say i have a data of cancer patients who have fever and i want to see what factors are associated with mortality. After performing univariate analysis (crosstab in SPSS), i have 3 factors with p-value <0.05; allso=cause of fever found, hc=cause of fever was blood infection, pneu=cause of fever was lung infection. (all are binary, value 1 indicates presence of cause) Among the 3 variables "allso" is like a big set while "hc" and "pneu" are subset in "allso". "hc" and "pneu" have some overlapping case. ( allso = hc + pneu - overlap_between_hc_and_pneu + other )
After perform multivariate binary regression, only "allso" remains significant. It is correct for me to conclude that if a cause of fever was found, the patient will be more likely to die irrespective of what the cause is? (which make biological sense because identifiable cause may means a great load of bacteria)
Just for understanding, i perform multivariate regression with only "hc" and "pneu" as independent variable and the result: none of them are significant. Could you explain what happens in this situation i really don't get it.
thanks, i have tried to find a similar question but failed