Results from simulation of Dynamic Precision Medicine 2-Window Trial

<p>Results from the simulation adapted from Beckman et al. PNAS 2012. For details see the preprint "A Generalized Evolutionary Classifier for Evolutionary Guided Precision Medicine"&nbsp; https://www.medrxiv.org/content/10.1101/2020.09.24.20201111v2</p> <p>&nbsp;</p> <p>The simualtion code that generated these results can be found at:&nbsp;https://github.com/GU-DPM/EvolutionaryClassifier</p> <p>------------------------------------------------------------------</p> <p>Annotations of simulation outcome files.</p> <p>Simulation outcomes are stored in multiple files.</p> <p>param_ALLDRUG_xxx.txt: parameter configuration values.<br>stopt_ALLDRUG_xxx.txt: survival times for each parameter configuration and each strategy.<br>dosage_ALLDRUG_xxx.txt: dosage combination sequences for each parameter configuration and each strategy.<br>pop_ALLDRUG_xxx.txt: population composition dynamics for each parameter configuration and each strategy.</p> <p>xxx denotes the index of the job when I ran simulations in my cluster. &nbsp;You can simply concatenate the files over all indices.</p> <p><br>param_ALLDRUG_xxx.txt format:</p> <p>Each line denotes a parameter configuration with the following entries:</p> <p>(1)parameter configuration index (one entry).<br>(2)initial population compositions x0 (four entries, x0(S), x0(R1), x0(R2), x0(R12)).<br>(3)growth rate g0 (one entry).<br>(4)drug sensitivity matrix Sa (eight entries, Sa(S,drug1), Sa(S,drug2), Sa(R1,drug1), Sa(R1,drug2), ...).<br>(5)transition rate matrix T (sixteen entries, T(S-&gt;S), T(S-&gt;R1), T(S-&gt;R2), T(S-&gt;R12), T(R1-&gt;S), T(R1-&gt;R1), T(R1-&gt;R2), T(R1-&gt;R12), ....</p> <p><br>stopt_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the survival times (stopping times) of 10 strategies for each parameter configuration.</p> <p>strategy 0: strategy 0 in the PNAS paper.<br>strategy 1: strategy 2.2 in the PNAS paper.<br>strategy 2: strategy 2.2 in the PNAS paper for first two treatment decisions, followed by strategy 0 in the PNAS paper</p> <p>Each line has 11 entries: parameter configuration index and the survival times of the 10 strategies.</p> <p>The time horizon of the therapy is 1800 days. &nbsp;Each period is 45 days. &nbsp;If the patient is cured (the total tumor population = 0), then the survival time is reported as 1845 days. &nbsp;If the survival time is 1800 days, then the tumor population size is greater than 0 and smaller than the mortal level (1e13).</p> <p><br>dosage_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the dosage combination sequence of each strategy for each parameter configuration.</p> <p>(1)parameter configuration index.<br>(2)strategy index (0-2).<br>(3)(drug1 dosage,drug2 dosage) at t=0.<br>(4)(drug1 dosage,drug2 dosage) at t=45.<br>...<br>(42)(drug1 dosage,drug2 dosage) at t=1755.</p> <p>If t exceeds the survival time of the strategy, then the drug dosage is set to -1.</p> <p><br>pop_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the population composition dynamics of each strategy for each parameter configuration.</p> <p>(1)parameter configuration index.<br>(2)strategy index (0-9).<br>(3)(S,R1,R1,R12) population size at t=45.<br>(4)(S,R1,R1,R12) at t=90.<br>...<br>(42)(S,R1,R1,R12) at t=1800.</p> <p>If t exceeds the survival time of the strategy, then the population size is set to -1.</p> <p>Notice the dosage combination is reported at the beginning of each period, and the population composition is reported at the end of each period.</p> <p>When the best strategy over strategies 0-8 cures the patient and yields survival time 1800 days, I did not bother running strategy 9 (since it's time consuming). &nbsp;Thus the dosage combination and population composition should be ignored.</p> <p>&nbsp;</p>