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1.3: Clinical Studies

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    Now you know how to collect a sample, next you need to learn how to conduct a study. We will discuss the basics of studies, both observational studies and experiments.

    Observational Study: This is where data is collected from just observing what is happening. There is no treatment or activity being controlled in any way. Observational studies are commonly conducted using surveys, though you can also collect data by just watching what is happening such as observing the types of trees in a forest.

    Survey: Surveys are used for gathering data to create a sample. There are many different kinds of surveys, but overall, a survey is a method used to ask people questions when interested in the responses. Examples of surveys are Internet and T.V. surveys, customer satisfaction surveys at stores or restaurants, new product surveys, phone surveys, and mail surveys. The majority of surveys are some type of public opinion poll.

    Experiment: This is an activity where the researcher controls some aspect of the study and then records what happens. An example of this is giving a plant a new fertilizer, and then watching what happens to the plant. Another example is giving a cancer patient a new medication, and monitoring whether the medication stops the cancer from growing. There are many ways to do an experiment, but a clinical study is one of the more popular ways, so we will look at the aspects of this.

    Clinical Study: This is a method of collecting data for a sample and then comparing that to data collected for another sample where one sample has been given some sort of treatment and the other sample has not been given that treatment (control). Note: There are occasions when you can have two treatments, and no control. In this case you are trying to determine which treatment is better.

    Example \(\PageIndex{1}\): Clinical Study Examples

    Here are examples of clinical studies.

    1. A researcher may want to study whether or not smoking increases a person's chances of heart disease.
    2. A researcher may want to study whether a new antidepressant drug will work better than an old antidepressant drug.
    3. A researcher may want to study whether taking folic acid before pregnancy will decrease the risk of birth defects.

    Clinical Study Terminology:

    Treatment Group: This is the group of individuals who are given some sort of treatment. The word treatment here does not necessarily mean medical treatment. The treatment is the cause, which may produce an effect that the researcher is interested in.

    Control Group: This is the group of individuals who are not given the treatment.

    Sometimes, they may be given some old treatment, or sometimes they will not be given anything at all. Other times, they may be given a placebo (see below).

    Example \(\PageIndex{2}\): Treatment/Control Group Examples

    Determine the treatment group, control group, treatment, and control for each clinical study in Example \(\PageIndex{1}\).

    Solution
    1. A researcher may want to study whether or not smoking increases a person's chances of heart disease.

    The treatment group is the people in the study who smoke and the treatment is smoking. The control group is the people in the study who do not smoke and the control is not smoking.

    1. A researcher may want to study whether a new antidepressant drug will work better than an old antidepressant drug.

    The treatment group is the people in the study who take the new antidepressant drug and the treatment is taking the new antidepressant drug. The control group is the people in the study who take the old antidepressant drug and the control is taking the old antidepressant drug. Note: In this case the control group is given some treatment since you should not give a person with depression a non-treatment.

    1. A researcher may want to study whether taking folic acid before pregnancy will decrease the risk of birth defects.

    The treatment group is the women who take folic acid before pregnancy and the treatment is taking folic acid. The control group is the women who do not take folic acid before pregnancy and the control is not taking the folic acid.

    Note: In this case, you may choose to do an observational study of women who did or did not take folic acid during pregnancy so that you are not inducing women to avoid folic acid during pregnancy which could be harmful to their baby.

    Confounding Variables: These are other possible causes that may produce the effect of interest rather than the treatment under study. Researchers minimize the effect of confounding variables by comparing the results from the treatment group versus the control group.

    Controlled Study: Any clinical study where the researchers compare the results of a treatment group versus a control group.

    Placebo: A placebo is sometimes used on the control group in a study to mimic the treatment that the treatment group is receiving. The idea is that if a placebo is used, then the people in the control group and in the treatment group will all think that they are receiving the treatment. However, the control group is merely receiving something that looks like the treatment, but should have no effect on the outcome. An example of a placebo could be a sugar pill if the treatment is a drug in pill form.

    Example \(\PageIndex{3}\): Placebo Examples

    For each situation in Example \(\PageIndex{1}\), identify if a placebo is necessary to use.

    1. A researcher may want to study whether or not smoking increases a person's chances of heart disease.

    In this example, it is impossible to use a placebo. The treatment group is comprised of people who smoke and the control group is comprised of people who do not smoke. There is no way to get the control group to think that they are smoking as well as the treatment group.

    1. A researcher may want to study whether a new antidepressant drug will work better than an old antidepressant drug.

    In this example, a placebo is not needed since we are comparing the results of two different antidepressant drugs.

    1. A researcher may want to study whether taking folic acid before pregnancy will decrease the risk of birth defects.

    In this example, the control group could be given a sugar pill instead of folic acid. However, they may think that they are taking folic acid and so the psychological effect on a person's health can be being measured. This way, when we compare the results of taking folic acid versus taking a sugar pill, we can see if there were any dramatic differences in the results.

    Blind Study: Usually, when a placebo is used in a study, the people in the study will not know if they received the treatment or the placebo until the study is completed. In other words, the people in the study do not know if they are in the treatment group or in the control group. This type of study is called a blind study. Note: When researchers use a placebo in a blind study, the people in the study are told ahead of time that they may be getting the actual treatment, or they may be getting the placebo.

    Double-Blind Study: Sometimes when researchers are conducting a very extensive study using many healthcare workers, the researchers will not tell the people in the study or the healthcare workers which patients will receive the treatment and which patients will receive the placebo. In other words, the healthcare workers who are administering the treatment or placebo to the people in the study do not know which people are in the treatment group and which people are in the control group. This type of study is called a double-blind study.

    Randomized Controlled Study: Any clinical study in which the treatment group and the control group are selected randomly from the population.

    Whether you are doing an observational study or an experiment, you need to figure out what to do with the data. You will have many data values that you collected, and it sometimes helps to calculate numbers from these data values. Whether you are talking about the population or the sample, determines what we call these numbers.

    Parameter: A numerical value calculated from a population

    Statistic: A numerical value calculated from a sample, and used to estimate the parameter

    Some examples of parameters that can be estimated from statistics are the percentage of people who strongly agree to a question and mean net worth of all Americans. The statistic would be the percentage of people asked who strongly agree to a question, and the mean net worth of a certain number of Americans.

    Notation for Parameter and Statistics:

    Parameters are usually denoted with Greek letters. This is not to make you learn a new alphabet. It is because there just are not enough letters in our alphabet. Also, if you see a letter you do not know, then you know that the letter represents a parameter. Examples of letters that are used are (mu), (sigma), (rho), and p (yes this is our letter because there is not a good choice in the Greek alphabet).

    Statistics are usually denoted with our alphabet, and in some cases we try to use a letter that would be equivalent to the Greek letter. Examples of letter that are used are (x-bar), s, r, and (p-hat, since we already used p for the parameter).

    In addition, N is used to denote the size of the population and n is used to denote the size of the sample.

    Sampling Error: This is the difference between a parameter and a statistic. There will always be some error between the two since a statistic is an estimate of a parameter. Sampling error is attributed to chance error and sample bias.

    Chance Error: The error inherent in taking information from a sample instead of from the whole population. This comes from the fact that two different samples from the same population will likely give two different statistics.

    Sample Bias: The error from using a sample that does not represent the population. To avoid this, use some sort of random sample.

    Sampling Rate: The fraction of the total population that is in the sample. This can be denoted by n/N.

    This page titled 1.3: Clinical Studies is shared under a CC BY-SA 4.0 license and was authored, remixed, and/or curated by Maxie Inigo, Jennifer Jameson, Kathryn Kozak, Maya Lanzetta, & Kim Sonier via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.