Moving from Forecast to Prediction: How Honors Programs Can Use Easily Accessible Predictive Analytics to Improve Enrollment Management

Most enrollment management systems today use historical data to build rough forecasts of what percentage of students will likely accept an offer of enrollment based on historical acceptance rates. While this aggregate forecast method has its uses, we propose that building an enrollment model based on predicting an individual’s likelihood of matriculation can be much more beneficial to an honors director than a historical aggregate forecast. Many complex predictive analytics techniques and specialized software can build such models, but here we show that a basic approach can also be easily accessible to honors directors where a small amount of data collection and basic spreadsheet software allow them to capture most of the benefits without needing the skills of a data scientist. The first step comes in understanding the difference between a forecast and a prediction. A forecast is an estimate of a future event, generally in aggregate form. For example, today I might forecast that our ice cream store will likely sell 1,000 scoops of ice cream based on weather, time of year, day of the week, and regional events—all useful information for staffing and inventory management as well as profitability analysis. Historically, an honors administrator might use this approach to predict the total number of students matriculating to the university or to an individual program. However, with predictive analytics one can acquire even more detail that could be useful in a setting like an honors program where not just the total number of “customers” matter but which ones will create a well-rounded, diverse honors program with students from multiple backgrounds (Siegel). In the ice cream case, a predictive analytics example might predict not just how many total ice cream scoops might be sold but how likely each individual is to buy ice cream. Deeper analysis might predict the type of ice cream, time of day customers might come, and how frequently they might visit the store. Predictive analytics might also lead to prescriptive analytics, where you learn what might be done to persuade someone who was not planning to buy ice cream to do so, e.g., what it might take to change a consumer’s mind so that she will buy ice cream today or how we can we get her to buy two scoops instead of one or to bring a friend. This type of predictive and prescriptive analytics has helped many organizations improve their efficiency and effectiveness (Siegel), and we believe that honors directors can also use it. In this approach, each potential honors student would receive an individualized probability score reflecting his or her likelihood of accepting an offer of admission. This score could still be aggregated into a direct forecast of how many students would likely attend, but it would also show the likelihood that any individual student would attend. The scores could predict how many from a certain group (e.g., science majors or Hispanic students) are likely to attend. This information could help strategically determine scholarship offers as well as the staff’s time commitments to recruitment and follow-up activities

Analyzing the Impacts of Public Policy on COVID-19 Transmission: A Case Study of the Role of Model and Dataset Selection Using Data from Indiana

Dynamic estimation of the reproduction number of COVID-19 is important for assessing the impact of public health measures on virus transmission. State and local decisions about whether to relax or strengthen mitigation measures are being made in part based on whether the reproduction number, Rt , falls below the self-sustaining value of 1. Employing branching point process models and COVID-19 data from Indiana as a case study, we show that estimates of the current value of Rt , and whether it is above or below 1, depend critically on choices about data selection and model specification and estimation. In particular, we find a range of Rt values from 0.47 to 1.20 as we vary the type of estimator and input dataset. We present methods for model comparison and evaluation and then discuss the policy implications of our findings

Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis

Governments have implemented social distancing measures to address the ongoing COVID-19 pandemic. The measures include instructions that individuals maintain social distance when in public, school closures, limitations on gatherings and business operations, and instructions to remain at home. Social distancing may have an impact on the volume and distribution of crime. Crimes such as residential burglary may decrease as a byproduct of increased guardianship over personal space and property. Crimes such as domestic violence may increase because of extended periods of contact between potential offenders and victims. Understanding the impact of social distancing on crime is critical for ensuring the safety of police and government capacity to deal with the evolving crisis. Understanding how social distancing policies impact crime may also provide insights into whether people are complying with public health measures. Examination of the most recently available data from both Los Angeles, CA, and Indianapolis, IN, shows that social distancing has had a statistically significant impact on a few specific crime types. However, the overall effect is notably less than might be expected given the scale of the disruption to social and economic life

The challenges of modeling and forecasting the spread of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies

Poly(ADP-Ribose) Polymerase Inhibition: "Targeted" Therapy for Triple-Negative Breast Cancer

In contrast to endocrine-sensitive and HER2-positive breast cancer, novel agents capable of treating advanced triple negative breast cancer (TNBC) are lacking. PARP (Poly-(adenosine diphosphate [ADP]-ribose) polymerase) inhibitors are emerging as one of the most promising ‘targeted’ therapeutics to treat TNBC, with the intended ‘target’ being DNA repair. PARP's are a family of enzymes involved in multiple cellular processes including DNA repair. TNBC shares multiple clinico-pathologic features with BRCA-mutated breast cancers which harbor dysfunctional DNA repair mechanisms. Investigators hypothesized PARP inhibition, in conjunction with the loss of DNA-repair via BRCA-dependent mechanisms, would result in synthetic lethality and augmented cell death. This hypothesis has borne out in both preclinical models and in clinical trials testing PARP inhibitors in both BRCA-deficient and TNBC. The focus of this review will include an overview of the preclinical rationale for evaluating PARP inhibitors in TNBC, the presumed mechanism of action of this novel therapeutic class, promising results from several influential clinical trials of PARP inhibition in advanced breast cancer (both TNBC and BRCA-deficient), proposed mechanisms of acquired resistance to PARP inhibitors, and, finally, conclude with current challenges and future directions for the development of PARP inhibitors in the treatment of breast cancer

Case report: Androgen-secreting adrenocortical tumors in eight cats

Urine marking, aggression, and other behavioral concerns are common reasons for cat owners to seek veterinary care. Empiric treatment for lower urinary tract disease or primary behavior disorders are commonly pursued, especially in those cases with normal routine laboratory evaluations. Herein, we report the clinicopathologic findings in eight sexually altered cats that were diagnosed with androgen-secreting adrenocortical tumors. Nearly all cats (n = 7) initially were evaluated for inappropriate urination and pungent urine, with additional behavioral concerns including aggression (n = 3) and excess vocalization (n = 4) commonly reported. Penile barbs (n = 5) were identified in all five male cats, and an enlarged clitoris was observed in one female cat. Testing of serum androgen concentrations revealed abnormally high androstenedione (n = 1) or testosterone (n = 7) concentrations. In the five cases with available adrenal tissue, histopathologic evaluation identified either an adrenocortical adenoma (n = 3) or adrenocortical carcinoma (n = 2). Hormonal abnormalities resolved and clinical signs improved in the four cats that underwent surgical adrenalectomy, with each of these cats surviving >1 year. However, clinical signs were minimally impacted with medical treatments, including one cat in which trilostane treatment failed to improve clinical signs or testosterone concentrations. This collection of cases underscores the importance of a detailed physical examination as well as the consideration of endocrine disturbances in cats undergoing evaluation for inappropriate urination or aggression. Furthermore, this report adds to the growing body of evidence that sex-hormone secreting adrenal tumors in cats may be an under-recognized syndrome

Clinical and Genomic Risk for Late Breast Cancer Recurrence and Survival.

BACKGROUND: The 21-gene recurrence score (RS) assay (Oncotype DX) is used to guide adjuvant chemotherapy use for patients with hormone receptor-positive, HER2 (human epidermal growth factor receptor 2)-negative, axillary node-negative breast cancer. Its role, however, in providing prognostic information for late distant recurrence when added to clinicopathologic prognostic factors is unknown. METHODS: A patient-specific meta-analysis including 10,004 women enrolled in three trials was updated using extended follow-up data from TAILORx, integrating the RS with histologic grade, tumor size, and age at surgery for the RSClin tool. Cox models integrating clinicopathologic factors and the RS were compared by using likelihood ratio (LR) tests. External validation of prognosis for distant recurrence in years 0 to 10 and 5 to 10 was performed in an independent cohort of 1098 women in a real-world registry. RESULTS: RSClin provided significantly more prognostic information than either the clinicopathologic factors (ΔLR chi-square, 86.2; P<0.001) or RS alone (ΔLR chi-square, 131.0; P<0.001). The model was prognostic in an independent cohort for distant recurrence by 10 years after diagnosis (standardized hazard ratio, 1.56; 95% confidence interval, 1.25 to 1.94), was associated with late distant recurrence risk between 5 and 10 years after diagnosis (standardized hazard ratio, 1.78; 95% confidence interval, 1.25 to 2.55), and approximated the observed 10-year distant recurrence risk (Lin concordance, 0.87) and 5- to 10-year distant recurrence risk (Lin concordance, 0.92). CONCLUSIONS: The 21-gene RS is prognostic for distant recurrence and overall survival in early breast cancer. A model integrating the 21-gene RS and clinicopathologic factors improved estimates of distant recurrence risk compared with either used individually and stratified late distant recurrence risk. (Funded by the National Cancer Institute, National Institutes of Health [U10CA180820, U10CA180794, UG1CA189859, U10CA180868, and U10CA180822] and others.)

Race, Ethnicity, and Clinical Outcomes in Hormone Receptor-Positive, HER2-Negative, Node-Negative Breast Cancer in the Randomized TAILORx Trial

BACKGROUND: Black race is associated with worse outcomes in early breast cancer. We evaluated clinicopathologic characteristics, the 21-gene recurrence score (RS), treatment delivered, and clinical outcomes by race and ethnicity among women who participated in the Trial Assigning Individualized Options for Treatment. METHODS: The association between clinical outcomes and race (White, Black, Asian, other or unknown) and ethnicity (Hispanic vs non-Hispanic) was examined using proportional hazards models. All P values are 2-sided. RESULTS: Of 9719 eligible women with hormone receptor-positive, HER2-negative, node-negative breast cancer, there were 8189 (84.3%) Whites, 693 (7.1%) Blacks, 405 (4.2%) Asians, and 432 (4.4%) with other or unknown race. Regarding ethnicity, 889 (9.1%) were Hispanic. There were no substantial differences in RS or ESR1, PGR, or HER2 RNA expression by race or ethnicity. After adjustment for other covariates, compared with White race, Black race was associated with higher distant recurrence rates (hazard ratio [HR] = 1.60, 95% confidence intervals [CI] = 1.07 to 2.41) and worse overall survival in the RS 11-25 cohort (HR = 1.51, 95% CI = 1.06 to 2.15) and entire population (HR = 1.41, 95% CI = 1.05 to 1.90). Hispanic ethnicity and Asian race were associated with better outcomes. There was no evidence of chemotherapy benefit for any racial or ethnic group in those with a RS of 11-25. CONCLUSIONS: Black women had worse clinical outcomes despite similar 21-gene assay RS results and comparable systemic therapy in the Trial Assigning Individualized Options for Treatment. Similar to Whites, Black women did not benefit from adjuvant chemotherapy if the 21-gene RS was 11-25. Further research is required to elucidate the basis for this racial disparity in prognosis

Clinical and Genomic Risk to Guide the Use of Adjuvant Therapy for Breast Cancer

BACKGROUND The use of adjuvant chemotherapy in patients with breast cancer may be guided by clinicopathological factors and a score based on a 21-gene assay to determine the risk of recurrence. Whether the level of clinical risk of breast cancer recurrence adds prognostic information to the recurrence score is not known. METHODS We performed a prospective trial involving 9427 women with hormone-receptor–positive, human epidermal growth factor receptor 2–negative, axillary node–negative breast cancer, in whom an assay of 21 genes had been performed, and we classified the clinical risk of recurrence of breast cancer as low or high on the basis of the tumor size and histologic grade. The effect of clinical risk was evaluated by calculating hazard ratios for distant recurrence with the use of Cox proportional-hazards models. The initial endocrine therapy was tamoxifen alone in the majority of the premenopausal women who were 50 years of age or younger. RESULTS The level of clinical risk was prognostic of distant recurrence in women with an intermediate 21-gene recurrence score of 11 to 25 (on a scale of 0 to 100, with higher scores indicating a worse prognosis or a greater potential benefit from chemotherapy) who were randomly assigned to endocrine therapy (hazard ratio for the comparison of high vs. low clinical risk, 2.73; 95% confidence interval [CI], 1.93 to 3.87) or to chemotherapy plus endocrine (chemoendocrine) therapy (hazard ratio, 2.41; 95% CI, 1.66 to 3.48) and in women with a high recurrence score (a score of 26 to 100), all of whom were assigned to chemoendocrine therapy (hazard ratio, 3.17; 95% CI, 1.94 to 5.19). Among women who were 50 years of age or younger who had received endocrine therapy alone, the estimated (±SE) rate of distant recurrence at 9 years was less than 5% (≤1.8±0.9%) with a low recurrence score (a score of 0 to 10), irrespective of clinical risk, and 4.7±1.0% with an intermediate recurrence score and low clinical risk. In this age group, the estimated distant recurrence at 9 years exceeded 10% among women with a high clinical risk and an intermediate recurrence score who received endocrine therapy alone (12.3±2.4%) and among those with a high recurrence score who received chemoendocrine therapy (15.2±3.3%). CONCLUSIONS Clinical-risk stratification provided prognostic information that, when added to the 21-gene recurrence score, could be used to identify premenopausal women who could benefit from more effective therapy