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doc - Cynosure Health
doc - Cynosure Health

... discharged. The fields listed above were used to create an odds ratio describing the characteristics power in predicting readmission (formula 1). The odds ratios with their corresponding characteristics were rank ordered. The top 10 characteristics were used to perform a linear regression analysis. ...
Elevated triglycerides/HDL-cholesterol ratio associated with insulin
Elevated triglycerides/HDL-cholesterol ratio associated with insulin

... terol particles.24 In addition, TG content in liver25-27 and muscle28-31 tissues is a determining factor for the development of IR. These findings support the important role played by TGs in IR. Hence, the results of this study showed a significant association, which translates into the possibility ...
toxicology news- 9- lm - American Clinical Solutions
toxicology news- 9- lm - American Clinical Solutions

... urinary samples always raises a red flag for illicit methamphetamine use in patients. A common question ensued is, "What is the source of methamphetamine?" (e.g., from street drugs or prescription drugs), since several prescription drugs and one over-the-counter inhaler can either contain or be conv ...
Perioperative low cardiac output syndrome
Perioperative low cardiac output syndrome

... myocardial oxygen consumption, intracellular calcium concentrations, or an adverse effect on diastolic function. ...
Descriptive statistical methods and comparison measures
Descriptive statistical methods and comparison measures

... The difference in the mean of an outcome variable between two independent samples is generally assessed using the t-test (validity condition: approximate normality and similar variability of the data in both groups or sufficiently large sample sizes.) If data have a skewed distribution (e.g., lab me ...
Advanced Statistics: Up with Odds Ratios! A Case for Odds Ratios
Advanced Statistics: Up with Odds Ratios! A Case for Odds Ratios

... no matter what the data show unless the RRs for the lower-risk groups were below 1.67. In fact, to take the extreme case, had the observed mortality in patients in the high-risk group receiving DCLHb been 100%, and ignoring random error, it would be reasonable to argue that the effect of DCLHb is ac ...
Day2
Day2

... In health, data are often in categories. Categories could be dichotomous, people having or not having disease, male or female, case or control, etc. Categories could be age group, ethnic group, treatment groups etc. We basically count the number in each category and present in terms of frequency dis ...
Back to Basics, 2003 POPULATION HEALTH
Back to Basics, 2003 POPULATION HEALTH

... samples from those populations. Inferences are valid only if samples are representative (to avoid bias). • Polls, surveys, etc. use inferential statistics to infer what the population thinks based on a few people. • RCT’s used them to infer treatment effects, etc. • 95% confidence intervals are a ve ...
Epidemiology and Community Medicine (3)
Epidemiology and Community Medicine (3)

... some common non-antineoplastic drugs may affect the course of cancer. The authors present two cases that appear to be consistent with such a possibility: that of a 63-year-old woman in whom a high-grade angiosarcoma of the forehead improved after discontinuation of lithium therapy and then progresse ...
Waist to Hip Ratio
Waist to Hip Ratio

... What is Waist-to-Hip Ratio? Waist-to-Hip Ratio (WHR) looks at the proportion of fat stored on your body in the waist and hip areas. Most people store their body fat in two ways: around the middle (the apple shape) and around the hips (the pear shape). People who tend to gain weight mostly in their h ...
Descriptive Epidemiology Dr. KANUPRIYA CHATURVEDI
Descriptive Epidemiology Dr. KANUPRIYA CHATURVEDI

... Poisson probability distribution is an inferential statistics probability ...
Biostatistics Breakdown
Biostatistics Breakdown

... ▫ Used when outcome data is categorical (binary) ...
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Odds ratio

In statistics, the odds ratio (usually abbreviated ""OR"") is one of three main ways to quantify how strongly the presence or absence of property A is associated with the presence or absence of property B in a given population. If each individual in a population either does or does not have a property ""A"", (e.g. ""high blood pressure""), and also either does or does not have a property ""B"" (e.g. ""moderate alcohol consumption"") where both properties are appropriately defined, then a ratio can be formed which quantitatively describes the association between the presence/absence of ""A"" (high blood pressure) and the presence/absence of ""B"" (moderate alcohol consumption) for individuals in the population. This ratio is the odds ratio (OR) and can be computed following these steps: For a given individual that has ""B"" compute the odds that the same individual has ""A"" For a given individual that does not have ""B"" compute the odds that the same individual has ""A"" Divide the odds from step 1 by the odds from step 2 to obtain the odds ratio (OR).The term ""individual"" in this usage does not have to refer to a human being, as a statistical population can measure any set of entities, whether living or inanimate.If the OR is greater than 1, then having ""A"" is considered to be ""associated"" with having ""B"" in the sense that the having of ""B"" raises (relative to not-having ""B"") the odds of having ""A"". Note that this is not enough to establish that B is a contributing cause of ""A"": it could be that the association is due to a third property, ""C"", which is a contributing cause of both ""A"" and ""B"" (confounding).The two other major ways of quantifying association are the risk ratio (""RR"") and the absolute risk reduction (""ARR""). In clinical studies and many other settings, the parameter of greatest interest is often actually the RR, which is determined in a way that is similar to the one just described for the OR, except using probabilities instead of odds. Frequently, however, the available data only allows the computation of the OR; notably, this is so in the case of case-control studies, as explained below. On the other hand, if one of the properties (say, A) is sufficiently rare (the ""rare disease assumption""), then the OR of having A given that the individual has B is a good approximation to the corresponding RR (the specification ""A given B"" is needed because, while the OR treats the two properties symmetrically, the RR and other measures do not).In a more technical language, the OR is a measure of effect size, describing the strength of association or non-independence between two binary data values. It is used as a descriptive statistic, and plays an important role in logistic regression.
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