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    Positive Predictive Value and Prevalence

    Diagnostic Testing Positive Predictive Value PPV Prevalence

    Positive Predictive Value (PPV) and Prevalence

    Increased prevalence of an outcome will lead to more cases being "picked up"

    Positive predictive value (PPV) is the likelihood that a person with a "+" on a diagnostic test actually has the disease state as it is detected using a "gold standard." Another way of defining PPV is how believable a "+" test result is in a given population.
     
    As the prevalence of a disease state in a given population increases, the positive predictive value of a test will increase. This is simply due to the fact that there are more cases or disease states that can be detected.

    If you are working with a rare outcome in a given population, be aware that less prevalent outcomes increase the number of false positives detected by a diagnostic test. By definition, lower prevalence dictates that there are not many true positives or "sick" people in a given population. With so few actual cases and more people being tested, the inherent measurement error associated with diagnostic testing will yield more and more false positives.    

    So, in conclusion, it is very important to know the baseline prevalence of your outcome or disease state in your population of interest when assessing diagnostic tests. Higher prevalence leads to increased PPV and lower prevalence leads to increased false positives.
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    Statistical nuggets by Dr. Heidel

    OK, everyone. My IT expert has told me that an integral part of getting the website out there is to have a blog. I find blogs to be self-serving pedestals where people proselytize everything about themselves for sake of attention. This will NOT be the purpose of my blog. I built the website to help people conduct research. So, instead of embellishing my vanity using this technological medium, "IT expert" and I decided it would be pertinent to give little "statistical snippets" each day.  You all can tell me to stop or to keep going.  Here we go...

    Statistical nugget #1: ALWAYS check your continuous variables for normality before running any statistical analyses. An outlier, defined as any observation that is more than 3.29 standard deviations away from the mean of your continuous variable, will artificially inflate or deflate your t-values. Use skewness and kurtosis statistics to assess the assumption of normality for each continuous variable in your dataset. Visit www.scalelive.com/statistics-engine.html to learn more about normality and running these analyses in SPSS.