I have been reading many of your comments and I was hoping that you could give me advice. I'm currently in my second year of the dissertation process of my PhD. My committee chair has not been very helpful in guiding me in which type of statistical analysis I should select for my proposed study. If I give you some basic information, I'm hoping you can give me some advice on what you would do if it were you so that this study can be completed as efficiently as possibly. I've been at the development/proposal stage of this study now for 2 years, and I'm not sure how much longer I can go around in circles. Any feedback would be extremely helpful!

Research Question:

Which prenatal/postnatal environmental risk factor is more prevalent in children with autism spectrum disorder (ASD) than control children? (I'm considering changing the maternal perception of stress and blood lead levels to dichotomous variables, before they were continuous)

Recruit (a goal of) 75 children ranging in ages from 2-5:11 that have a medical diagnosis or school classification of ASD, match them with 75-typically developing children matched via age, gender, and parental education. Those with ASD can be sampled from a variety of sources in Yuma County- Division of Developmental Disabilities (state-based services), schools, private clinics, support groups. Typicals can be recruited from regular daycare/preschool programs, pediatrician offices.

IV1- Maternal perception of stress during pregnancy via maternal retrospective recall of stress

IV2- Blood lead levels obtained via medical release from pediatrician's office

DV- Absence/Diagnosis of ASD (I'm considering making all three dichotomous variables)

2. Description of Data Analysis:

1:1 matched case-control study of offspring (2-5) with ASD. 56 matched pairs. Controls will be age, gender, and parental education (SES)-matched without ASD.

The goal of the analysis is to determine whether the diagnosis of ASD is related to perinatal/postnatal environmental risk factors (blood lead levels, maternal perception of stress).

If I use conditional likelihood logistic regression, I can use the information contained in the matches. I won’t be able to estimate the intercept, and with no intercept in the conditional logistic regression model, I can’t estimate P. Odds ratio can be estimated using the beta so we can estimate other effects that I’m interested in (besides intercept).

Conditional Likelihood (Lc)

Lc = πP(Xi) π[1-P(Xi)] / Σ{ΣP(Xi) π[1-P(Xi)] }

= πexp(ΣβiXi) / Σ[πexp(ΣβiXi)]

πP(Xi) =probability of pair’s case having the risk factor

π[1-P(Xi)] = probability of pair’s control not having the risk factor

Σ{ΣP(Xi) π[1-P(Xi)] } = the joint probability that either the case or control

has had the risk factor.