= Solution
Changing the <reference level in a regression factor> from A to B does not change the statistical model, fitted probabilities, likelihood, or deviance. If the first parametrization is
$$
(\beta_0,\beta_B,\beta_C,\beta_M),
$$
then the second is
$$
(\beta'_0,\beta'_A,\beta'_C,\beta'_M)
=(\beta_0+\beta_B,-\beta_B,\beta_C-\beta_B,\beta_M).
$$
Numerically,
$$
\begin{aligned}
1.6855+0.8096&=2.4951,\\
-0.8096&=-0.8096,\\
-0.5423-0.8096&\approx-1.3520,\\
-0.9048&=-0.9048.
\end{aligned}
$$
The A-versus-B test is the same test as the former B-versus-A test, with its sign reversed, so its p-value remains $0.0624$. The malignancy coefficient is unchanged in this additive model, so its p-value remains $0.0175$. The intercept now represents site B rather than site A, so its test changes.
The `siteC` coefficient also changes meaning: in `fit1` it tests C versus A, whereas in `fit2` it tests C versus B. Its standard error and p-value therefore need not agree. The displayed tables show that C versus A is not significant ($p=0.2610$), while C versus B is significant ($p=0.0160$). There is no contradiction: these are different pairwise hypotheses.
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