Like the other statistical techniques we’ve talked about, regression techniques allow us to examine the relationship between two variables. But the regression techniques go a step beyond the Chi-Square and T-Test because they allow us to examine the relationship of multiple variables.  Unlike the previous tests, regression allows you to find the correlation of multiple variables at one time, and then see what portion of the variation is due to each individual variable. This allows for control of potentially confounding variables, and is helpful for supporting the presence or absence of causation. In studies that use regression techniques you may read the terms dependent andRead More →

On Wednesday we used a T-Test to see if there was a difference in mean labor time between women who worked as a doula for income and those that worked as a doula for hobby. The next obvious question is, what is a T-Test and why did we choose to use it? Remember back to last week when we talked about a chi-square?  With a chi-square we were able to see if two groups differed on a characteristic that was a categorical variable. This means we could look for differences that are basically yes or no categories.  For example, those who had gestational diabetes andRead More →

On Monday we talked about how p-values tell us the probability of obtaining the result if the null hypothesis (the status quo) is true.  Today we turn our attention to the confidence interval and the additional information using a confidence interval provides. There are two things you need to remember to make sense of a confidence interval.  First, your sample provides an estimate of the true value.  Second, if you took a different sample, you would get a different estimate.  So what does this mean?Read More →

We’ve talked about data and the descriptive statistics.  We’ve talked about the measures we use and stratifying by populations and sub populations to assess disparities.  We talked about the types of studies and last week we moved into our first statistical test of a hypothesis, the chi-square.  Today we will talk about how we know the results of our statistical test are significant by using a p-value. P-Value The p-value is the probability of obtaining the result if the null hypothesis is true.  What level of a p-value is significant is actually arbitrary – it simply needs to be selected before the experiment begins. MostRead More →

In this study, Pregnancy outcome in women with previous one cesarean section, the authors did many statistical comparisons of the data. One comparison is easily understood with a chi-square – does having a previous vaginal delivery increase the success rate of VBAC?Read More →

Before we move into our example study we need to spend a few minutes on the difference between risk and odds.  It can be difficult to understand, but it is important for interpretation of studies. When research presents risk, what is being presented is the number of people with the selected characteristic divided by the total number of people. When research presents odds, what is being presented is the number of people with the selected characteristic divided by the number of people without the characteristic. Visualize this difference by thinking of a set of dice.  With six sides, each having a different number between 1-6,Read More →

  We have been talking about statistics, and how to understand the statistics piece of a research article. Today we will start looking at the most common statistical tests used in health-care research. We will not talk about how to do these tests. Instead we will focus on how and why these tests are used. We will begin with the Chi-square. Chi-square is used to analyze the difference in proportions for categorical data. If you remember, categorical data is information with discrete groups like race, sex, or the rating out of ten given to pain during pushing stage. A proportion is simply the number inRead More →

You’ve designed your study, recruited a sample and collected data. Maybe your study finds 20% of the women who gave birth at a birth center had at least a second degree tear, while 25% of the women who gave birth at a hospital had at least a second degree tear.  The next question you need to ask yourself is this: Does this difference represent a real difference, or is this difference due to the random variation I should expect when I sample and measure?Read More →

No doubt you’ve seen something like this hierarchy of studies from Duke University’s Introduction to Evidence-Based Practice site. In this hierarchy, the studies are classified by the type of sample used. Types of Samples In a case study or case report, the sample is one or two individuals who experienced something unusual or noteworthy. In a case-control study, the sample is a group of individuals with the characteristic of interest (usually diagnosed with something) called the cases and another set of individuals who do not have that characteristic called the controls.  The controls are matched to the cases on as many demographic and health criteria asRead More →

When we compare two populations and find a difference in a health outcome, we want to look more closely to determine if that outcome difference is due to a difference in health related characteristics or non-health related characteristics.  When the difference is due to something other than health characteristics, the difference is considered a disparity. Disparities are particularly disturbing because it means something about the health system or society is causing differences in health outcomes.  Globally, disparities in maternal health have to do with the distribution of health care resources. Industrialized countries have higher rates of cesarean delivery and lower rates of maternal mortality thanRead More →