It is an oversimplification. Allelic fractions are a function of tumor purity, allele-specific copy number, cellular fractions (percentage of tumor cells with mutation), and mapping biases. You are probably interested in a differences of cellular fraction, so you need to adjust for any differences in purity, copy number (which most likely would be biologically relevant and also interesting) or biases. You can safely assume that the mapping biases are similar.
If you think there is no change in copy number, all you need to do is to get an estimate of the difference in tumor purity, for example by plotting allelic fractions of all somatic variants against each other. (Heterozygous, diploid, mono-clonal somatic mutations have an expected allelic fraction of half the tumor purity.)
You could then use a binomial or beta-binomial distribution to calculate the probability of observing that many adjusted number of reads in PDX given the coverage and the adjusted allelic fraction in tumor.
superFreq would be a tool pretty much designed for this problem (https://github.com/ChristofferFlensburg/superFreq, not yet published AFAIK). We wrote a similar tool for providing posterior probabilities for all possible genotypes (PureCN in Bioconductor), especially for clinical samples without matched normals.
If you have matched normals, you can use tools like PyClone or SciClone which in addition cluster your variants into clones.