Using learning profiles to inform education priorities: An editors' overview of the Special Issue.

This special issue explores the use of learning profiles for analysing the dynamics of low learning in low- and middle-income countries and informing priorities to address the learning crisis. The 12 papers in the special issue draw on learning data from more than 50 countries and 6 million individuals, with implications for education policy and practice. Taken together, they point to a need to steepen learning trajectories by prioritizing early mastery of foundational skills for all children. The papers show that addressing the learning crisis will not be achieved through more school grade attainment alone, nor through within-country equality across groups (such as girls and boys or rich and poor). Positive examples show that programs focused on foundational learning both improved average learning and reduced inequality. Addressing the learning crisis will require a focus on systems improvement, using foundational learning as a case in point for making the needed systems improvements to steepen learning throughout children's time in school. Learning profiles can provide a guide for education actors aiming to improve learning outcomes.

Looking beyond changes in averages in evaluating foundational learning: Some inequality measures.

This paper uses measurements of learning inequality to explore whether learning interventions that are aimed at improving means also reduce inequality, and if so, under what conditions. There is abundant evidence that learning levels are generally low in low- and middle-income countries (LMIC), but there is less knowledge about how learning achievement is distributed within these contexts, and especially about how these distributions change as mean levels increase. We use child-level data on foundational literacy outcomes to quantitatively explore whether and how learning inequality using metrics borrowed from the economics and inequality literature can help us understand the impact of learning interventions. The paper deepens recent work in several ways. First, it extends the analysis to six LMIC, displaying which measures are computable and coherent across contexts and baseline levels. This extension can add valuable information to program evaluation, without being redundant with other metrics. Second, we show the large extent to which the disaggregation of inequality of foundational skills between- and within-schools and grades varies by context and language. Third, we present initial empirical evidence that, at least in the contexts of analysis of foundational interventions, improving average performance can reduce inequality as well, across all levels of socioeconomic status (SES). The data show that at baseline, the groups with the highest internal inequality tend to be the groups with lowest SES and lowest reading scores, as inequality among the poor themselves is higher than among their wealthier counterparts. Regardless of which SES groups benefit more in terms of a change in mean levels of reading, there is still a considerable reduction in inequality by baseline achievement as means increase. These results have policy implications in terms of targeting of interventions: much can be achieved in terms of simultaneously improving averages and increasing equality. This seems particularly true when the initial learning levels are as low as they currently are the developing world.

Estimating a Treatment Effect in Residual Time Quantiles under the Additive Hazards Model.

For randomized clinical trials where the endpoint of interest is a time-to-event subject to censoring, estimating the treatment effect has mostly focused on the hazard ratio from the Cox proportional hazards model. Since the model's proportional hazards assumption is not always satisfied, a useful alternative, the so-called additive hazards model, may instead be used to estimate a treatment effect on the difference of hazard functions. Still, the hazards difference may be difficult to grasp intuitively, particularly in a clinical setting of, e.g., patient counseling, or resource planning. In this paper, we study the quantiles of a covariate's conditional survival function in the additive hazards model. Specifically, we estimate the residual time quantiles, i.e., the quantiles of survival times remaining at a given time t, conditional on the survival times greater than t, for a specific covariate in the additive hazards model. We use the estimates to translates the hazards difference into the difference in residual time quantiles, which allows a more direct clinical interpretation. We determine the asymptotic properties, assess the performance via Monte-Carlo simulations, and demonstrate the use of residual time quantiles in two real randomized clinical trials.

Controlling the family-wise error rate in multi-arm, multi-stage trials.

BACKGROUND AND AIMS: Multi-arm, multi-stage trials have recently gained attention as a means to improve the efficiency of the clinical trials process. Many designs have been proposed, but few explicitly consider the inherent issue of multiplicity and the associated type I error rate inflation. It is our aim to propose a straightforward design that controls family-wise error rate while still providing improved efficiency. METHODS: In this article, we provide an analytical method for calculating the family-wise error rate for a multi-arm, multi-stage trial and highlight the potential for considerable error rate inflation in uncontrolled designs. We propose a simple method to control the error rate that also allows for computation of power and expected sample size. RESULTS: Family-wise error rate can be controlled in a variety of multi-arm, mutli-stage trial designs using our method. Additionally, our design can substantially decrease the expected sample size of a study while maintaining adequate power. CONCLUSION: Multi-arm, multi-stage designs have the potential to reduce the time and other resources spent on clinical trials. Our relatively simple design allows this to be achieved while weakly controlling family-wise error rate and without sacrificing much power.

On estimation of covariate-specific residual time quantiles under the proportional hazards model.

Estimation and inference in time-to-event analysis typically focus on hazard functions and their ratios under the Cox proportional hazards model. These hazard functions, while popular in the statistical literature, are not always easily or intuitively communicated in clinical practice, such as in the settings of patient counseling or resource planning. Expressing and comparing quantiles of event times may allow for easier understanding. In this article we focus on residual time, i.e., the remaining time-to-event at an arbitrary time t given that the event has yet to occur by t. In particular, we develop estimation and inference procedures for covariate-specific quantiles of the residual time under the Cox model. Our methods and theory are assessed by simulations, and demonstrated in analysis of two real data sets.

Internal structure and standardized scores of the Spanish adaptation of the EGRA (Early Grade Reading Assessment) for early reading assessment / Estructura interna y baremación de la adaptación española del EGRA (Early Grade Reading Assessment) para la evaluación temprana de la lectura

BACKGROUND: The EGRA (Early Grade Reading Assessment) is an assessment tool containing the main predictors of reading learning disabilities based on the National Reading Panel (NRP) (National Institute of Child Health and Human Development, 2000). This study has two main objectives: First, to analyze the internal structure of the EGRA, and second, to examine the validity and normative data for first and second grade primary school students in a Spanish-Speaking population. METHOD: This study had a sample of 400 children (196 female and 204 male) attending early grades of Primary School, between 6 and 8 years of age. RESULTS: Our findings indicate that the EGRA has acceptable psychometric properties and an internal structure that is based on the two main factors of «decoding and comprehension» and «oral comprehension». CONCLUSIONS: We believe that the normative data collected from this study may be useful for the early detection of «at risk» Spanish children having reading disabilities, as well as for planning of early reading education

ANTECEDENTES: EGRA (Early Grade Reading Assessment) es un instrumento para la evaluación temprana de la lectura e incluye los principales componentes que según el National Reading Panel (NRP) (National Institute of Child Health and Human Development, 2000) predicen las dificultades de aprendizaje en lectura. El principal objetivo de este trabajo ha sido, por una parte, estudiar la estructura interna del EGRA y, por otra, analizar su validez y establecer datos normativos para 1º y 2º curso de Educación Primaria en población escolar española. MÉTODO: se seleccionó una muestra de 400 alumnos (196 niñas y 204 niños) pertenecientes al primer ciclo de Educación Primaria, cuyas edades oscilaban entre 6 y 8 años. RESULTADOS: los datos apuntan a que el EGRA reúne las características métricas exigibles a este tipo de pruebas y que su estructura interna responde a dos factores principales como sería la «descodificación y comprensión» y la «comprensión oral». CONCLUSIONES: los datos normativos obtenidos podrían ser utilizados para la detección temprana de niños españoles con riesgo de presentar dificultades de aprendizaje en la lectura, y también para la planificación de la enseñanza de la lectura en los primeros niveles de la escolaridad

Internal structure and standardized scores of the Spanish adaptation of the EGRA (Early Grade Reading Assessment) for early reading assessment.

BACKGROUND: The EGRA (Early Grade Reading Assessment) is an assessment tool containing the main predictors of reading learning disabilities based on the National Reading Panel (NRP) (National Institute of Child Health and Human Development, 2000). This study has two main objectives: First, to analyze the internal structure of the EGRA, and second, to examine the validity and normative data for first and second grade primary school students in a Spanish-Speaking population. METHOD: This study had a sample of 400 children (196 female and 204 male) attending early grades of Primary School, between 6 and 8 years of age. RESULTS: Our findings indicate that the EGRA has acceptable psychometric properties and an internal structure that is based on the two main factors of "decoding and comprehension" and "oral comprehension". CONCLUSIONS: We believe that the normative data collected from this study may be useful for the early detection of 'at risk' Spanish children having reading disabilities, as well as for planning of early reading education.

A model for the design and construction of a resource for the validation of prognostic prostate cancer biomarkers: the Canary Prostate Cancer Tissue Microarray.

Tissue microarrays (TMAs) provide unique resources for rapid evaluation and validation of tissue biomarkers. The Canary Foundation Retrospective Prostate Tissue Microarray Resource used a rigorous statistical design, quota sampling, a variation of the case-cohort study, to select patients for inclusion in a multicenter, retrospective prostate cancer TMA cohort. The study is designed to definitively validate tissue biomarkers of prostate cancer recurrence after radical prostatectomy. Tissue samples from over 1000 participants treated for prostate cancer with radical prostatectomy between 1995 and 2004 were selected at 6 participating institutions in the United States and Canada. This design captured the heterogeneity of screening and clinical practices in the contemporary North American population. Standardized clinical data were collected in a centralized database. The project has been informative in several respects. The scale and complexity of assembling TMAs with over 200 cases at each of 6 sites involved unanticipated levels of effort and time. Our statistical design promises to provide a model for outcome-based studies where tissue localization methods are applied to high-density TMAs.

Urinary lead exposure and breast cancer risk in a population-based case-control study.

BACKGROUND: Lead is a toxic nonessential metal with widespread exposure starting in utero. Lead has been reclassified in 2004 by the International Agency for Research on Cancer Working Group from a "possible" to a "probable" human carcinogen. Lead may be a facilitative or permissive carcinogen, which means that lead may permit or augment the genotoxic effects of other exposures. METHODS: This population-based study in Wisconsin gathered survey data and home-collected urine specimens from 246 women, ages 20 to 69 years, with incident invasive breast cancer identified from the Wisconsin state registry and 254 age-matched control subjects from population lists from September 2004 to February 2005. We measured urinary lead concentrations by inductively coupled plasma mass spectrometry, adjusted the values by specific gravity, and conducted interviews by telephone to obtain information on known and suspected breast cancer risk factors. RESULTS: Women in the highest quartile of specific gravity-adjusted lead level (>/=1.10 mug/L) had twice the breast cancer risk of those in the lowest quartile (<0.42 mug/L; odds ratio, 1.99; 95% confidence interval, 1.1-3.6) after adjustment for established risk factors. Excluding women who were currently taking nonsteroidal aromatase inhibitors (n = 52), we did not observe any increased breast cancer risk after adjustment for established risk factors. CONCLUSION: Our population-based case-control study suggests that lead exposure, as determined by specific gravity-adjusted urinary lead concentrations, is not associated with a significant increased risk for breast cancer.