Biostat 215 Discussion #1 Thomas B. Newman, MD, MPH with thanks to Gabriel Escobar, MD; Michael Kuzniewicz, MD, MPH, Chuck, Eric and Steve)

Outline n Background about jaundice and phototherapy n Discussion/Review of some key topics –Potential and counterfactual outcomes –Underlying assumptions –Causal model statements as if/then statements

Background for phototherapy dataset n Bilirubin: Yellow breakdown product of heme n Jaundice: Yellow color due to high bilirubin. n DAT: Direct Antiglobulin (“Coombs”) Test: measures maternal antibodies on infant red cells, a cause of jaundice n Phototherapy: Shining light on the babies skin -- helps lower bilirubin levels n Exchange transfusion: replace baby’s blood with donor blood – more drastic treatment to lower bilirubin levels n Kernicterus: Permanent brain damage (cerebral palsy, deafness) from very high bilirubin levels (and other illness)

Background -2 n 2/3 of newborns develop jaundice n 5-15% in Northern CA Kaiser hospitals are treated with phototherapy (PT) n Current treatment thresholds higher than used in previous trials n No randomized trials of PT as currently recommended

AAP Phototherapy Guidelines

AAP Exchange Transfusion Guidelines

NNT paper title n Research Questions n For newborns with total serum bilirubin (TSB) levels close to those at which the AAP recommends phototherapy –What is the efficacy of phototherapy? –What is the number needed to treat (NNT)? –What factors affect the NNT?

Methods n Design: Historical cohort study using electronically available data n Setting: 12 Northern California Kaiser Permanente Medical Care Program hospitals, n Subjects eligible if ≥ 2000 g, ≥ 35 weeks gestation and qualifying TSB level ± 3 mg/dL from AAP phototherapy threshold (PTT) (N=22,547)

“Qualifying” TSB levels

Qualifying TSB levels and key confounder TSB - PTT = + 3 mg/dL TSB - PTT = -2 mg/dL

Qualifying TSB levels and key confounder In phototherapy.dta this variable is called qual_TSB and is coded as follows: Qualifying TSB – PT Threshold (mg/dL) 1-3 to < to < to <0 40 to <1 51 to <2 62 to <3

Methods, cont'd n Intervention: hospital phototherapy within 8 hours of the qualifying TSB (“phototherapy”) n Covariates: age, birth weight, gestational age, hospital of birth, sex, qual_TSB (6 categories), year of birth

Outcome variable n Crossing the AAP exchange transfusion threshold within 48 hours of the qualifying TSB = “over_thresh” n Rationale –Incorporates age and AAP risk group –If ET threshold crossed after 48 hours, initial decision not to do PT probably reasonable

TSB levels  AAP Exchange level

Questions?

Possible discussion questions -1 n What do we mean my potential outcomes and counterfactual outcomes? n Potential outcome estimation is deterministic, but the world isn’t. Is this a problem? n Why is potential outcome estimation like a missing data problem? What does it have to do with imputation?

Possible discussion questions -2 n What is the “positivity” assumption? (Also called the “experimental treatment assumption” = ETA). n What is the “randomization” assumption? n Why should estimates from causal models be considered if…then statements? n Other questions?

Splines -1 n Want to relate a continuous predictor to an outcome n Not reasonable to assume relationship is linear n What to do? n Example: relationship between birth weight and outcome (“over_thresh”) in PT dataset

Switch to Stata

lowess over_thresh birth_wt, logit

Stata code *Biostat 215 Discussion #1 use "/Users/thomasnewman/Documents/TEACH/BIOSTAT/Biostat 215 Causal modeling MSMs DAGs etc/Stata and labs/phototherapy.dta" tab birth_wt logistic over_thresh birth_wt estat gof, group(10) tab * Logistic model fits poorly tabu bwcat over_thresh, row chi * Can use indicator variables logistic over_thresh i.bwcat * What will estat gof show? estat gof, group(10) tab * Indicator variables lead to discontinuity at cutoffs. * Demonstrate REGULAR splines mkspline bwsp 5 = birth_wt, di scatter bwsp1 birth_wt scatter bwsp2 birth_wt scatter bwsp3 birth_wt scatter bwsp4 birth_wt ggen junk = bwsp1+ bwsp2+ bwsp3+ bwsp4+ bwsp5 regress birth_wt junk logistic over bwsp* * Contrast with RESTRICTED CUBIC SPLINES mkspline bwsp = birth_wt, cubic scatter bwsp1 birth_wt scatter bwsp2 birth_wt scatter bwsp3 birth_wt scatter bwsp4 birth_wt logistic over bwsp* **Alternatively, include quadratic term sum birth_wt ggen bw2=(birth_wt-3.324)^2 logistic over birth_wt bw2 estat gof, group(10) tab