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Effect decomposition and Table 2 fallacy

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 What? To show multiple adjusted effect estimates shown in a single table from a single regression model. What are the consequences? 1. Interpretative complexities- create confusion in the interpretation of direct effect estimates to total effect estimates for covariates in the model. 2. Though the effect estimate of the main exposure are not confounded but overall effect estimate is confounded due to including all the covarites in the same model 3. Effect estimate may complicate further by heterogeneity (variation, modification) of the exposure effect estimate are presented Why we need effect decomposition?: To identify the interventional factors and understand the pathways to design for intervention to improve health and prevent diseases. Few term before explaining the concept of effect decomposition: Primary effect- effect of primary exposure of interest in the initial adjustment model Secondary effect- effect of covariate (confounder or effect modifier) not of primary interest ...
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Which seed do you eat?

 Which seeds would you like to add to your healthy diet? Nutritional Comparison of Common Seeds: Nutrient Pumpkin Seeds Hemp Seeds Chia Seeds Sesame Seeds Flaxseeds Sunflower Seeds Calories (kcal) 153 157 138 160 151 164 Protein (g) 6.9 9.0 4.4 4.8 5.2 5.8 Fat (g) 13.0 13.8 8.7 13.6 12.0 14.4 Carbs (g) 5.0 2.5 12.3 7.3 8.2 5.6 Fiber (g) 1.8 1.1 9.8 4.0 7.8 2.4 Calcium (mg) 15 20 179 281 72 22 Iron (mg) 2.3 2.3 2.2 4.2 1.6 1.5 Magnesium (mg) 156 210 95 101 113 36 Zinc (mg) 2.2 3.0 1.3 2.0 1.3 1.5 Selenium (mcg) 5.2 5.7 15.7 1.6 2.6 22.5 Vitamin E (mg) 0.56 0.8 0.5 0.25 0.3 7.4 Omega-3 (ALA) (g) 0.1 0.6 5.1 0.1 6.5 0.1 Omega-6 (g) 5.8 3.8 1.6 6.0 1.7 9.5 Copper (mg) 0.4 0.5 0.1 0.7 0.6 0.5 Manganese (mg) 0.1 2.3 0.6 0.7 0.7 0.6 Phosphorus (mg) 332 469 244 181 182 185 Potassium (mg) 228 341 115 126 228 185 Lignans (mg) ...
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Propensity Score Matching (PSM)

  The multivariable scoring system is based on the value of the predictor variables to construct a PSM value. On the other hand, it is the probability that subject with given characteristics will receive treatment or not. To describe the propensity score, let the dichotomous (0,1) variable Z indicate treatment, and let X be the vector of available pretreatment covariates. The propensity score e(X) for an individual is defined as the conditional probability of being treated given his or her covariates X: e(X) = Pr(Z=1|X). The propensity score is a one-dimensional variable that summarizes the multidimensional pretreatment covariates X. Among persons with a given propensity score, the distribution of the covariates X is on average the same among the treated and untreated. v   Method: Identify candidate predictors of the two treatments, perform a logistic regression where outcome will be two treatment, obtain a predicted probability of two treatments, this probability fo...
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Selection bias in epidemiological studies

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What is selection bias? Systematic error in how subjects are selected or loss to follow-up during the study. Selection bias occurs when we found there is a discrepancy between target population and source population. This bias usually arises during the data collection procedure.  In case control study, if selection of control group  is not appropriately represented by the source population where case rises than we have selection bias.  In cohort study, if the population who are dropped out are showing differential pattern in exposure and disease groups then we can say there is a selection bias.  In the experimental trial, loss to follow-up differs from one treatment group to another. Selection bias is more concerning in a case-control study than in a cohort study because the study participants have all the available information from the source population.    Example : 1. For example, participants included in an influenza vaccine trial may be healthy young a...
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