An Introduction to the Priorities of the Michigan Cancer Consortium

These Notes from the Field serve as an introduction to the Priorities of the Michigan Cancer Consortium (MCC). This introduction lays the groundwork for four Notes from the Field intended to follow, that describe the MCC\u27s cancer control priorities and the results of collaborative efforts to address them

Lessons Learned from Revising the Cancer Plan for Michigan

Comprehensive Cancer Control (CCC) programs are a collaborative method to address cancer burden. Each CCC program is required to have a cancer plan to guide activities to reduce the cancer burden in their jurisdiction and should reflect the most recent research and evidence-based strategies. In 2015, Michigan set out to revise its CCC plan that met the needs of its coalition while using a participatory process involving its network of approximately 100 stakeholders. A collaborative approach involving multiple workgroups was used to develop the criteria and content for the CCC plan. The Michigan Department of Health and Human Services (MDHHS) staff did the research and the coalition workgroups finalized the objectives and strategies to include in the plan. The coalition\u27s leadership oversaw the process and approved the final CCC plan. The resulting CCC plan was brief, yet comprehensive, and based on data and research. The structured process resulted in a data driven CCC plan with input from stakeholders throughout the process. The Cancer Plan for Michigan is measurable and provides a clear method for assessing progress on reducing the cancer burden. The planning and support from MDHHS staff allowed for stakeholders to have focused conversations and use their time efficiently. A formal evaluation of the process was not conducted and will be adopted in the future

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future