title: mixed model questions without group
body: <p>So I'm new to linear mixed models and know basic R but that's it. I understand the idea of mixed models conceptually but am having trouble with the syntax and interpretation. </p>
  
  <p>I have one outcome measure (blood level) and am trying to see if they change over time (which I have in days, but can also make into a dichotomous baseline/followup variable). However, they were all tested at the end in batches on the testing machine which leads to some inter-batch variation. However, batches are unique to each participant-time combination (e.g. participant 1 may have had things tested on batch 2 at timepoint 1 but batch 3 at timepoint 2, while participant 2 may have had levels tested on batch 4 at both timepoints).  </p>
  
  <p>Thus, I'd like to model this using subject id as a random effect and the others as fixed effects.  </p>
  
  <p>The syntax might be:</p>
  
  <pre><code>lme.model &lt;- lmer(bloodlevel ~ batch + time + (1 | ID), data = dataset, REML=T)
  </code></pre>
  
  <p>with time being the time after initial blood draw (at baseline it'd be 0 and followup would be like 90 days for example).</p>
  
  <p>When I do: <code>anova(lme.model)</code> to get p values, the output is as follows:</p>
  
  <pre><code>Type III Analysis of Variance Table with Satterthwaite's method
                Sum Sq Mean Sq NumDF  DenDF F value Pr(&gt;F)
  batch         5.8020  5.8020     1 54.080  0.6147 0.4365
  Time          4.5306  4.5306     1 52.394  0.4800 0.4915 
  </code></pre>
  
  <p>I'm a little unclear how to interpret this. Does that mean that the p value for the difference in blood levels of my sample between the two timepoints was 0.4915? Or what is the correct way to model this?</p>
  
  <p>I'm still learning this so please forgive my ignorance. </p>
  
  <p>Edit: Please could somebody help me with this?</p>