In the SEER-Medicare database, differences in breast cancer survival between black and white women did not substantially change among women diagnosed between 1991 and 2005. These differences in survival appear primarily related to presentation characteristics at diagnosis rather than treatment differences.

IMPORTANCE Early-life antibiotic exposure has been associated with increased adiposity in animal models, mediated through the gut microbiome. Infant antibiotic exposure is common and often inappropriate. Studies of the association between infant antibiotics and childhood weight gain have reported inconsistent results. OBJECTIVE To assess the association between early-life antibiotic exposure and childhood weight gain. DESIGN AND SETTING Retrospective, longitudinal study of singleton births and matched longitudinal study of twin pairs conducted in a network of 30 pediatric primary care practices serving more than 200 000 children of diverse racial and socioeconomic backgrounds across Pennsylvania, New Jersey, and Delaware.

Among children with acute respiratory tract infections, broad-spectrum antibiotics were not associated with better clinical or patient-centered outcomes compared with narrow-spectrum antibiotics, and were associated with higher rates of adverse events. These data support the use of narrow-spectrum antibiotics for most children with acute respiratory tract infections.

In observational studies of treatment effects, matched samples have traditionally been constructed using two tools, namely close matches on one or two key covariates and close matches on the propensity score to stochastically balance large numbers of covariates. Here we propose a third tool, fine balance, obtained using the assignment algorithm in a new way. We use all three tools to construct a matched sample for an ongoing study of provider specialty in the treatment of ovarian cancer. Fine balance refers to exact balance of a nominal covariate, often one with many categories, but it does not require individually matched treated and control subjects for this variable. In the example the nominal variable has 72 = 9 × 8 categories formed from 9 possible years of diagnosis and 8 geographic locations or SEER sites. We obtain exact balance on the 72 categories and close individual matches on clinical stage, grade, year of diagnosis, and other variables using a distance, and stochastically balance a total of 61 covariates using a propensity score. Our approach finds an optimal match that minimizes a suitable distance subject to the constraint that fine balance is achieved. This is done by defining a special patterned distance matrix and passing it to a subroutine that solves the optimal assignment problem, which optimally pairs the rows and columns of a matrix using a polynomial time algorithm. In the example we used the function Proc Assign in SAS. A new theorem shows that with our patterned distance matrix, the assignment algorithm returns an optimal, finely balanced matched sample whenever one exists, and otherwise returns an infinite distance, indicating that no such matched sample exists.KEY WORDS: Assignment algorithm; Matched sampling; Observational study; Optimal matching; Propensity score. EXACT BALANCE WITHOUT EXACT MATCHINGIn an observational study of treatment effects, matched sampling is often used to select subjects for study who, before to treatment, appeared similar with respect to observed covariates. As one example, the Surgical Outcomes Study (Silber et al. 2001(Silber et al. , 2005 examined the effects of preoperative antibiotics on mortality after surgery in the Pennsylvania Medicare population by matching deaths to survivors using many covariates available from Medicare. For 681 matched pairs, paper hospital charts were located throughout Pennsylvania and individually abstracted to determine, among other things, preoperative antibiotics use. Although performing chart abstraction is too expensive for the entire Medicare population, it is essential if information not recorded by Medicare is to be used, and it is practical for a matched sample.Traditionally, matched samples have been constructed either by finding close matches on one or two key covariates, by finding close matches on the propensity score to stochastically balance large numbers of covariates, or by doing both at once. For a few key covariates, the Mahalanobis distance is often used (Rubin 1980). The propensity score is the conditio...