Search results for: Gebreegziabher Hailu Gebrecherkos

Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

Abstract:

In biomedical researches and randomized clinical trials, the most commonly interested outcomes are time-to-event so-called survival data. The importance of robust models in this context is to compare the effect of randomly controlled experimental groups that have a sense of causality. Causal estimation is the scientific concept of comparing the pragmatic effect of treatments conditional to the given covariates rather than assessing the simple association of response and predictors. Hence, the causal effect based semiparametric transformation model was proposed to estimate the effect of treatment with the presence of possibly time-varying covariates. Due to its high flexibility and robustness, the semiparametric transformation model which shall be applied in this paper has been given much more attention for estimation of a causal effect in modeling left-truncated and right censored survival data. Despite its wide applications and popularity in estimating unknown parameters, the maximum likelihood estimation technique is quite complex and burdensome in estimating unknown parameters and unspecified transformation function in the presence of possibly time-varying covariates. Thus, to ease the complexity we proposed the modified estimating equations. After intuitive estimation procedures, the consistency and asymptotic properties of the estimators were derived and the characteristics of the estimators in the finite sample performance of the proposed model were illustrated via simulation studies and Stanford heart transplant real data example. To sum up the study, the bias of covariates was adjusted via estimating the density function for truncation variable which was also incorporated in the model as a covariate in order to relax the independence assumption of failure time and truncation time. Moreover, the expectation-maximization (EM) algorithm was described for the estimation of iterative unknown parameters and unspecified transformation function. In addition, the causal effect was derived by the ratio of the cumulative hazard function of active and passive experiments after adjusting for bias raised in the model due to the truncation variable.

Keywords: causal estimation, EM algorithm, semiparametric transformation models, time-to-event outcomes, time-varying covariate

Procedia PDF Downloads 125

Authors: Mekonen Hailu, Liu Liping

Abstract:

Cage aquaculture is highly preferred due to higher production per unit volume of water, lower costs of investment, and simpler routine farm management procedures compared to pond systems. In the 1980s, cage culture was first used on a trial basis in sub-Saharan Africa. Over the past 20 years, a small number of prosperous freshwater cage culture operations have started to emerge in Egypt, Rwanda, Kenya, Uganda, Tanzania, Ghana, Malawi, Zambia and Zimbabwe. Brackish and marine cage culture also offers a lot of potential, although this subsector hasn't seen any significant commercial growth to date. In 2019, 263 cage aquaculture installations on the African inland waters on 18 water bodies within eight countries with an estimated 20,114 cages were reported. The lakes Victoria, Kariba, Volta, and River Volta, which together account for 82.9% of all cage aquaculture installations regarded as sub-Saharan Africa's principal cage aquaculture regions (Fig 1). Except few small-scale trials with North African catfish (Clarias gariepinus), almost all farms in Sub-Saharan Africa and Egypt grow Nile tilapia (Oreochromis niloticus). More than 247,398 tonnes of fish are produced yearly from ten African countries through cage aquaculture. The expansion of cage culture in Africa provides job opportunities for both skilled and unskilled workers, nutritious food and foreign currency. The escaping non-native strains of tilapia in Lake Volta and the occurrence of a risky Tilapia lake virus (syncytial hepatitis), which has the potential to wipe out entire populations in both wild and farmed Nile tilapia on Lake Victoria, are threats coming with the expansion of cage aquaculture in Africa. In addition, the installations of 138 cage aquacultures were found in contrary to best cage culture practices. To sustain cage aquaculture development and maintain harmony with other water uses, developers must strictly abide by best practices. Hence, the exclusion of protected areas and small lakes (average depth 5 m or less) should be done, as well an Environmental Impact Assessment should be conducted before establishing the cage farms.

Keywords: Africa, cage aquaculture, production, threats

Procedia PDF Downloads 70

Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

Abstract:

a systematic observation from a defined time of origin up to certain failure or censor is known as survival data. Survival analysis is a major area of interest in biostatistics and biomedical researches. At the heart of understanding, the most scientific and medical research inquiries lie for a causality analysis. Thus, the main concern of this study is to investigate the causal effect of treatment on survival time conditional to the possibly time-varying covariates. The theory of causality often differs from the simple association between the response variable and predictors. A causal estimation is a scientific concept to compare a pragmatic effect between two or more experimental arms. To evaluate an average treatment effect on survival outcome, the estimating equation was adjusted for time-varying covariates under the semi-parametric transformation models. The proposed model intuitively obtained the consistent estimators for unknown parameters and unspecified monotone transformation functions. In this article, the proposed method estimated an unbiased average causal effect of treatment on survival time of interest. The modified estimating equations of semiparametric transformation models have the advantage to include the time-varying effect in the model. Finally, the finite sample performance characteristics of the estimators proved through the simulation and Stanford heart transplant real data. To this end, the average effect of a treatment on survival time estimated after adjusting for biases raised due to the high correlation of the left-truncation and possibly time-varying covariates. The bias in covariates was restored, by estimating density function for left-truncation. Besides, to relax the independence assumption between failure time and truncation time, the model incorporated the left-truncation variable as a covariate. Moreover, the expectation-maximization (EM) algorithm iteratively obtained unknown parameters and unspecified monotone transformation functions. To summarize idea, the ratio of cumulative hazards functions between the treated and untreated experimental group has a sense of the average causal effect for the entire population.

Keywords: a modified estimation equation, causal effect, semiparametric transformation models, survival analysis, time-varying covariate

Procedia PDF Downloads 175