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Copy pathCWP_LogRank.m
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CWP_LogRank.m
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function CWP_LogRank
tic;
% prepare
fp = 'Z:\elw\MATLAB\cw_analy\meta_data\';
%fp = 'C:\Documents and Settings\williae1\cw_meta_data\';
fig_loc = 'Z:\elw\MATLAB\cw_analy\figures\latest\';
fn = {'MUTTER_MASTER_ChestWall_Cox_DiVj_DVHs_fx-1_a2bInf.mat'};
%fn = {'RIMNER_MASTER_ChestWall_Cox_DiVj_DVHs_fx-1_a2bInf.mat'};
CGobj = cell(length(fn),1);
screen_size=get(0,'ScreenSize');
%scrsz = get(0,'ScreenSize');
%set(0,'DefaultFigurePosition',[scrsz(1)+scrsz(1)/4 scrsz(4)/2 scrsz(3)/2 scrsz(4)/2]);
% load data
for m = 1:length(fn)
load(strcat(fp,fn{m}),'CGobj_current');
CGobj{m} = CGobj_current;
end
for m = 1:length(fn)
%% Log-Rank p-value map
% Vx
f = cellfun(@(x) strcmpi('VDx',x),CGobj{m}.mLogRank(:,1));
pvx = CGobj{m}.mLogRank{f,2};% vmax
f1 = pvx(:,:,6)== 0;
f2 = pvx(:,:,5)<=0.05;
pvx = pvx(:,:,5);
pvx(~f1)=NaN;% Remove p-vals for which correlation not calc (unnecessary bc pval=1 otherwise)
pvx(~f2)=NaN;
fig_ctr=1;
figure(fig_ctr);clf reset;
fig_ctr=fig_ctr+1;
% hack for better printing
%gl = opengl('data')
%opengl('software')
h=imagesc(pvx');
set(gca, 'ActivePositionProperty','OuterPosition');
set(h,'alphadata',~isnan(pvx'));
set(gca,'YDir','normal');
xlim([0,60]);
ylim([0,800]);
colorbar;
mycmap = get(gcf,'Colormap');
set(gcf,'Colormap',flipud(mycmap));
set(gca,'xminortick','on','yminortick','on');
xlabel('Dose [Gy]','fontsize',14);
ylabel('Volume [cc]','fontsize',14);
title('Significant Log-Rank p-values for Vx','fontsize',14);
% VDx
dose=39;
%
% Vx=zeros(length(CGobj{m}.mBinsDose),CGobj{m}.mNumInGrp,1);
% for d=1:length(CGobj{m}.mBinsDose),
% for k=1:CGobj{m}.mNumInGrp
% Vx(d,k) = CGobj{m}.mGrp(k).fVolAtDose( d );
% end
% end
%
[~,fdose] = min(abs(CGobj{m}.mBinsDose - dose));
% change to VDx after re-run, doesn't matter which one, all have same
% info!
f = cellfun(@(x) strcmpi('VDx',x),CGobj{m}.mLogRank(:,1));
pvx = CGobj{m}.mLogRank{f,2};% vmax
pvx = squeeze(pvx(fdose,:,:)); % look at volumes V_{fdose}, then pvx = [V_{fdose} bins]X[n1,c1,n2,c2,p,flg]
f = pvx(:,6) == 0;
% find minimum p-value and corresponding
% volume split value in 2-3rd quartile range
v39_pvals = pvx(f,5);
v39_vols = CGobj{m}.mBinsVol(f);
[sorted_v39_pvals,idx_sorted_pvals] = sort(pvx(f,5));
sorted_v39_vols = v39_vols(idx_sorted_pvals);
mid_quart_pvals =...
sorted_v39_pvals(find([sorted_v39_vols>quantile(sorted_v39_vols,0.25)].*[sorted_v39_vols<quantile(sorted_v39_vols,0.75)]));
[min_mid_qrt_pval,~] = min(mid_quart_pvals);
mid_qrt_best_split = find([v39_pvals==min_mid_qrt_pval]);
if length(mid_qrt_best_split)>1
mid_qrt_best_split = mid_qrt_best_split(1);
end
disp(['Best split v39 volume for 2-3rd quantiles: ',num2str(v39_vols(mid_qrt_best_split)),10,...
' with pval: ',num2str(min_mid_qrt_pval)]);
%med_v39 = median(vx);
figure(fig_ctr); clf reset;
fig_ctr=fig_ctr+1;
%[ax,~,~]=plotyy(CGobj{m}.mBinsVol(f),pvx(f,5),CGobj{m}.mBinsVol(f),pvx(f,7),@semilogy)
semilogy(CGobj{m}.mBinsVol(f),pvx(f,5),'-','LineWidth',2); hold on;
semilogy(CGobj{m}.mBinsVol(f),0.05,'r-','LineWidth',1);
hold off; % grid on;
xlabel('Volume (cc)','fontsize',14);
ylabel('p-value','fontsize',14);
title(strcat('Log-Rank p-values for V_{',num2str(dose),'} values'),'fontsize',12);
[min_pvx,idx_pvx] = min(pvx(f,5));
disp(['Minimum Log-Rank p-value is ',...
num2str(min_pvx),...
', at V_{',num2str(dose),'} = ',...
num2str(CGobj{m}.mBinsVol(idx_pvx))]);
f = cellfun(@(x) strcmpi('VDx',x),CGobj{m}.mLogRank(:,1));
pvx = CGobj{m}.mLogRank{f,2};% vmax
min_pvx = nan(length(CGobj{m}.mBinsDose),1);
idx_pvx = -ones(length(CGobj{m}.mBinsDose),1);
med_vols = nan(length(CGobj{m}.mBinsDose),1);
mn_vols = nan(length(CGobj{m}.mBinsDose),1);
for i=1:length(CGobj{m}.mBinsDose)
[~,fdose] = min(abs(CGobj{m}.mBinsDose - CGobj{m}.mBinsDose(i)));
cur_pvx = squeeze(pvx(fdose,:,:)); % look at volumes V_{fdose}, then pvx = [V_{fdose} bins]X[n1,c1,n2,c2,p,flg]
f = cur_pvx(:,6) == 0;
if sum(f)<1
continue;
end
Vx=zeros(CGobj{m}.mNumInGrp,1);
for k=1:CGobj{m}.mNumInGrp
Vx(k) = CGobj{m}.mGrp(k).fVolAtDose( CGobj{m}.mBinsDose(i));
end
%med_vols(i) = median(CGobj{m}.mBinsVol(f));
%mn_vols(i) = mean(CGobj{m}.mBinsVol(f));
med_vols(i) = median(Vx);
mn_vols(i) = mean(Vx);
cur_pvx =cur_pvx(f,5);
[min_pvx(i),idx_pvx(i)] =min(cur_pvx);
end
figure(fig_ctr); clf reset;
fig_ctr=fig_ctr+1;
semilogy(CGobj{m}.mBinsDose,min_pvx,'-','LineWidth',2); hold on;
semilogy(CGobj{m}.mBinsDose,0.05,'r-','LineWidth',1);
set(gca,'xminortick','on','yminortick','on');
grid on;
xlabel('Dose [Gy]','fontsize',14);
ylabel('Minimum Log-Rank p-value','fontsize',14);
title('Log-Rank p-value vs V_{D}','fontsize',12);
hold off; % grid on;
f4=figure(fig_ctr); clf reset;
fig_ctr=fig_ctr+1;
%set(f4,'Position',[0 0 screen_size(3)/2 screen_size(4)/2]);
h=idx_pvx<1;
idx_pvx(h)=[];
hold on;
j(1)=plot(CGobj{m}.mBinsDose(~h),CGobj{m}.mBinsVol(idx_pvx),'-','LineWidth',2);
j(2)=plot(CGobj{m}.mBinsDose(~h),med_vols(~h),'r--','LineWidth',1);
j(3)=plot(CGobj{m}.mBinsDose(~h),mn_vols(~h),'g--','LineWidth',1);
hold off;
set(gca,'xminortick','on','yminortick','on');
grid on;
legend(j,'Best V_{D}','Median V_{D}','Mean V_{D}');
xlabel('Dose [Gy]','fontsize',14);
ylabel('Volume of best Log-Rank split [cc]','fontsize',14);
title('Log-Rank Volume split vs V_{D}','fontsize',12);
hold off; % grid on;
%% KM curve displays
% compose complication KM curves
sa=classKaplanMeierCurve(); % initialize a survivalanalysis obj
sa2=classKaplanMeierCurve(); % initialize a survivalanalysis obj
CG = CGobj{m};
% survival/complication time
f2 = ~cellfun('isempty',{CG.mGrp.mDateComp}); % patients with no complication date
f3 = ~cellfun('isempty',{CG.mGrp.mDateLastFollowup}); % patients with no last follow up date
compdate = inf(CG.mNumInGrp,1);
lastfollowup = inf(CG.mNumInGrp,1);
compdate(f2) = ([CG.mGrp(f2).mDateComp] - [CG.mGrp(f2).mDateBaseline])' / 30;
lastfollowup(f3) = ([CG.mGrp(f3).mDateLastFollowup] - [CG.mGrp(f3).mDateBaseline])' / 30;
compdate = min( lastfollowup, compdate );
flgcensor = [CG.mGrp.mFlgCensor]';
% V39
%median split
dose=39;
[~,fdose] = min(abs(CGobj{m}.mBinsDose - dose));
f = cellfun(@(x) strcmpi('VDx',x),CGobj{m}.mLogRank(:,1));
pvx = CGobj{m}.mLogRank{f,2};
pvx = squeeze(pvx(fdose,:,:));
f = pvx(:,6) < 2;
vd=zeros(CG.mNumInGrp,1); % volume v at dose d
numstart=CG.mLogRankMinSize;
for d=fdose:fdose
% volume under d
vd(:)=0;
for k=1:CG.mNumInGrp
vd(k) = CG.mGrp(k).fVolAtDose( CG.mBinsDose(d) );
end
g=find(vd>-1); % non-zeros volume cases
% median volume
vol = median(vd);
%vol = 97;
[~,fvol] = min(abs(CGobj{m}.mBinsVol - vol));
disp(['Median value of V_{',num2str(dose),'} = ',num2str(vol)]);
disp(['Log-Rank p-value of V_{',num2str(dose),...
'} split at ',num2str(vol),...
' = ',num2str(pvx(fvol,5))]);
str_pval1 = {strcat('Log-Rank p-value = ',num2str(pvx(fvol,5),'%10.2e\n'))};
% (di,vj)
numend=length(g)-numstart;
for v=fvol:fvol
% check smaple size
flg_volbelow1=vd(g)<=CG.mBinsVol(v); f=length(find(flg_volbelow1)); % group DVHs by (d,v)
if f<numstart || f>numend % one group has too less patients, or the volume at the dose is too small, skip it
continue;
end
% assign properties of object sa
survivedate={compdate(g(flg_volbelow1)); compdate(g(~flg_volbelow1))}; % survive time of each group
fcensor={flgcensor(g(flg_volbelow1)); flgcensor(g(~flg_volbelow1))}; % censor flag for each group
sa.mSurvivalTime=survivedate;
sa.mFlgCensor=fcensor;
% compute survival curves and compare them
sa=sa.fCalculateSurvivalCurve();
sa=sa.fCombineSurvivalTime();
sa=sa.fCompareSurvivalByLogrank();
end
end
% plot KM curves
figure(fig_ctr); clf reset; hold on; % grid on;
fig_ctr=fig_ctr+1;
h_km(1)=stairs(sa.mSurvivalTimeSorted{1},1-sa.mSurvivalCurve{1});
plot(sa.mSurvivalTimeSorted{1}(sa.mCensorStatistics{1}(:,1)),...
1-sa.mSurvivalCurve{1}(sa.mCensorStatistics{1}(:,1)),'+');
h_km(2)=stairs(sa.mSurvivalTimeSorted{2},1-sa.mSurvivalCurve{2},'r');
plot(sa.mSurvivalTimeSorted{2}(sa.mCensorStatistics{2}(:,1)),...
1-sa.mSurvivalCurve{2}(sa.mCensorStatistics{2}(:,1)),'r+');
% xticks = get(gca,'Xlim'); set(gca,'XTick',0:6:max(xticks));
text(38,0.25,str_pval1,'FontSize',12);
lgnd=legend(h_km,...
strcat('V$_{39}\leq',num2str(vol,4),'$'),...
strcat('V$_{39}\geq',num2str(vol,4),'$'),'Location','Best');
set(lgnd,'FontSize',14);
h=legend;
set(h,'interpreter','latex');
set(gca,'xminortick','on','yminortick','on');
xlabel(['Months'],'fontsize',14);
ylabel(['Probability of CW Pain'],'fontsize',14);
title('V_{39}, Median split','fontsize',14);
dose=33;
split=-1;
fPlotKaplanMeierCurve_VDx(CGobj,dose,split);
fig_ctr=fig_ctr+1;
dose=39;
split=-1;
fPlotKaplanMeierCurve_VDx(CGobj,dose,split);
fig_ctr=fig_ctr+1;
dose=39;
split=97;%best
fPlotKaplanMeierCurve_VDx(CGobj,dose,split);
fig_ctr=fig_ctr+1;
% % V33
% %median split
% dose=33;
% [~,fdose] = min(abs(CGobj{m}.mBinsDose - dose));
% f = cellfun(@(x) strcmpi('VDx',x),CGobj{m}.mLogRank(:,1));
% pvx = CGobj{m}.mLogRank{f,2};
% pvx = squeeze(pvx(fdose,:,:));
% f = pvx(:,6) < 2;
%
%
% vd=zeros(CG.mNumInGrp,1); % volume v at dose d
% numstart=CG.mLogRankMinSize;
% for d=fdose:fdose
% % volume under d
% vd(:)=0;
% for k=1:CG.mNumInGrp
% vd(k) = CG.mGrp(k).fVolAtDose( CG.mBinsDose(d) );
% end
% g=find(vd>-1); % non-zeros volume cases
%
% % median volume
% vol = median(vd);
% [~,fvol] = min(abs(CGobj{m}.mBinsVol - vol));
% disp(['Median value of V_{',num2str(dose),'} = ',num2str(vol)]);
% disp(['Log-Rank p-value of V_{',num2str(dose),...
% '} split at ',num2str(vol),...
% ' = ',num2str(pvx(fvol,5))]);
% str_pval2 = {strcat('Log-Rank p-value = ',num2str(pvx(fvol,5),'%10.2e\n'))};
%
% % (di,vj)
% numend=length(g)-numstart;
% for v=fvol:fvol
% % check smaple size
% flg_volbelow1=vd(g)<=CG.mBinsVol(v); f=length(find(flg_volbelow1)); % group DVHs by (d,v)
% if f<numstart || f>numend % one group has too less patients, or the volume at the dose is too small, skip it
% continue;
% end
%
% % assign properties of object sa
% survivedate={compdate(g(flg_volbelow1)); compdate(g(~flg_volbelow1))}; % survive time of each group
% fcensor={flgcensor(g(flg_volbelow1)); flgcensor(g(~flg_volbelow1))}; % censor flag for each group
% sa.mSurvivalTime=survivedate;
% sa.mFlgCensor=fcensor;
% % compute survival curves and compare them
% sa=sa.fCalculateSurvivalCurve();
% sa=sa.fCombineSurvivalTime();
% sa=sa.fCompareSurvivalByLogrank();
% end
% end
%
% % plot KM curves
% figure(6); clf reset; hold on; % grid on;
% h_km(1)=stairs(sa.mSurvivalTimeSorted{1},1-sa.mSurvivalCurve{1});
% plot(sa.mSurvivalTimeSorted{1}(sa.mCensorStatistics{1}(:,1)),...
% 1-sa.mSurvivalCurve{1}(sa.mCensorStatistics{1}(:,1)),'+');
% h_km(2)=stairs(sa.mSurvivalTimeSorted{2},1-sa.mSurvivalCurve{2},'r');
% plot(sa.mSurvivalTimeSorted{2}(sa.mCensorStatistics{2}(:,1)),...
% 1-sa.mSurvivalCurve{2}(sa.mCensorStatistics{2}(:,1)),'r+');
% % xticks = get(gca,'Xlim'); set(gca,'XTick',0:6:max(xticks));
% text(38,0.25,str_pval2,'FontSize',12);
% lgnd=legend(h_km,...
% strcat('V$_{33}\leq',num2str(vol,4),'$'),...
% strcat('V$_{33}\geq',num2str(vol,4),'$'),'Location','Best');
% set(lgnd,'FontSize',14);
% h=legend;
% set(h,'interpreter','latex');
%
% set(gca,'xminortick','on','yminortick','on');
% xlabel(['Months'],'fontsize',14);
% ylabel(['Probability of CW Pain'],'fontsize',14);
% title('V_{33}, Median split','fontsize',14);
%
% V30
%median split
% dose=30;
dose=30;
[~,fdose] = min(abs(CGobj{m}.mBinsDose - dose));
f = cellfun(@(x) strcmpi('VDx',x),CGobj{m}.mLogRank(:,1));
cur_pvx = CGobj{m}.mLogRank{f,2};
cur_pvx = squeeze(cur_pvx(fdose,:,:));
f = cur_pvx(:,6) < 2;
f7=figure(fig_ctr); clf reset;
fig_ctr=fig_ctr+1;
set(f7,'Position',[0 0 screen_size(3)/2 screen_size(4)/2]);
x_axis = CGobj{m}.mBinsVol(f);
x_axis = x_axis(1:150);
v30_hrs = cur_pvx(f,7);
v30_hrs = v30_hrs(1:150);
v30_pvals = cur_pvx(f,5);
v30_pvals = v30_pvals(1:150);
%semilogy(CGobj{m}.mBinsVol(f),cur_pvx(f,5),'-','LineWidth',2); hold on;
[ax,h1,h2]=plotyy(x_axis,v30_hrs,x_axis,v30_pvals,@semilogy);hold on;
set(h1,'LineWidth',2);
set(h2,'LineWidth',2);
set(ax(1),'YLim',[2.5 7]);
set(ax(1),'YTick',[2.5:1:7]);
set(get(ax(1),'Ylabel'),'String','Hazard Ratio','FontSize',15);
set(get(ax(2),'Ylabel'),'String','p-value','FontSize',15);
semilogy(x_axis,0.05,'r-','LineWidth',1);
%xlim([0 max(x_axis)]);
set(ax(1),'XLim',[0 max(x_axis)]);
set(ax(2),'XLim',[0 max(x_axis)]);
set(ax,'FontSize',14);
%line(vol,ylim);
%here
Lmed = line([med_vols(31) med_vols(31)],ylim,'Color','k','LineWidth',2);
%L30 = line([30 30],ylim,'Color','g','LineWidth',2);
%L50 = line([50 50],ylim,'Color','c','LineWidth',2);
%L70 = line([70 70],ylim,'Color','m','LineWidth',2);
hold off; % grid on;
xlabel('Volume (cc)','fontsize',15);
%ylabel('p-value','fontsize',14);
title(strcat('Log-Rank p-values for V_{',num2str(dose),'} values'),'fontsize',14);
% lgnd=legend([Lmed L30 L50 L70],['Median = ',num2str(med_vols(30))],'30cc','50cc','70cc','Location','SouthEast');
lgnd=legend(Lmed,['Median = ',num2str(med_vols(31),4),' cc']);
set(lgnd,'FontSize',14);
% vd=zeros(CG.mNumInGrp,1); % volume v at dose d
% numstart=CG.mLogRankMinSize;
% for d=fdose:fdose
% % volume under d
% vd(:)=0;
% for k=1:CG.mNumInGrp
% vd(k) = CG.mGrp(k).fVolAtDose( CG.mBinsDose(d) );
% end
% g=find(vd>-1); % non-zeros volume cases
%
% % median volume
% vol = median(vd);
% %vol = 50;
% [~,fvol] = min(abs(CGobj{m}.mBinsVol - vol));
% disp(['Median value of V_{',num2str(dose),'} = ',num2str(vol)]);
% disp(['Log-Rank p-value of V_{',num2str(dose),...
% '} split at ',num2str(vol),...
% ' = ',num2str(cur_pvx(fvol,5))]);
% str_pval2 = {strcat('Log-Rank p-value = ',num2str(cur_pvx(fvol,5),'%10.2e\n'))};
%
%
%
% % (di,vj)
% numend=length(g)-numstart;
% for v=fvol:fvol
% % check smaple size
% flg_volbelow1=vd(g)<=CG.mBinsVol(v); f=length(find(flg_volbelow1)); % group DVHs by (d,v)
% if f<numstart || f>numend % one group has too less patients, or the volume at the dose is too small, skip it
% continue;
% end
%
% % assign properties of object sa
% survivedate={compdate(g(flg_volbelow1)); compdate(g(~flg_volbelow1))}; % survive time of each group
% fcensor={flgcensor(g(flg_volbelow1)); flgcensor(g(~flg_volbelow1))}; % censor flag for each group
% sa.mSurvivalTime=survivedate;
% sa.mFlgCensor=fcensor;
% % compute survival curves and compare them
% sa=sa.fCalculateSurvivalCurve();
% sa=sa.fCombineSurvivalTime();
% sa=sa.fCompareSurvivalByLogrank();
% end
%
%
% vol2 = 30; %cc
% [~,fvol2] = min(abs(CGobj{m}.mBinsVol - vol2));
% disp(['Median value of V_{',num2str(dose),'} = ',num2str(vol2)]);
% disp(['Log-Rank p-value of V_{',num2str(dose),...
% '} split at ',num2str(vol2),...
% ' = ',num2str(cur_pvx(fvol2,5))]);
% str_pval3 = {strcat('Log-Rank p-value = ',num2str(cur_pvx(fvol2,5),'%10.2e\n'))};
%
% for v=fvol2:fvol2
% % check smaple size
% flg_volbelow1=vd(g)<=CG.mBinsVol(v); f=length(find(flg_volbelow1)); % group DVHs by (d,v)
% if f<numstart || f>numend % one group has too less patients, or the volume at the dose is too small, skip it
% continue;
% end
%
% % assign properties of object sa
% survivedate={compdate(g(flg_volbelow1)); compdate(g(~flg_volbelow1))}; % survive time of each group
% fcensor={flgcensor(g(flg_volbelow1)); flgcensor(g(~flg_volbelow1))}; % censor flag for each group
% sa2.mSurvivalTime=survivedate;
% sa2.mFlgCensor=fcensor;
% % compute survival curves and compare them
% sa2=sa2.fCalculateSurvivalCurve();
% sa2=sa2.fCombineSurvivalTime();
% sa2=sa2.fCompareSurvivalByLogrank();
% end
%
%
%
% end
%
%
% % plot KM curves
% % V_{30} split at median: 50cc
% f8=figure(8); clf reset; hold on; % grid on;
% set(f8,'Position',[0 0 screen_size(3)/2 screen_size(4)/2]);
% h_km(1)=stairs(sa.mSurvivalTimeSorted{1},1-sa.mSurvivalCurve{1});
% plot(sa.mSurvivalTimeSorted{1}(sa.mCensorStatistics{1}(:,1)),...
% 1-sa.mSurvivalCurve{1}(sa.mCensorStatistics{1}(:,1)),'+');
% h_km(2)=stairs(sa.mSurvivalTimeSorted{2},1-sa.mSurvivalCurve{2},'r');
% plot(sa.mSurvivalTimeSorted{2}(sa.mCensorStatistics{2}(:,1)),...
% 1-sa.mSurvivalCurve{2}(sa.mCensorStatistics{2}(:,1)),'r+');
% % xticks = get(gca,'Xlim'); set(gca,'XTick',0:6:max(xticks));
% text(38,0.25,str_pval2,'FontSize',14);
% ylim([0 0.53]);
% lgnd=legend(h_km,...
% strcat('V$_{30}\leq',num2str(vol,4),'$~cc'),...
% strcat('V$_{30}\geq',num2str(vol,4),'$~cc'),'Location','Best');
% set(lgnd,'FontSize',14);
% h=legend;
% set(h,'interpreter','latex');
%
% set(gca,'xminortick','on','yminortick','on');
% xlabel(['Months'],'fontsize',14);
% ylabel(['Probability of CW Pain'],'fontsize',14);
% title(strcat('V_{30}, Median split (',num2str(vol),' cc)'),'fontsize',14);
%
% % plot KM curves
% % V_{30} split at 30cc
% f9=figure(9); clf reset; hold on; % grid on;
% set(f9,'Position',[0 0 screen_size(3)/2 screen_size(4)/2]);
%
% h_km(1)=stairs(sa2.mSurvivalTimeSorted{1},1-sa2.mSurvivalCurve{1});
% plot(sa2.mSurvivalTimeSorted{1}(sa2.mCensorStatistics{1}(:,1)),...
% 1-sa2.mSurvivalCurve{1}(sa2.mCensorStatistics{1}(:,1)),'+');
% h_km(2)=stairs(sa2.mSurvivalTimeSorted{2},1-sa2.mSurvivalCurve{2},'r');
% plot(sa2.mSurvivalTimeSorted{2}(sa2.mCensorStatistics{2}(:,1)),...
% 1-sa2.mSurvivalCurve{2}(sa2.mCensorStatistics{2}(:,1)),'r+');
% % xticks = get(gca,'Xlim'); set(gca,'XTick',0:6:max(xticks));
% text(38,0.25,str_pval3,'FontSize',14);
% lgnd=legend(h_km,...
% strcat('V$_{30}\leq',num2str(vol2,4),'$~cc'),...
% strcat('V$_{30}\geq',num2str(vol2,4),'$~cc'),'Location','Best');
% set(lgnd,'FontSize',14);
% h=legend;
% set(h,'interpreter','latex');
% ylim([0 0.53]);
% set(gca,'xminortick','on','yminortick','on');
% xlabel(['Months'],'fontsize',14);
% ylabel(['Probability of CW Pain'],'fontsize',14);
% title('V_{30}, 30cc split','fontsize',14);
dose=30;
split=med_vols(31);
fPlotKaplanMeierCurve_VDx(CGobj,dose,split);
fig_ctr=fig_ctr+1;
ylim([0 0.65]);
dose=30;
split=50;
fPlotKaplanMeierCurve_VDx(CGobj,dose,split);
fig_ctr=fig_ctr+1;
ylim([0 0.65]);
dose=30;
split=30;
fPlotKaplanMeierCurve_VDx(CGobj,dose,split);
fig_ctr=fig_ctr+1;
ylim([0 0.53]);
dose=30;
split=70;
fPlotKaplanMeierCurve_VDx(CGobj,dose,split);
fig_ctr=fig_ctr+1;
ylim([0 0.53]);
%% BMI split
% at median
[bmiCox,~,~] = CGobj{m}.fCoxParameter_DVH('BMI');
bmi_data = [bmiCox.data_exposure];
bmi_idx = bmi_data>0;
bmi_data = bmi_data(bmi_idx);
numstart=CG.mLogRankMinSize;
%here
bmi_split = median(bmi_data);
% (di,vj)
numend=length(bmi_data)-numstart;
flg_volbelow1= (bmi_data<bmi_split);
f=length(find(flg_volbelow1)); % group DVHs by (d,v)
if f<numstart || f>numend % one group has too less patients, or the volume at the dose is too small, skip it
continue;
end
% assign properties of object sa
% survivedate={compdate(flg_volbelow1); compdate(~flg_volbelow1)}; % survive time of each group
% fcensor={flgcensor(flg_volbelow1); flgcensor(~flg_volbelow1)}; % censor flag for each group
% sa.mSurvivalTime=survivedate;
% sa.mFlgCensor=fcensor;
% % compute survival curves and compare them
% sa=sa.fCalculateSurvivalCurve();
% sa=sa.fCombineSurvivalTime();
% sa=sa.fCompareSurvivalByLogrank();
[~,fsplit] = min(abs(unique(bmi_data)-bmi_split));
f = cellfun(@(x) strcmpi('BMI',x),CGobj{m}.mLogRank(:,1));
if ~isempty(f)
pvx = CGobj{m}.mLogRank{f,2};
%here
sa = pvx{2}{fsplit};
pvx = pvx{1};
pvx = squeeze(pvx(fsplit,:,:));
f = pvx(:,6) < 2;
str_pval1 = {strcat('Log-Rank p-value = ',num2str(pvx(1,5),'%10.2e\n'))};
% plot KM curves
f11=figure(fig_ctr); clf reset; hold on; % grid on;
fig_ctr=fig_ctr+1;
set(f11,'Position',[0 0 screen_size(3)/2 screen_size(4)/2]);
h_km(1)=stairs(sa.mSurvivalTimeSorted{1},1-sa.mSurvivalCurve{1});
plot(sa.mSurvivalTimeSorted{1}(sa.mCensorStatistics{1}(:,1)),...
1-sa.mSurvivalCurve{1}(sa.mCensorStatistics{1}(:,1)),'+');
h_km(2)=stairs(sa.mSurvivalTimeSorted{2},1-sa.mSurvivalCurve{2},'r');
plot(sa.mSurvivalTimeSorted{2}(sa.mCensorStatistics{2}(:,1)),...
1-sa.mSurvivalCurve{2}(sa.mCensorStatistics{2}(:,1)),'r+');
% xticks = get(gca,'Xlim'); set(gca,'XTick',0:6:max(xticks));
ylim([0 0.55]);
text(38,0.12,str_pval1,'FontSize',16);
lgnd=legend(h_km,...
strcat('$\rm{BMI} <',num2str(bmi_split,4),'$'),...
strcat('$\rm{BMI} \geq',num2str(bmi_split,4),'$'),...
'Location','Best');
set(lgnd,'FontSize',16);
h=legend;
set(h,'interpreter','latex');
set(gca,'xminortick','on','yminortick','on');
xlabel(['Months'],'fontsize',14);
ylabel(['Probability of CW Pain'],'fontsize',14);
title('BMI, Median split','fontsize',14);
f = cellfun(@(x) strcmpi('BMI',x),CGobj{m}.mLogRank(:,1));
pvx = CGobj{m}.mLogRank{f,2};% vmax
pvx = pvx{1}
%pvx = squeeze(pvx(fdose,:,:)); % look at volumes V_{fdose}, then pvx = [V_{fdose} bins]X[n1,c1,n2,c2,p,flg]
f = pvx(:,6) == 0;
f12=figure(fig_ctr); clf reset;
fig_ctr=fig_ctr+1;
set(f12,'Position',[0 0 screen_size(3)/2 screen_size(4)/2]);
unique_bmi_data = unique(bmi_data);
[min_pvx,idx_pvx] = min(pvx(f,5));
unique_bmi_data = unique_bmi_data(f);
semilogy(unique_bmi_data,pvx(f,5),'-','LineWidth',2); hold on;
semilogy([min(unique_bmi_data) max(unique_bmi_data)],[0.05 0.05],'r--','LineWidth',2);
semilogy([unique_bmi_data(idx_pvx) unique_bmi_data(idx_pvx)],ylim,'g--','LineWidth',2);
hold off; % grid on;
xlim([min(unique_bmi_data) max(unique_bmi_data)]);
xlabel('BMI','fontsize',14);
ylabel('p-value','fontsize',14);
title(strcat('Log-Rank p-values for BMI split values'),'fontsize',14);
str_pval1 = {strcat('Log-Rank p-value = ',...
num2str(min_pvx,'%10.2e\n'),...
' at BMI = ',...
num2str(unique_bmi_data(idx_pvx)))};
text(15,0.001,str_pval1,'FontSize',14);
%% BMI at min pval
best_bmi = unique_bmi_data(idx_pvx);
[bmiCox,~,~] = CGobj{m}.fCoxParameter_DVH('BMI');
bmi_data = [bmiCox.data_exposure];
bmi_idx = bmi_data>0;
bmi_data = bmi_data(bmi_idx);
numstart=CG.mLogRankMinSize;
%here
bmi_split = best_bmi;
% (di,vj)
numend=length(bmi_data)-numstart;
flg_volbelow1= (bmi_data<bmi_split);
f=length(find(flg_volbelow1)); % group DVHs by (d,v)
if f<numstart || f>numend % one group has too less patients, or the volume at the dose is too small, skip it
continue;
end
cox_beta=coxphfit(~flg_volbelow1,compdate(bmi_idx),'baseline',0,'censoring',flgcensor(bmi_idx));
cox_hr = exp(cox_beta);
disp(['HR(BMI,min p-val): ',num2str(cox_hr)]);
% assign properties of object sa
% survivedate={compdate(flg_volbelow1); compdate(~flg_volbelow1)}; % survive time of each group
% fcensor={flgcensor(flg_volbelow1); flgcensor(~flg_volbelow1)}; % censor flag for each group
% sa.mSurvivalTime=survivedate;
% sa.mFlgCensor=fcensor;
% % compute survival curves and compare them
% sa=sa.fCalculateSurvivalCurve();
% sa=sa.fCombineSurvivalTime();
% sa=sa.fCompareSurvivalByLogrank();
%
[~,fsplit] = min(abs(unique(bmi_data)-bmi_split));
f = cellfun(@(x) strcmpi('BMI',x),CGobj{m}.mLogRank(:,1));
pvx = CGobj{m}.mLogRank{f,2};
% here
sa = pvx{2}{fsplit};
pvx = pvx{1};
pvx = squeeze(pvx(fsplit,:,:));
f = pvx(:,6) < 2;
str_pval1 = {strcat('Log-Rank p-value = ',num2str(pvx(1,5),'%10.2e\n'))};
% plot KM curves
f13=figure(fig_ctr); clf reset; hold on; % grid on;
fig_ctr=fig_ctr+1;
set(f13,'Position',[0 0 screen_size(3)/2 screen_size(4)/2]);
h_km(1)=stairs(sa.mSurvivalTimeSorted{1},1-sa.mSurvivalCurve{1});
plot(sa.mSurvivalTimeSorted{1}(sa.mCensorStatistics{1}(:,1)),...
1-sa.mSurvivalCurve{1}(sa.mCensorStatistics{1}(:,1)),'+');
h_km(2)=stairs(sa.mSurvivalTimeSorted{2},1-sa.mSurvivalCurve{2},'r');
plot(sa.mSurvivalTimeSorted{2}(sa.mCensorStatistics{2}(:,1)),...
1-sa.mSurvivalCurve{2}(sa.mCensorStatistics{2}(:,1)),'r+');
% xticks = get(gca,'Xlim'); set(gca,'XTick',0:6:max(xticks));
ylim([0 0.55]);
text(38,0.15,str_pval1,'FontSize',16);
lgnd=legend(h_km,...
strcat('$\rm{BMI} <',num2str(bmi_split,4),'$'),...
strcat('$\rm{BMI} \geq',num2str(bmi_split,4),'$'),...
'Location','Best');
set(lgnd,'FontSize',16);
h=legend;
set(h,'interpreter','latex');
set(gca,'xminortick','on','yminortick','on');
xlabel(['Months'],'fontsize',14);
ylabel(['Probability of CW Pain'],'fontsize',14);
title('BMI, best split','fontsize',14);
%% BMI at 29
best_bmi = unique_bmi_data(idx_pvx);
[bmiCox,~,~] = CGobj{m}.fCoxParameter_DVH('BMI');
bmi_data = [bmiCox.data_exposure];
bmi_idx = bmi_data>0;
bmi_data = bmi_data(bmi_idx);
numstart=CG.mLogRankMinSize;
%here
%bmi_split = 29;
bmi_split = 30;
% (di,vj)
numend=length(bmi_data)-numstart;
flg_volbelow1= (bmi_data<bmi_split);
f=length(find(flg_volbelow1)); % group DVHs by (d,v)
if f<numstart || f>numend % one group has too less patients, or the volume at the dose is too small, skip it
continue;
end
% assign properties of object sa
% survivedate={compdate(flg_volbelow1); compdate(~flg_volbelow1)}; % survive time of each group
% fcensor={flgcensor(flg_volbelow1); flgcensor(~flg_volbelow1)}; % censor flag for each group
% sa.mSurvivalTime=survivedate;
% sa.mFlgCensor=fcensor;
% % compute survival curves and compare them
% sa=sa.fCalculateSurvivalCurve();
% sa=sa.fCombineSurvivalTime();
% sa=sa.fCompareSurvivalByLogrank();
[~,fsplit] = min(abs(unique(bmi_data)-bmi_split));
f = cellfun(@(x) strcmpi('BMI',x),CGobj{m}.mLogRank(:,1));
pvx = CGobj{m}.mLogRank{f,2};
%here
sa = pvx{2}{fsplit};
cox_beta=coxphfit(~flg_volbelow1,compdate(bmi_idx),'baseline',0,'censoring',flgcensor(bmi_idx));
cox_hr = exp(cox_beta);
disp(['Cox HR for BMI: ',num2str(cox_hr)]);
pvx = pvx{1};
pvx = squeeze(pvx(fsplit,:,:));
f = pvx(:,6) < 2;
%str_pval1 = {strcat('Log-Rank p-value = ',num2str(pvx(1,5),'%10.2e\n'))};
str_pval1 = ['Log-Rank p-value = ',num2str(pvx(1,5),'%3.1e\n'),10,...
'HR = ',num2str(cox_hr,'%3.1f')];
% plot KM curves
f14=figure(fig_ctr); clf reset; hold on; % grid on;
fig_ctr=fig_ctr+1;
set(f14,'Position',[0 0 screen_size(3)/2 screen_size(4)/2]);
h_km(1)=stairs(sa.mSurvivalTimeSorted{1}./12,1-sa.mSurvivalCurve{1});
plot(sa.mSurvivalTimeSorted{1}(sa.mCensorStatistics{1}(:,1))./12,...
1-sa.mSurvivalCurve{1}(sa.mCensorStatistics{1}(:,1)),'+');
h_km(2)=stairs(sa.mSurvivalTimeSorted{2}./12,1-sa.mSurvivalCurve{2},'r');
plot(sa.mSurvivalTimeSorted{2}(sa.mCensorStatistics{2}(:,1))./12,...
1-sa.mSurvivalCurve{2}(sa.mCensorStatistics{2}(:,1)),'r+');
% xticks = get(gca,'Xlim'); set(gca,'XTick',0:6:max(xticks));
ylim([0 0.65]);
textbp(str_pval1,'FontSize',16);
lgnd=legend(h_km,...
strcat('$\rm{BMI} <',num2str(bmi_split,4),'$'),...
strcat('$\rm{BMI} \geq',num2str(bmi_split,4),'$'),...
'Location','Best');
set(lgnd,'FontSize',18);
h=legend;
set(h,'interpreter','latex');
set(gca,'xminortick','on','yminortick','on');
set(gca,'fontsize',16);
xlabel(['Years'],'fontsize',20);
ylabel(['Incidence of CW Pain'],'fontsize',20);
title(['BMI, split at ',num2str(bmi_split,4)],'fontsize',20);
%% V_{39} + BMI split
dose=39;
[~,fdose] = min(abs(CGobj{m}.mBinsDose - dose));
[bmiCox,~,~] = CGobj{m}.fCoxParameter_DVH('BMI');
bmi_data = [bmiCox.data_exposure];
bmi_idx = bmi_data>0;
vd=zeros(CG.mNumInGrp,1); % volume v at dose d
numstart=CG.mLogRankMinSize;
for d=fdose:fdose
% volume under d
vd(:)=0;
for k=1:CG.mNumInGrp
vd(k) = CG.mGrp(k).fVolAtDose( CG.mBinsDose(d) );
end
%g=find(vd>-1); % non-zeros volume cases
g=find(bmi_idx);
%here
vd_bmi = 0.0207.*vd(g) + 0.0408.*bmi_data(g);
vd_bmi_split = median(vd_bmi);
%disp(['Median value of V_{',num2str(dose),'} = ',num2str(vol)]);
%disp(['Log-Rank p-value of V_{',num2str(dose),...
% '} split at ',num2str(vol),...
% ' = ',num2str(pvx(fvol,5))]);
% (di,vj)
numend=length(g)-numstart;
flg_volbelow1= (vd_bmi<vd_bmi_split);
f=length(find(flg_volbelow1)); % group DVHs by (d,v)
if f<numstart || f>numend % one group has too less patients, or the volume at the dose is too small, skip it
continue;
end
%
% % assign properties of object sa
% survivedate={compdate(flg_volbelow1); compdate(~flg_volbelow1)}; % survive time of each group
% fcensor={flgcensor(flg_volbelow1); flgcensor(~flg_volbelow1)}; % censor flag for each group
% sa.mSurvivalTime=survivedate;
% sa.mFlgCensor=fcensor;
% % compute survival curves and compare them
% sa=sa.fCalculateSurvivalCurve();
% sa=sa.fCombineSurvivalTime();
% sa=sa.fCompareSurvivalByLogrank();
end
[~,fsplit] = min(abs(unique(vd_bmi)-vd_bmi_split));
f = cellfun(@(x) strcmpi('V39BMI',x),CGobj{m}.mLogRank(:,1));
if ~isempty(f)
pvx = CGobj{m}.mLogRank{f,2};
sa = pvx{2}{fsplit};
pvx = pvx{1};
pvx = squeeze(pvx(fsplit,:,:));
f = pvx(:,6) < 2;
str_pval1 = {strcat('Log-Rank p-value = ',num2str(pvx(1,5),'%10.2e\n'))};
end
% plot KM curves
f10=figure(fig_ctr); clf reset; hold on; % grid on;
fig_ctr=fig_ctr+1;
set(f10,'Position',[0 0 screen_size(3)/2 screen_size(4)/2]);
h_km(1)=stairs(sa.mSurvivalTimeSorted{1},1-sa.mSurvivalCurve{1});
plot(sa.mSurvivalTimeSorted{1}(sa.mCensorStatistics{1}(:,1)),...
1-sa.mSurvivalCurve{1}(sa.mCensorStatistics{1}(:,1)),'+');
h_km(2)=stairs(sa.mSurvivalTimeSorted{2},1-sa.mSurvivalCurve{2},'r');
plot(sa.mSurvivalTimeSorted{2}(sa.mCensorStatistics{2}(:,1)),...
1-sa.mSurvivalCurve{2}(sa.mCensorStatistics{2}(:,1)),'r+');
% xticks = get(gca,'Xlim'); set(gca,'XTick',0:6:max(xticks));
text(38,0.25,str_pval1,'FontSize',16);
ylim([0 0.8]);
lgnd=legend(h_km,...
strcat('$\beta_{V_{39}}\times V_{39}+\beta_{BMI}\times BMI <',num2str(vd_bmi_split,3),'$'),...
strcat('$\beta_{V_{39}}\times V_{39}+\beta_{BMI}\times BMI \geq',num2str(vd_bmi_split,3),'$'),...
'Location','NorthEast');
set(lgnd,'FontSize',14);
h=legend;
set(h,'interpreter','latex');
set(gca,'xminortick','on','yminortick','on');
xlabel(['Months'],'fontsize',14);
ylabel(['Probability of CW Pain'],'fontsize',14);
title('V_{39} + BMI, Median split','fontsize',14);
end
%% V_{30} + BMI split
dose=30;
[~,fdose] = min(abs(CGobj{m}.mBinsDose - dose));
[bmiCox,~,~] = CGobj{m}.fCoxParameter_DVH('BMI');
bmi_data = [bmiCox.data_exposure];
bmi_idx = bmi_data>0;
vd=zeros(CG.mNumInGrp,1); % volume v at dose d
numstart=CG.mLogRankMinSize;
for d=fdose:fdose
% volume under d
vd(:)=0;
for k=1:CG.mNumInGrp
vd(k) = CG.mGrp(k).fVolAtDose( CG.mBinsDose(d) );
end
%g=find(vd>-1); % non-zeros volume cases
g=find(bmi_idx);
%here
vd_bmi = 0.0131*vd(g) + 0.0426*bmi_data(g);
vd_bmi_split = median(vd_bmi);
numend=length(g)-numstart;
flg_volbelow1= (vd_bmi<vd_bmi_split);
f=length(find(flg_volbelow1)); % group DVHs by (d,v)
if f<numstart || f>numend % one group has too less patients, or the volume at the dose is too small, skip it
continue;
end
% sa.mSurvivalTime=survivedate;
% sa.mFlgCensor=fcensor;
% % compute survival curves and compare them
% sa=sa.fCalculateSurvivalCurve();
% sa=sa.fCombineSurvivalTime();
% sa=sa.fCompareSurvivalByLogrank();
[~,fsplit] = min(abs(unique(vd_bmi)-vd_bmi_split));
f = cellfun(@(x) strcmpi('V30BMI',x),CGobj{m}.mLogRank(:,1));
pvx = CGobj{m}.mLogRank{f,2};
sa = pvx{2}{fsplit};
pvx = pvx{1};
pvx = squeeze(pvx(fsplit,:,:));
f = pvx(:,6) < 2;
str_pval1 = {strcat('Log-Rank p-value = ',num2str(pvx(1,5),'%10.2e\n'))};
end
% plot KM curves
f16=figure(fig_ctr); clf reset; hold on; % grid on;
fig_ctr=fig_ctr+1;
set(f16,'Position',[0 0 screen_size(3)/2 screen_size(4)/2]);
h_km(1)=stairs(sa.mSurvivalTimeSorted{1},1-sa.mSurvivalCurve{1});
plot(sa.mSurvivalTimeSorted{1}(sa.mCensorStatistics{1}(:,1)),...
1-sa.mSurvivalCurve{1}(sa.mCensorStatistics{1}(:,1)),'+');
h_km(2)=stairs(sa.mSurvivalTimeSorted{2},1-sa.mSurvivalCurve{2},'r');
plot(sa.mSurvivalTimeSorted{2}(sa.mCensorStatistics{2}(:,1)),...
1-sa.mSurvivalCurve{2}(sa.mCensorStatistics{2}(:,1)),'r+');
% xticks = get(gca,'Xlim'); set(gca,'XTick',0:6:max(xticks));
%text(38,0.25,str_pval1,'FontSize',16);
textbp(str_pval1,'FontSize',16);
ylim([0 0.65]);
lgnd=legend(h_km,...
strcat('$\beta_{V_{30}}\times V_{30}+\beta_{BMI}\times BMI <',num2str(vd_bmi_split,3),'$'),...
strcat('$\beta_{V_{30}}\times V_{30}+\beta_{BMI}\times BMI \geq',num2str(vd_bmi_split,3),'$'),...
'Location','NorthEast');
set(lgnd,'FontSize',18);
h=legend;
set(h,'interpreter','latex');
set(gca,'xminortick','on','yminortick','on');
set(gca,'fontsize',16);
xlabel(['Months'],'fontsize',20);
ylabel(['Probability of CW Pain'],'fontsize',20);
title('V_{30} + BMI, Median split','fontsize',20);
%% V_{30} + BMI + Tx
sa=classKaplanMeierCurve(); % initialize a survivalanalysis obj
dose=30;
[~,fdose] = min(abs(CGobj{m}.mBinsDose - dose));
[bmiCox,~,~] = CGobj{m}.fCoxParameter_DVH('BMI');
bmi_data = [bmiCox.data_exposure];
bmi_idx = bmi_data>0;
[txCox,~,~] = CGobj{m}.fCoxParameter_DVH('Tx');
tx_data = [txCox.data_exposure];
vd=zeros(CG.mNumInGrp,1); % volume v at dose d
numstart=CG.mLogRankMinSize;