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 and those who did not or those with an intact perinium and those without. This works well for some things, but what if you wanted to know how big the difference was?
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? Continue reading
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.
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. Most commonly researchers will use a p-value of 0.05 or less as significant. But what does this really mean? Continue reading
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? Continue reading
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, your risk of rolling a 1 is 1 divided by six which is 0.167. But your odds of rolling a 1 is 1 divided by 5 (the total number of non-one options) which is 0.20. Continue reading
I’ve seen bits and pieces of the Full 40 Campaign, but took the time to dig out all the materials after attending the AWHONN Conference last week. As usual, there are some things I like, and some I wonder about.
What do I like? AWHONN has produced some easy to share materials. You can find them at the Healthy Mom & Baby website. There is also a collection of social media images you can use. These were harder to find – I eventually had to copy them from the Facebook album to get them into Pinterest to share. Continue reading
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 in the group who experienced something.
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?
Here are some updates from around the birth world…
The National Partnership for Women and Families released its report: Expecting Better: A State by State Analysis of Laws that Help New Parents.
Midwives Alliance of North America released the I am a Midwife Education Campaign.
Her Royal Highness Princess Sarah Zeid of Jordan wrote a piece on Global Motherhood titled Delivering Joy: Midwives are the Key to a Future With Healthy Mothers and Babies.
Be sure to support the “Improving Access to Maternity Care Act of 2014″
Upcoming Conferences and Webinars:
Check the Birth Professional Conferences Calendar for upcoming events including the Midwives Alliance of North America and the combined Lamaze/DONA conference.
ACNM is hosting a webinar to educate midwives about the Affordable Care Act’s birth control and breastfeeding benefits on June 25.
And don’t forget…
ACNM is accepting abstract submissions for the 2015 Annual Meeting through August 4.
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 as possible to allow researches to identify meaningful differences between the groups (the differences that lead to the diagnosis). Here is an example of a case-control study: Identifying risk factors for recurrent cesarean scar pregnancy: a case-control study. Continue reading