum. The full model predicting 90-day mortality yielded AUROC of 0.78 when trained in OPTUM and 0.70 when externally validated on THIN. The 12 variable model achieved internal AUROC of 0.77 and external AUROC of 0.71 in THIN. Conclusions A simple prediction model based on sex, age, and 10 comorbidities that can identify patients at high risk of short-term mortality following TKR was developed that demonstrated good, robust performance. The 12-feature mortality model is easily implemented and the performance suggests it could be used to inform evidence based shared decision-making prior to surgery and targeting prophylaxis for those at high risk. Level of evidence III.

Common Factor Cause-Specific Mortality Model

Recent pension reforms in Europe have implemented a link between retirement age and life expectancy. The accurate forecast of life tables and life expectancy is hence paramount for governmental policy and financial institutions. We developed a multi-population mortality model which includes a cause-specific environment using Archimedean copulae to model dependence between various groups of causes of death. For this, Dutch data on cause-of-death mortality and cause-specific mortality data from 14 comparable European countries were used. We find that the inclusion of a common factor to a cause-specific mortality context increases the robustness of the forecast and we underline that cause-specific mortality forecasts foresee a more pessimistic mortality future than general mortality models. Overall, we find that this non-trivial extension is robust to the copula specification for commonly chosen dependence parameters.

A Square-Root Factor-Based Multi-Population Extension of the Mortality Laws

In this paper, we present and calibrate a multi-population stochastic mortality model based on latent square-root affine factors of the Cox-Ingersoll and Ross type. The model considers a generalization of the traditional actuarial mortality laws to a stochastic, multi-population and time-varying setting. We calibrate the model to fit the mortality dynamics of UK males and females over the last 50 years. We estimate the optimal states and model parameters using quasi-maximum likelihood techniques.

O-004 Self-correction in human preimplantation development: What do we know?

text Recent advances in preimplantation genetic testing for aneuploidy (PGT-A) and time-lapse imaging have improved our understanding of the early human embryo confirming the variable patterns of development and chromosomal status. Aneuploidy is common and increased sensitivity in PGT-A allows the non-binary reporting of euploid-aneuploid mosaicism. The PGT-A result is the inference of the biopsied embryo’s ploidy status at a point in time, by assessment of a small percentage of cells, and, whilst concordance with the rest of the embryo is high; it is not absolute. Many reports have demonstrated that, with the transfer of embryos with increasing severity and complexity of mosaicism, comes compromised implantation, reduced ongoing pregnancy rates and increased miscarriage rates. Segmental mosaic embryos have been reported to have slightly reduced implantation potential compared with euploid counterparts. However, complex mosaic embryos are widely reported to result in severely reduced implantation success, if transferred. Outside of PGT-A treatment cycles, undoubtedly fertility clinics are unwittingly transferring mosaic and aneuploid embryos daily, with variable success. The transfer of embryos in which mosaicism has been detected, although associated with lower implantation and higher miscarriage rates than euploid embryos, can lead to normal pregnancies and healthy births. We know that the placenta can harbour chromosomal aberrations which are absent from the fetus, and there are few reports of births with demonstrably high levels of mosaicism through fetal development. This raises the question as to whether correction mechanisms exist. In other words, do conceptuses become chromosomally more normal as development progresses, and what are the mechanisms, if so? PGT-A, time lapse, novel live cell imaging and in vitro model techniques have enabled a more detailed study of early embryo development and consideration of the phenomenon of self-correction. This has provided insights and hypotheses surrounding the mechanisms of development and of self-correction. The relatively lower levels of chromosome abnormality in the blastocyst, compared with cleavage stage, are well documented and indicative of some form of correction. A recent investigation reported that a large proportion of embryos initially diagnosed as mosaic were later diagnosed as euploid when assessed at day 12 of development; providing evidence of the depletion of abnormal cells throughout the early post-implantation stages. There are many time-lapse reports of anomalous ‘direct’ or multichotomous blastomere divisions being associated with aneuploidy, and leading to developmental arrest or reduced implantation potential and of temporal delays in aneuploid embryos compared with their euploid counterparts. It is possible, therefore, that errors and even attempts to repair them, in individual cells in the rapidly developing embryo; which involve complex biochemical systems, could delay karyo- and cytokinesis, resulting in these detectable delays. The embryonic mortality model suggests that there is selection against embryos based on their degree of aneuploidy, such that aneuploid cell lines are lost during implantation. We know that irregularities in blastomere cleavage can generate chromosome segregation errors but these may sporadically be confined to cells excluded, or extruded, from the morula or from the blastocyst; a possible exhibition of the clonal depletion or embryo mortality model. The trisomic/monosomic rescue model suggests that aneuploid cells can give rise to diploid cells (and possibly uniparental disomy) through mitotic chromosome losses or gains. We know that an abnormal number of pronuclei does not always produce an aneuploid blastocyst and that early embryos exhibiting multinucleation can result in healthy live births. Finally, the preferential allocation of aneuploid cells to the trophectoderm model is based on the hypothesis that euploid cells are preferentially retained in the ICM in order to achieve viability. This presentation aims to consider what we know, and discuss the theories and available evidence for self-correction.

Coherent Mortality Model in A State-Space Approach

Mortality improvements that have recently become apparent in most developing countries have significantly shaped queries on forecast divergent between populations in recent years. Therefore, to ensure a more coherent way of forecasting, previous researchers have proposed multi-population mortality model in the form of independent estimation procedures. However, similar to single-population mortality model, such independent approaches might lead to inaccurate prediction interval. As a result of this inaccurate mortality forecasts, the life expectancies and the life annuities that the mortality model aims to generate is underestimated. In this study, we propose another new extension of the multi-population mortality model in a joint estimation approach by recasting the model into a state-space framework. A combination of augmented Li-Lee and O’Hare-Li methods are employed, before we transform the proposed model into a state-space formulation. In addition, this study incorporates the quadratic age effect parameter to the proposed model to better capture the younger ages mortality. We apply the method to gender and age-specific data for Malaysia. The results show that our latter framework brings a significant contribution to the multi-population mortality model due to the incorporation of joint-estimate and quadratic age effect parameters into the model’s structure. Consequently, the proposed model improves the mortality forecast accuracy.