# Which method (enter, Forward LR or Backward LR) of logistic regression to use? [duplicate]

My study is a prospective observational study. My dependent variable (outcome) is development of surgical site infection (SSI) after surgery and my independent variables (predictors) are many factors containing socio-demographics, pre-operative, intra-operative and post-operative factors.

Outcome is dichotomous: SSI: 0=No , 1=Yes

Predictors are dichotomous as well as polychotomous( 3 or more categories), e.g. ASA score: 0=Class_1, 1=Class_2, 2=Class_3, 3=Class_4

I have already done the cross-tabulation (Chi square test) and i have also done univariate analysis using Enter method of binary logistics for every single variable.

Now i want to perform a multivariate analysis using all the predictors who came out to be significant in the univariate analysis (P= <0.25 as significant). I am now a bit confused which method i have to use in order to get more authentic results. I have seen literature similar to my study using simple logistic regression or forward step-wise regression as well. The references are as below:

Reference 1: http://www.ncbi.nlm.nih.gov/pubmed/23392976

Reference 2: http://www.ncbi.nlm.nih.gov/pubmed/11198018

My questions:

1) For polychotomous variables, i transformed them into dichotomous variables for one single category. e.g. I made 4 seperate columns for 4 classes of ASA score. and put them all individually in Univariate? and those who come out to be significant will be put in multivariate with 0=No as the reference category? Is this method acceptable?

2) Which method regarding binary logistics is the best as per my study?

i want to find out independent risk factors of SSI with Odds ratio?

Do not do any kind of stepwise variable selection, whether based on $p$ values, information criteria or anything else. Stepwise procedures invalidate subsequent inference. See here for a terse summary, and look through the references as needed. (Note that one author, Frank Harrell, knows what he is talking about.)