# Mixed Model for Repeated Measurement

# A Note on Model Specification: Mixed Model for Repeated Measurement (MMRM)

### Mixed Model for Repeated Measurement (MMRM)

#### Full and Naive Structure

Let
- C<sub>s, t</sub> be the Change from Baseline in Patient <b>s</b> at Visit <b>t</b>;
- BL<sub>s</sub> be the Observation of Patient <b>s</b> at Baseline <b>t=0</b>;
- V<sub>t</sub> be the Visit for C <b>t</b>, for example, 21 or 42 days;
- T be the planned treatment group for Patient <b>s</b>, for example, Reference vs Test;
- s(t) be the variance-covariance matrix of Patient <b>s</b> nesting Visit <b>t</b>;
- and a error term present the residual.

The model can be written as:

<div align="center">
  <img src="https://latex.codecogs.com/svg.image?&space;C_{s,t}=\mu&plus;\beta_{1}\times&space;B_{s}&plus;\beta_{2}\times&space;V_{t}&plus;\beta_{3}\times&space;T_{s}&plus;\beta_{4}\times&space;V_{t}\times&space;T_{s}&plus;\gamma\times&space;s(t)&plus;\varepsilon_{s,t}">
</div>

#### Full and Dummized Structure

The interaction are preferred to be expanded:
<div align="center">
  <img src="https://latex.codecogs.com/svg.image?C_{s,t}=\mu&plus;\beta_{1}\times&space;B_{s}&plus;\beta_{2}\times&space;V_{t}&plus;\beta_{3}\times&space;T_{s}&plus;\beta_{41}\times(V_{t}=21)\times&space;T_{s}&plus;\beta_{42}\times(V_{t}=42)\times&space;T_{s}&plus;\gamma\times&space;s(t)&plus;\varepsilon_{s,t}">
</div>
<br>
At this step, if you notice, T<sub>s</sub> can be simplified by factoring out(additive term for &beta;<sub>2</sub>, &beta;<sub>3</sub>, &beta;<sub>41</sub> and &beta;<sub>42</sub>). However, V<sub>t</sub> has to be retained as part of matrix s(t).

#### Simplified and Dummized Structure

Factoring out T<sub>s</sub>
<div align="center">
  <img src="https://latex.codecogs.com/svg.image?C_{s,t}=\mu&plus;\beta_{1}\times&space;B_{s}&plus;\beta_{2}\times&space;V_{t}&plus;\beta'_{41}\times(V_{t}=21)\times&space;T_{s}&plus;\beta'_{42}\times(V_{t}=42)\times&space;T_{s}&plus;\gamma\times&space;s(t)&plus;\varepsilon_{s,t}">
</div>

#### Linear Combination
Let B&#773; be the population mean at Baseline.

Once model converage, &beta; are optimized. Estimations can be obtained through linear combination of terms.

The change from baseline at 21 days for Reference
<div align="center">
  <img src="https://latex.codecogs.com/svg.image?\widehat{C_{21}}=\mu&plus;\beta_{1}\times&space;\overline{B_{s}}">
</div>

The change from baseline at 21 days for Test
<div align="center">
  <img src="https://latex.codecogs.com/svg.image?\widehat{C_{21}}=\mu&plus;\beta_{1}\times&space;\overline{B_{s}}&plus;\beta'_{41}">
</div>

The change from baseline at 42 days for Reference
<div align="center">
  <img src="https://latex.codecogs.com/svg.image?\widehat{C_{42}}=\mu&plus;\beta_{1}\times&space;\overline{B_{s}}&plus;\beta_{2}">
</div>

The change from baseline at 42 days for Test
<div align="center">
  <img src="https://latex.codecogs.com/svg.image?\widehat{C_{42}}=\mu&plus;\beta_{1}\times&space;\overline{B_{s}}&plus;\beta_{2}&plus;\beta'_{42}">
</div>

Then, the difference between Reference and Test at 42 days
<div align="center">
  <img src="https://latex.codecogs.com/svg.image?\widehat{D_{42}}=\beta'_{42}">
</div>

### Baseline-contrained Longitudial Data Analysis (cLDA)
To be updated.

### Appendix
<details>
  <summary>CodeCogs</summary>
  <p>C_{s,t} = \mu + \beta_{1}\times B_{s} + \beta_{2}\times V_{t} + \beta_{3}\times T_{s} + \beta_{4}\times V_{t} \times T_{s} + \gamma\times s(t) + \varepsilon_{s,t}</p>
  <p>C_{s,t} = \mu + \beta_{1}\times B_{s} + \beta_{2}\times V_{t} + \beta_{3}\times T_{s} + \beta_{41}\times (V_{t}=21)\times T_{s} + \beta_{42}\times (V_{t}=42)\times T_{s} + \gamma\times s(t) + \varepsilon_{s,t}</p>
  <p>C_{s,t} = \mu + \beta_{1}\times B_{s} + \beta_{2}\times V_{t} + \beta'_{41}\times (V_{t}=21)\times T_{s} + \beta'_{42}\times (V_{t}=42)\times T_{s} + \gamma\times s(t) + \varepsilon_{s,t}</p>
  <p>\widehat{C_{21}} = \mu + \beta_{1}\times \overline{B_{s}} + \beta_{2}\times V_{t} + \beta'_{41}\times (V_{t}=21)\times T_{s} + \beta'_{42}\times (V_{t}=42)\times T_{s}</p>
</details>