BME100 f2014:Group32 L1

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BME 100 Fall 2014 Home
Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3
Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6
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Independent and Dependent Variables

The independent variable in the "Designing an Experiment" lab is the dosage levels of an inflammation inducing agent called lipopolysaccharide because the dosage levels are able to be controlled by the experimenters in the experiment. As for the dependent variable, it would be the amount of the inflammatory protein, Inflammotin, in the blood of the elderly subjects in the experiment because this is the variable that we are measuring and quantifying.

Experimental Design


Based off of prior experimentation, we know the results of a 10mg pill of lipopolysaccharide on subjects. We decided to choose six groups based on dosage of lipopolysaccharide to find which dosage will effectively produce inflammatory protein at the lowest possible amount to save cost. These groups are in even increments from 10mg down to 0mg. The groups are thus listed as 00mg, 2mg, 4mg, 6mg, 8mg and 10mg.

The 00mg group will be the control group that no dosage is being administered to.

Age range is set to ages between 65-70 providing a range of elderly subjects suited for the test.

Number of subjects per group
There will be 10 subjects per group to ensure accuracy when testing each increment of lipopolysaccharide . Ten subjects within our age parameters will be randomly selected to provide a wide array of results.

Subject Selection

To choose our test subjects we have chosen to randomly select 10 healthy subjects with ages between 65 and 70 for each test group, resulting in a total of 60 controlled test subjects. To acquire our test subjects we have found it to be least biased to obtain a list of 500 qualified volunteers/candidates, exceeding the amount of total subjects needed for the experiment and assign each one a number. After each volunteer has been assigned a number we will assign subjects to each test group based on the results of a proven random number generator after each subject is given a number.

The subjects are under strict criteria. They must have no other preexisting or heavy history of diseases related to inflammatory conditions. The subjects will be picked from a pool of healthy candidates randomly.

Sources of Error and Bias

Some possible sources of error:

One possible source of error is choosing a patient with an already existing condition or a tendency towards inflammation. External variables that affect inflammation or the reaction with the drug due to the patient's genetic makeup could affect the experiment as well. In order to control this a thorough history of genetics and disease of the patient must be checked. Files will be compiled and patients will be checked to ensure that the inflammation produced is a product of the drug given rather than an external factor or different reaction than intended.

Another possible source of error is choosing patients that do not follow the recommended plan of action, plan of experimentation and either increases or decrease the inflammation. The subject could also not follow the recommended dosage and take too little or too much at the wrong times and thus affect the experiment negatively by producing non-correlative data. This will be reduced by closely monitoring the patient's lifestyle and daily routine, and making sure that they take the recommended dosage at the recommended times without deviating from the experimental standards.

The placebo effect could also play into this type of experiment. The patient could possibly cause higher levels of inflammation through this. In order to minimize this one needs to double blind the experiment so that the experimenters and the experimented do not provide relevant information to cause the placebo effect. There is a also a control group to see how much the placebo effect possibly affects the experiment.

One needs to minimize machine and human error in calculations. The accuracy of the ELISA measuring technique will have some error regardless of what is done. A more accurate protein quantification technique will be needed to reduce error.

T-statistics and ANOVAs perhaps do not usually represent the true distribution of the population but rather a modified distribution of it, thus giving data that might not truly be correct. In order to gain a better census of the population and the effectiveness of the drug on the general population the sample size of the experiment needs to be increased. Also randomization will be employed in order to get the best results and variation in the population the experiment is examining. To get the proper amount of the population a power analysis will need to be done and a pearson's R correlation coefficient will need to be calculated.

More error could be caused by the times of the day that the dosage is taken. Lifestyle, exercise, diet and sleeping habits could all play into effect in increasing bodily metabolism and excretion of certain drugs. The activity of the cellular components of the body at different times during the day for different people could easily cause the drug to act differently in different intervals of time. In order to minimize this the population must be controlled for age, gender, disease, disease history, fitness level, diet and several other possibly unintended external factors.

Sources of Bias are also present:

A main source of bias is the testers themselves, in that testers want a new drug to do well. To help combat this testers are blinded to what is given to patients so that they could not skew the outcome. This is usually coupled with blinding the patients so that they also do not know if they are receiving the medication or not in trying to work around the placebo effect in what is known as a double blind test.

Another common bias found in experiments is selection bias, where the selection of test subjects is done not randomly but at the testers discretion. This of course is mitigated in this experiment by using a random number generator to select patients not a testers bias.

During the administration of the treatment there are biases associated with how the patient views how they receive the treatment. In that patients need to be given the medicine separately with a doctor that treats them only professionally and patients do not interact with one another about their treatment. Interaction that deviate from this begin to give the patient a placebo effect depending on the interaction such as having a nice doctor and wanting to please them with good results, or talking to another patient and finding they are doing well therefore the patient must be doing well regardless of how they actually feel.

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