hiddenGem

201 Final

Jun 20th, 2021
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  1. % Again Part of my 201 Class
  2. % removed identifying features
  3. % Similar to Final Exam Applications paste
  4.  
  5. %% Question 1b
  6. clc, clear, clf
  7.  
  8. % Virus Values
  9. VmaxVirus = 0.75;   % Vmax for Virus
  10. KmVirus = 5.7;      % Km for Virus
  11. C = logspace(-2, 4);        % Creating equally spaced log vector from 10^-2 to 10^4
  12. VobsVirus = (VmaxVirus.*C)./(C+KmVirus);    % Vobs function for Virus in terms of Vmax, Km, and variable
  13.  
  14. % Human Values
  15. VmaxHuman = 0.81;       % Vmax for Human
  16. KmHuman = 21;           % Km for Human
  17. VobsHuman = (VmaxHuman.*C)./(C+KmHuman);        % Creating Vobs for Human in terms of Vmax, KM, and variable
  18.  
  19. % Graphing the functions
  20. hold on         % Hold on so I can plot two functions on one figure
  21. semilogx(VobsVirus)         % Plotting Virus function on log x-axis
  22. semilogx(VobsHuman)         % Plotting Human function on log x-axis
  23. title('V Observed vs. Concentration of Remdesivir-TP for Human and Virus')      % Title for figure
  24. legend('Virus', 'Human', 'Location', 'best')    % Creating legend to differentiate between functions in the best location
  25. xlabel('Concentration of Remdesivir-TP(uM)')    % Horizontal label
  26. ylabel('V Observed')            % Vertical label
  27. hold off        % Removes hold on graph
  28. %% Question 1c
  29.  
  30. V = [0.01 0.1 1 10]        % [uM] Creating a concentration vector
  31. VobsV = (VmaxVirus.*V)./(V+KmVirus)        % Creating Vobs function for Virus with set concentrations
  32. VobsH = (VmaxHuman.*V)./(V+KmHuman)        % Creating Vobs function for Human with set concentrations
  33. TW = VobsV - VobsH         % Therapeutic window
  34. %% Question 2a
  35. clc, clear, clf
  36.  
  37. s1 = table([84;16],[80;20],'VariableNames',{'Experimental','Placebo'},'RowNames',{'Lived', 'Died'})         % Creating table for scenario 1
  38. s2 = table([840;160],[800;200],'VariableNames',{'Experimental','Placebo'},'RowNames',{'Lived', 'Died'})     % Creating table for scenario 2
  39. [h1, p1, stats1] = fishertest(s1)       % Performing fisher's exact test on scenario 1 and getting values from results
  40. [h2, p2, stats2] = fishertest(s2)       % Performing fisher's exact test on scenario 2 and getting values from results
  41. %% Question 2b
  42. clc, clear, clf
  43.  
  44. CohortSize =[20 200 2000 20000];       % Creating cohort sizes vector
  45.  
  46. for i = 1:length(CohortSize)      % Beginning of the for loop that runs for length of the cohort vector
  47.     C = CohortSize(i);          % Reallocating the cohort size into a vector with a smaller name
  48.     b = C/2 * 0.80;      % Taking half of the cohort size and then saying 80% of this new group will live with placebo arm
  49.     d = C/2 * 0.20;     % Taking half of the cohort size and then saying 20% of this new group will die with placebo arm
  50.     c = d*.80;        % The number of patient who died with the experimental arm happens to be 80% in value of patients who died with the placebo arm
  51.     a = C/2 - c;       % Taking half of cohort size and subtracting the amount of the patients who died with experimental arm leaves the survivors with experimental arm
  52.     s = [a b
  53.          c d];          % Creating vector for the contingency table
  54.     s = round(s);       % round all the numbers within the vector
  55.     [~, P(i), ~] = fishertest(s);      % Perform the fisher's exact test and saving the p-values
  56.    
  57. end         % end for loop
  58.  
  59.  
  60. semilogy(CohortSize, P)         % Plot p-values vs. cohort size with log y-axis
  61. xlabel('Cohort Size')           % horizontal label
  62. ylabel('p-values')      % vertical label
  63. title('p-values vs. Cohort Size')       % Title for figure
  64.  
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