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%%
acuteTBI=IDENTITY.DEMO(:,3)==2;
Sx_idxs=unique(IDENTITY.DEMO(acuteTBI,1));
AttritionPredictors=NaN(length(Sx_idxs),14);
for sxi=1:length(Sx_idxs)
thisguy=Sx_idxs(sxi);
FIRST=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==1) ));
SECOND=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==2) ));
THIRD=find(logical( double(IDENTITY.DEMO(:,1)==thisguy) .* double(IDENTITY.DEMO(:,2)==3) ));
if ~isempty(FIRST), Attrition(sxi,1)=1;
AttritionPredictors_HDR={'age';'sex';'TOPF';'Coding';'Span';'HVLT13';'HVLTDelay';'GCS';'LOCtime';'LOM';'Days';'BDI';'NSI';'FrSBe'};
AttritionPredictors(sxi,:)=[...
IDENTITY.DEMO(FIRST,6:7),...
IDENTITY.NP(FIRST,4:8),...
IDENTITY.TBI(FIRST,[4,6:8]),...
IDENTITY.QUEX(FIRST,[4,5,10]),...
];
else Attrition(sxi,1)=0; end
if ~isempty(SECOND), Attrition(sxi,2)=1; else Attrition(sxi,2)=0; end
if ~isempty(THIRD), Attrition(sxi,3)=1; else Attrition(sxi,3)=0; end
clear thisguy FIRST SECOND THIRD;
end
% Clear NaNs
AttritionPredictors=AttritionPredictors(~isnan(AttritionPredictors(:,1)),:);
Attrition=Attrition(~isnan(AttritionPredictors(:,1)),:);
for atti=1:size(AttritionPredictors,2)
for sessi=1:3 % 1 doesn't really make sense, but added here to keep columns nice
A=AttritionPredictors(:,atti);
B=Attrition(:,sessi);
A2=A(~isnan(A));
B2=B(~isnan(A));
% B_acc is [constant, v1, v2,v1*v2] [validated by SPSS]
% STATS_acc.p is the p value for each
[B_acc,DEV_acc,STATS_acc] = glmfit(zscore(A2),B2, 'binomial','link','logit');
Predictors_Logistic{sessi}(atti,1)=B_acc(2);
Predictors_Logistic{sessi}(atti,2)=STATS_acc.p(2);
[~,P,~,STATS]=ttest2(A2(B2==1),A2(B2==0));
Predictors_t{sessi}(atti,1)=STATS.tstat;
Predictors_t{sessi}(atti,2)=P;
[P,~,U]=ranksum(A2(B2==1),A2(B2==0));
Predictors_u{sessi}(atti,1)=U.zval;
Predictors_u{sessi}(atti,2)=P;
clear B_acc DEV_acc STATS_acc A B A2 B2 STATS P U;
end
end
% % % S2:
% Logistic - 5
% t-test - 5
% U-test - 5
% % % S3:
% Logistic - 3,5 [almost 4]
% t-test - 3,5 [almost 4]
% U-test - 3,4,5
% 3=TOPF
% 4=Coding
% 5=Span
% Sess 2 dropouts:
nanmean(AttritionPredictors(Attrition(:,2)==0,3:5))
% Sess 2 stays:
nanmean(AttritionPredictors(Attrition(:,2)==1,3:5))
% Sess 3 dropouts:
nanmean(AttritionPredictors(Attrition(:,3)==0,3:5))
% Sess 3 stays:
nanmean(AttritionPredictors(Attrition(:,3)==1,3:5))
% So Lower Span predicts S2 dropout and Lower Span, Coding, and TOPF predict S3 dropout
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