<p>It seems you are trying to ascertain a relationship between treatment-type and tumor mutation burden (mutations per megabase). chi- square test for association could be helpful for a particular cancer-type. You can compute chi-square for each cancer-type.</p>

CC BY-SA 4.0
2024-04-29T14:29:13.283Z
2024-04-29T14:29:13.283Z
Subhash C. Davar
<p>I am struggling a bit to model the following problem. Basically, I would like to model tumor mutation burden (mutations per megabase, a continuous) as a function of treatment (categorical). The following is an example of how data looks like</p>

<pre><code>patient-id therapy-1 therapy-2 therapy-3 cancer-type TMB
PID-1      1         NA        1         CT-1        1.3
PID-2      0         0         1         CT-1        0.14 
PID-3      NA        0         1         CT-2        5
PID-4      1.        1         NA        CT-3        2.3
PID-5      1         1         NA        CT-3        10
...
...
</code></pre>

<p>1 - treated; 0 - not treated; NA - No information</p>

<p>I was trying multiple linear regression, but in most cases one patient got multiple therapies. This is where I am confused on how to interpret the results of linear regression. So far, I haven't included the <code>cancer-type</code> information into the model. However, IMO, including cancer-type into model will yield specific treatment effects in specific cancer type? Or is it over-expectation of what we can achieve from linear model? </p>

<p>Also, is there a better way to address the problem to get reasonable associations between treatment and TMB in this case?</p>

<p>Thank you for the help. Stay safe!</p>

CC BY-SA 4.0
2020-09-29T08:26:54.637Z
Could you post your model is it something like TMB ~ cancer-type*patient-id ?
llrs
<r><python><mutations><linear-regression>
Model the effect of treatment on mutation burden
# Model the effect of treatment on mutation burden

(Asked by: username_0 on 2020-04-27)

I am struggling a bit to model the following problem. Basically, I would like to model tumor mutation burden (mutations per megabase, a continuous) as a function of treatment (categorical). The following is an example of how data looks like
patient-id therapy-1 therapy-2 therapy-3 cancer-type TMB
PID-1      1         NA        1         CT-1        1.3
PID-2      0         0         1         CT-1        0.14 
PID-3      NA        0         1         CT-2        5
PID-4      1.        1         NA        CT-3        2.3
PID-5      1         1         NA        CT-3        10
...
...

1 - treated; 0 - not treated; NA - No information
I was trying multiple linear regression, but in most cases one patient got multiple therapies. This is where I am confused on how to interpret the results of linear regression. So far, I haven't included the cancer-type information into the model. However, IMO, including cancer-type into model will yield specific treatment effects in specific cancer type? Or is it over-expectation of what we can achieve from linear model? 
Also, is there a better way to address the problem to get reasonable associations between treatment and TMB in this case?
Thank you for the help. Stay safe!

**Tags:** r, python, mutations, linear-regression
> **Comments:**
> **username_1**: Could you post your model is it something like TMB ~ cancer-type*patient-id ?  

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## 1 answers

### Answer by username_2 on 2024-04-29. Score: -1

It seems you are trying to ascertain a relationship between treatment-type and tumor mutation burden (mutations per megabase). chi- square test for association could be helpful for a particular cancer-type. You can compute chi-square for each cancer-type.


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