Even in a carefully designed randomized trial, outcomes for some study participants can be missing, or more precisely, ill-defined, because participants had died prior to date of outcome collection. This problem, known as truncation by death, means that the treated and untreated are no longer balanced with respect to covariates determining survival. To overcome this problem, researchers often utilize principal stratification and focus on the Survivor Average Causal Effect (SACE). The SACE is the average causal effect among the subpopulation that will survive regardless of treatment status. In this paper, we present a new approach based on matching for SACE identification and estimation. We provide an identification result for the SACE that motivates the use of matching to restore the balance among the survivors. We discuss various practical issues, including the choice of distance measures, possibility of matching with replacement, post-matching crude and model-based SACE estimators, and non-parametric tests. Our simulation results demonstrate the flexibility and advantages of our approach. Because the cross-world assumptions needed for SACE identification can be too strong and are unfalsifiable, we also present sensitivity analysis techniques and illustrate their use in real data analysis. Finally, a recent alternative for SACE that does not demand cross-world unfalsifiable assumptions targets the conditional separable effects. We show how our approach can also be utilized to estimate these causal effects.

Background Quantitative estimates of collateral resistance induced by antibiotic use are scarce. Objectives To estimate the effects of treatment with amoxicillin/clavulanate or cefazolin, compared with cefuroxime, on future resistance to ceftazidime among hospitalized patients. Methods A retrospective analysis of patients with positive bacterial cultures hospitalized in an Israeli hospital during 2016–19 was conducted. Patients were restricted to those treated with amoxicillin/clavulanate, cefazolin or cefuroxime and re-hospitalized with a positive bacterial culture during the following year. Matching was performed using exact, Mahalanobis and propensity score matching. Each patient in the amoxicillin/clavulanate and cefazolin groups was matched to a single patient from the cefuroxime group, yielding 185:185 and 298:298 matched patients. Logistic regression and the g-formula (standardization) were used to estimate the OR, risk difference (RD) and number needed to harm (NNH). Results Cefuroxime induced significantly higher resistance to ceftazidime than amoxicillin/clavulanate or cefazolin; the marginal OR was 1.76 (95% CI = 1.16–2.83) compared with amoxicillin/clavulanate and 1.98 (95% CI = 1.41–2.8) compared with cefazolin and the RD was 0.118 (95% CI = 0.031–0.215) compared with amoxicillin/clavulanate and 0.131 (95% CI = 0.058–0.197) compared with cefazolin. We also estimated the NNH; replacing amoxicillin/clavulanate or cefazolin with cefuroxime would yield ceftazidime resistance in 1 more patient for every 8.5 (95% CI = 4.66–32.14) or 7.6 (95% CI = 5.1–17.3) patients re-hospitalized in the following year, respectively. Conclusions Our results indicate that treatment with amoxicillin/clavulanate or cefazolin is preferable to cefuroxime, in terms of future collateral resistance. The results presented here are a first step towards quantitative estimations of the ecological damage caused by different antibiotics.

Even in a carefully designed randomised trial, outcomes for some study participants can be missing, or more precisely, ill defined, because participants had died prior to outcome collection. This problem, known as truncation by death, means that the treated and untreated are no longer balanced with respect to covariates determining survival. Therefore, researchers often utilise principal stratification and focus on the Survivor Average Causal Effect (SACE). We present matching-based methods for SACE identification and estimation. We provide identification results motivating the use of matching and discuss practical issues, including the choice of distance measures, matching with replacement, and post-matching estimators. Because the assumptions needed for SACE identification can be too strong, we also present sensitivity analysis techniques and illustrate their use in real data analysis.

Background: Quantitative estimates of collateral resistance induced by antibiotic use are scarce. This study compared the effects of treatment with amoxicillin/clavulanate or cefazolin, compared to cefuroxime, on future resistance to ceftazidime among hospitalized patients. Methods: A retrospective analysis of patients with positive bacterial cultures hospitalized in an Israeli hospital during 2016-2019 was conducted. Patients were restricted to those treated with either amoxicillin/clavulanate, cefazolin, or cefuroxime and re-hospitalized with a positive bacterial culture during the following year. A 1:1 matching was performed for each patient in the amoxicillin/clavulanate and cefazolin groups, to a single patient from the cefuroxime group, yielding 185:185 and 298:298 matched patients. Logistic regression and g-formula (standardization) were used to estimate the odds ratio (OR), risk difference (RD), and number needed to harm (NNH). Results: Cefuroxime induced significantly higher resistance to ceftazidime than amoxicillin/clavulanate or cefazolin: the marginal OR was 1.76) 95%CI 1.16-2.83) compared to amoxicillin/clavulanate, and 1.98 (95%CI 1.41- 2.8) compared to cefazolin; The RD was 0.118 (95%CI 0.031-0.215) compared to amoxicillin/clavulanate, and 0.131 (95%CI 0.058-0.197) compared to cefazolin. We also estimated the NNH: replacing amoxicillin/clavulanate or cefazolin with cefuroxime would yield ceftazidime-resistance in one more patient for every 8.5 (95% CI 4.66-32.14) or 7.6 (95% CI 5.1-17.3) patients re-hospitalized in the following year. Conclusions: Our results indicate that treatment with amoxicillin/clavulanate or cefazolin is preferable to cefuroxime, in terms of future collateral resistance. The results presented here are a first step towards quantitative estimations of the ecological damage caused by different antibiotics.