A systematic approach towards missing lab data in electronic health records: A case study in non‐small cell lung cancer and multiple myeloma

Abstract Real‐world data derived from electronic health records often exhibit high levels of missingness in variables, such as laboratory results, presenting a challenge for statistical analyses. We developed a systematic workflow for gathering evidence of different missingness mechanisms and performing subsequent statistical analyses. We quantify evidence for missing completely at random (MCAR) or missing at random (MAR), mechanisms using Hotelling's multivariate t‐test, and random forest classifiers, respectively. We further illustrate how to apply sensitivity analyses using the not at random fully conditional specification procedure to examine changes in parameter estimates under missing not at random (MNAR) mechanisms. In simulation studies, we validated these diagnostics and compared analytic bias under different mechanisms. To demonstrate the application of this workflow, we applied it to two exemplary case studies with an advanced non‐small cell lung cancer and a multiple myeloma cohort derived from a real‐world oncology database. Here, we found strong evidence against MCAR, and some evidence of MAR, implying that imputation approaches that attempt to predict missing values by fitting a model to observed data may be suitable for use. Sensitivity analyses did not suggest meaningful departures of our analytic results under potential MNAR mechanisms; these results were also in line with results reported in clinical trials

Divergent Patterns and Trends in Breast Cancer Incidence, Mortality and Survival Among Older Women in Germany and the United States

BACKGROUND: Breast cancer treatment has changed tremendously over the last decades. In addition, the use of mammography screening for early detection has increased strongly. To evaluate the impact of these developments, long-term trends in incidence, mortality, stage distribution and survival were investigated for Germany and the United States (US). METHODS: Using population-based cancer registry data, long-term incidence and mortality trends (1975–2015), shifts in stage distributions (1998–2015), and trends in five-year relative survival (1979–2015) were estimated. Additionally, trends in five-year relative survival after standardization for stage were explored (2004–2015). RESULTS: Age-standardized breast cancer incidence rates were much higher in the US than in Germany in all periods, whereas age-standardized mortality began to lower in the US from the 1990s on. The largest and increasing differences were observed for patients aged 70+ years with a 19% lower incidence but 45% higher mortality in Germany in 2015. For this age group, large differences in stage distributions were observed, with 29% (Germany) compared to 15% (US) stage III and IV patients. Age-standardized five-year relative survival increased strongly between 1979–1983 and 2013–2015 in Germany (+17% units) and the US (+19% units) but was 9% units lower in German patients aged 70+ years in 2013–2015. This difference was entirely explained by differences in stage distributions. CONCLUSIONS: Overall, our results are in line with a later uptake and less extensive utilization of mammography screening in Germany. Further studies and efforts are highly needed to further explore and overcome the increased breast cancer mortality among elderly women in Germany