When I do tumor immune treatment, some mice tumors (say 4 to 6) of the experimental group were smaller than the controls but not so obvious, the other two tumors were the same size or even bigger than the controls , how to analyze the result? Is this mean the treatment is noneffective ? Or should I change the does of the antigen and immunization time line and do the experiment again?
Your example shows how difficult it can be to try to draw conclusions when there's a good deal of experimental variation in the data. In any experimental group where individuals in the group receive the same treatment, the data you get from each individual will differ from one another by some degree. The possible reasons for this inter-individual variation are many, but the greater this variation is, the harder it is to be able to compare one treatment group to another group and conclude that they're different.
To get a sense for whether your treatment is having a significant effect, determine how big tumors are in each group (calculate a mean or median tumor size), how much variation there is in the data (SD or SEM), and use a statistical test to compare the groups and determine whether any differences in tumor size between treated and untreated groups are statistically significant.
If there is a statistically significant difference between tumor size in your groups, you should also consider whether there is actual, physiological significance to the differences you've observed (i.e., even if tumors are statistically smaller in the experimental group, does this affect survival/prognosis/propensity for metastasis?).
If there isn't a statistically significant difference, it could be that the treatment just doesn't work, but it could also be that the variation in the data is so large that it blurs any differences between your treated and untreated groups. Either way, you'd conclude the same thing: there is no significant difference between treated and untreated groups.
To make a long story short, the solution comes down to repetition: you just have to do the experiment enough times and with enough mice to be able to get a sense of the variation in your system and judge whether the differences you see between groups are meaningful. If after that, the difference in tumor size ends up not being significant, you can always try a different immunization protocol or perhaps other readouts that better demonstrate the efficacy of your treatment. Good luck!
Your example shows how difficult it can be to try to draw conclusions when there's a good deal of experimental variation in the data. In any experimental group where individuals in the group receive the same treatment, the data you get from each individual will differ from one another by some degree. The possible reasons for this inter-individual variation are many, but the greater this variation is, the harder it is to be able to compare one treatment group to another group and conclude that they're different.
To get a sense for whether your treatment is having a significant effect, determine how big tumors are in each group (calculate a mean or median tumor size), how much variation there is in the data (SD or SEM), and use a statistical test to compare the groups and determine whether any differences in tumor size between treated and untreated groups are statistically significant.
If there is a statistically significant difference between tumor size in your groups, you should also consider whether there is actual, physiological significance to the differences you've observed (i.e., even if tumors are statistically smaller in the experimental group, does this affect survival/prognosis/propensity for metastasis?).
If there isn't a statistically significant difference, it could be that the treatment just doesn't work, but it could also be that the variation in the data is so large that it blurs any differences between your treated and untreated groups. Either way, you'd conclude the same thing: there is no significant difference between treated and untreated groups.
To make a long story short, the solution comes down to repetition: you just have to do the experiment enough times and with enough mice to be able to get a sense of the variation in your system and judge whether the differences you see between groups are meaningful. If after that, the difference in tumor size ends up not being significant, you can always try a different immunization protocol or perhaps other readouts that better demonstrate the efficacy of your treatment. Good luck!
Increase number of mice in your experiment to have better sample size to reduce variation in the results.
Apply appropriate statistical test to compare the tumor between control and treated group.
And repeat experiment at least 3 times if u r having variations.
What is your theory? Why should immunotherapy work? What is the cause of the tumors?
Dear Yuan
This heterogenicity of data is usual especially when you use cancer cell injection for tumor induction. To solve this problem you can compare the tumor growth rate (for example) of each mouse and compare the measurement results of any mouse with its previous data. Then draw the growth curve and analysis the data between groups. Anyway other parameters like cytokine or VEGF when you record them by high tech techniques like immunohistochemistry shows the microenvironment of tumor and real time data.
Hope it helps.
Good Luck
i have rules for diagnosis any types of grades and void by using refinement query
in next research i will try to doing run time robotic capture the type of grades (tumors)
this my research for phd
i need to training for robotic biology in your university .can you help me
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