Although several causal models relevant to epidemiology have been proposed, a key question that has remained unanswered is why some people at high-risk for a particular disease do not develop the disease while some people at low-risk do develop it. The equivalence model, proposed herein, addresses this dilemma. The equivalence model provides a graphical description of the overall effect of risk and protective factors at the individual level. Risk factors facilitate the occurrence of the outcome (the development of disease), whereas protective factors inhibit that occurrence. The equivalence model explains how the overall effect relates to the occurrence of the outcome. When a balance exists between risk and protective factors, neither can overcome the other; therefore, the outcome will not occur. Similarly, the outcome will not occur when the units of the risk factor(s) are less than or equal to the units of the protective factor(s). In contrast, the outcome will occur when the units of the risk factor(s) are greater than the units of the protective factor(s). This model can be used to describe, in simple terms, causal inferences in complex situations with multiple known and unknown risk and protective factors. It can also justify how people with a low level of exposure to one or more risk factor(s) may be affected by a certain disease while others with a higher level of exposure to the same risk factor(s) may remain unaffected.

Objective: There has remained a need to better understanding of prognostic factors that affect the survival or risk in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), particularly in developing countries. The aim of the present study aimed to identify the prognostic factors influencing AIDS progression in HIV positive patients in Hamadan province of Iran, using random survival forest in the presence of competing risks (death from causes not related to AIDS). This method considers all interactions between variables and their nonlinear effects. Method(s): A data set of 585 HIV-infected patients extracted from 1997 to 2011 was utilized. The effect of several prognostic factors on cumulative incidence function (probability) of AIDS progression and death were investigated. Result: The used model indicated that using antiretroviral therapy tuberculosis co-infection are two top most important variables in predicting cumulative incidence function for AIDS progression in the presence of competing risks, respectively. The patients with tuberculosis had much higher predicted cumulative incidence probability. Predicted cumulative incidence probability of AIDS progression was also higher for mother to child mode of HIV transmission. Moreover, transmission type and gender were two top most important variables for the competing event. Men and those patients with IDUS transmission mode had higher predicted risk compared to others. Conclusion: Considering nonlinear effects and interaction between variables, confection with tuberculosis was the most important variable in prediction of cumulative incidence probability of AIDS progression.

Background: Survival time of lymphoma patients can be estimated with the help of microarray technology. In this study, with the use of iterative Bayesian Model Averaging (BMA) method, survival time of Mantle Cell Lymphoma patients (MCL) was estimated and in reference to the findings, patients were divided into two highrisk and low-risk groups. Materials and Methods: In this study, gene expression data of MCL patients were used in order to select a subset of genes for survival analysis with microarray data, using the iterative BMA method. To evaluate the performance of the method, patients were divided into high-risk and low-risk based on their scores. Performance prediction was investigated using the log-rank test. The bioconductor package "iterativeBMAsurv" was applied with R statistical software for classification and survival analysis. Results: In this study, 25 genes associated with survival for MCL patients were identified across 132 selected models. The maximum likelihood estimate coefficients of the selected genes and the posterior probabilities of the selected models were obtained from training data. Using this method, patients could be separated into high-risk and low-risk groups with high significance (p<0.001). Conclusions: The iterative BMA algorithm has high precision and ability for survival analysis. This method is capable of identifying a few predictive variables associated with survival, among many variables in a set of microarray data. Therefore, it can be used as a low-cost diagnostic tool in clinical research.

Background and Aim: Acquired Immunodeficiency Syndrome (AIDS) caused by Human Immunodeficiency Virus (HIV), is a chronic and potentially life-threatening disease. Numerous factors affect its development and progression. Therefore, the present study attempted to identify characteristics impacting the prognosis and progression of AIDS using multistate models. Methods & Materials: The present retrospective study consisted of 2185 patients affected with HIV referring to Behavioral Disease Counseling Centers in Tehran City, Iran, from 2004 to 2013. We considered multiple states of AIDS, tuberculosis, and tuberculosis/AIDS in the natural history of the disease (from the onset of HIV disease until death occurred). Then, we applied the multistate models, to examine the effect of contextual demographic and clinical variables on survival time; subsequently, the transition probabilities of HIV. Ethical Considerations: This study was approved by the Research Ethics Committee of Hamadan University of Medical Sciences (Code: IR.UMSHA.REC.1396.117). Results: HIV-Related deaths in individuals with an incarnation history were 2.40 times higher than in those without the prison history. Death risk was also 1.70 and 1.80 times higher in those aged 25-44 and 44 years, respectively, compared to the individuals aged less than 25 years. An inverse relationship was also found between CD4 levels and the risk of death in our participants. Conclusion: Antiretroviral therapy, CD4 count, age, and history of imprisonment were the main factors in the progression of the disease and subsequent death in HIV patients. Thus, preventing the further spread of the disease to the community and controlling the disease in the patients requires targeted educational and therapeutic interventions; accordingly, the community will be familiarized with transmission routes and the preventing principle of disease. Furthermore, we can encourage patients to visit the healthcare centers early.

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