Objective To evaluate the total, and direct effects of vitamin D, measured by circulating 25-hydroxyvitamin D [25(OH)D] levels, on GWG after adjustment for confounding variables, and then assess the indirect effects by demonstrating the role of gestational age at birth as a mediator in this association. Design A secondary analysis of data collected in a screening program in pregnancy. Setting and population Data collected in “Khuzestan Vitamin D Deficiency Screening Program in Pregnancy” was used for the present study; it was included the data of 900 pregnant women referred to the health centers of Shushtar (Khuzestan Province, Iran), whose vitamin D status during the third trimester of pregnancy was available. Methods A mediation analysis was applied to detect the causal relationship between serum level of 25(OH)D, covariates (maternal age, parity, and baseline maternal weight), mediator (gestational age), and outcome (GWG). Main outcome measures The main outcome measure of the study was gestational weight gain. Results The adjusted total effect of vitamin D on GWG was estimated 0.0699 (95%CI: 0.0537, 0.0849; P=0.000). Although, an adjusted direct effect of vitamin D on GWG was not statistically significant, the adjusted indirect effect of this micronutrient on GWG by considering gestational age as a mediator was found to be significant [0.059 (95%CI: 0.048, 0.0708; P=0.000)]. Women with severe vitamin D deficiency had the lowest speed as compared to moderate and normal levels. Conclusion This study shows that maternal vitamin D status affects the gestational weight gain by reducing the risk of preterm delivery.

Purpose: Bayesian inference has become very popular science. It offers some pragmatic approaches to account for uncertainty in inference-decision making. Various estimation methods have been introduced to implement Bayesian methods but although these algorithms are powerful they are not always easy to grasp. This paper aims to provide an intuitive framework of four key Bayesian computational methods for researchers in clinical studies. We will not cover daunting mathematical discussion of these approaches, but rather offer a non-quantitative description of these algorithms and provide some illuminating examples. Materials and methods: Bayesian computational methods namely, i) Importance sampling, ii) Rejection sampling, iii) Markov-chain Monte Carlo, iv) Data augmentation were introduced. Results and conclusions: A load of literature published on Bayesian inference has proved its popularity among researches while its concept is not straightforward for amateur learners. We showed that alternative approaches which are intuitively appealing and easy-to-understand work well in case of low-dimensional problems and appropriate Prior information such as weighted prior, otherwise MCMC is a Trouble-free tool.