scholarly journals Generalizing randomized trial findings to a target population using complex survey population data

2020 ◽  
Author(s):  
Benjamin Ackerman ◽  
Catherine R. Lesko ◽  
Juned Siddique ◽  
Ryoko Susukida ◽  
Elizabeth A. Stuart
Keyword(s):  
Randomized Trial ◽  
Population Data ◽  
Target Population ◽  
Complex Survey ◽  
Survey Population

scholarly journals Generalizing Treatment Effect Estimates From Sample to Population: A Case Study in the Difficulties of Finding Sufficient Data

2016 ◽  
Vol 41 (4) ◽  
pp. 357-388 ◽  
Author(s):  
Elizabeth A. Stuart ◽  
Anna Rhodes
Keyword(s):  
Head Start ◽  
Randomized Trial ◽  
Treatment Effect ◽  
External Validity ◽  
Randomized Trials ◽  
Target Population ◽  
Data Sets ◽  
Common Data Elements ◽  
Ex Post

Background: Given increasing concerns about the relevance of research to policy and practice, there is growing interest in assessing and enhancing the external validity of randomized trials: determining how useful a given randomized trial is for informing a policy question for a specific target population. Objectives: This article highlights recent advances in assessing and enhancing external validity, with a focus on the data needed to make ex post statistical adjustments to enhance the applicability of experimental findings to populations potentially different from their study sample. Research design: We use a case study to illustrate how to generalize treatment effect estimates from a randomized trial sample to a target population, in particular comparing the sample of children in a randomized trial of a supplemental program for Head Start centers (the Research-Based, Developmentally Informed study) to the national population of children eligible for Head Start, as represented in the Head Start Impact Study. Results: For this case study, common data elements between the trial sample and population were limited, making reliable generalization from the trial sample to the population challenging. Conclusions: To answer important questions about external validity, more publicly available data are needed. In addition, future studies should make an effort to collect measures similar to those in other data sets. Measure comparability between population data sets and randomized trials that use samples of convenience will greatly enhance the range of research and policy relevant questions that can be answered.

scholarly journals Consequences of Promoting Less-Populated Rural Areas to Urban Areas: A Case Study, Bushehr Province

2015 ◽  
Vol 10 (1) ◽  
pp. 91-100
Author(s):  
Ali Bastin
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Residential Buildings ◽  
Urban System ◽  
Population Data ◽  
Target Population ◽  
Positive Outcomes ◽  
Negative Consequences

The modified law of Iranian Administrative divisions has greatly altered the pattern of settlement in recent decades. The promotion of rural areas to urban areas has shifted from mere population standard to combined population-administrative standards. However, all censuses suggest that many rural areas reported as smaller than the minimum population standard have been promoted to urban areas. In the last two decades, this is a clearly prominent phenomenon in the urban system of Iran. This paper evaluates the effects and consequences of promoting small and sparsely populated rural areas to urban areas in the Bushehr province. The used methodology is analytic-descriptive using a questionnaire distributed among 380 members of the target population. Data analysis is conducted in physical, economic, social and urban servicing domains using one-sample T-test and the utility range. The results show that promotion of rural areas to urban areas has positive outcomes such as improved waste disposal system, improved quality of residential buildings, increased monitoring of the construction, increased income, prevented migration and improved health services. However, the results of utility range show that the negative consequences of this policy are more than its positive outcomes, which have been studied in detail.

Extending inferences from a randomized trial to a target population

2019 ◽  
Vol 34 (8) ◽  
pp. 719-722 ◽  
Author(s):  
Issa J. Dahabreh ◽  
Miguel A. Hernán
Keyword(s):  
Randomized Trial ◽  
Target Population

scholarly journals ASSESSMENT OF FINANCIAL MANAGEMENT CHALLENGES FACING THE MICRO AND SMALL ENTERPRISES (MSES) IN KENYA: A CASE OF MERCHANDIZING MSES LOCATED IN NAIROBI RIVERROAD AREA

2017 ◽  
Vol 2 (1) ◽  
pp. 1-22
Author(s):  
Amos M. Tayari ◽  
Ms. Esther Nkatha
Keyword(s):  
Inventory Management ◽  
Financial Management ◽  
Trade Credit ◽  
Population Data ◽  
Management Training ◽  
Target Population ◽  
Small Enterprises ◽  
Sampling Frame ◽  
Set Up ◽  
Descriptive Survey

Purpose:  This study was an assessment of the financial management challenges facing MSEs in Kenya in the case of merchandizing MSEs located along River Road.Methodology: A descriptive survey research design was adopted. The target population was all the 210 MSEs located along river Road in Nairobi.  The study used systematic random sampling. All the MSEs were numbered and included in the sampling frame.  One MSE out of every five MSE was picked at random thus resulting to a sample size of 42 which was 20% of the population. Data was collected using a questionnaire, analyzed by use of descriptive statistics and findings presented using charts and graphs.Results: Findings in this study indicated that the financial management challenges facing MSEs were in the area of trade credit management, inventory management, debtors’ management and cash management. It was concluded that indeed MSEs were facing a serious challenge in financial management.Unique contribution to theory, practice and policy: It was recommended in this study that business incubation projects should be set up to impart financial knowledge to MSE owners. It was suggested that a correlation or a regression analysis should be carried out as an area of further study in order to ascertain the influence of financial management training and MSE success/growth. Such a study would inform the formulation of government policy aimed at promoting the growth of the MSE sector

scholarly journals Dynamic Ecological Inference for Time-Varying Population Distributions Based on Sparse, Irregular, and Noisy Marginal Data

2019 ◽  
Vol 27 (3) ◽  
pp. 388-396 ◽  
Author(s):  
Devin Caughey ◽  
Mallory Wang
Keyword(s):  
Population Distribution ◽  
Auxiliary Information ◽  
Population Data ◽  
Target Population ◽  
Small Subset ◽  
Ecological Inference ◽  
Joint Distributions ◽  
Marginal Data ◽  
Time Periods ◽  
Over Time

Social scientists are frequently interested in how populations evolve over time. Creating poststratification weights for surveys, for example, requires information on the weighting variables’ joint distribution in the target population. Typically, however, population data are sparsely available across time periods. Even when population data are observed, the content and structure of the data—which variables are observed and whether their marginal or joint distributions are known—differ across time, in ways that preclude straightforward interpolation. As a consequence, survey weights are often based only on the small subset of auxiliary variables whose joint population distribution is observed regularly over time, and thus fail to take full advantage of auxiliary information. To address this problem, we develop a dynamic Bayesian ecological inference model for estimating multivariate categorical distributions from sparse, irregular, and noisy data on their marginal (or partially joint) distributions. Our approach combines (1) a Dirichlet sampling model for the observed margins conditional on the unobserved cell proportions; (2) a set of equations encoding the logical relationships among different population quantities; and (3) a Dirichlet transition model for the period-specific proportions that pools information across time periods. We illustrate this method by estimating annual U.S. phone-ownership rates by race and region based on population data irregularly available between 1930 and 1960. This approach may be useful in a wide variety of contexts where scholars wish to make dynamic ecological inferences about interior cells from marginal data. A new R package estsubpop implements the method.

Trends of utilization of free maternal services implemented among postnatal women in sub-county hospitals of selected counties in Nyanza, Kenya

2020 ◽  
Vol 7 (10) ◽  
pp. 4086
Author(s):  
Jackline Mosinya Nyaberi ◽  
Otieno G. Ochieng ◽  
Osero O. S. Justus
Keyword(s):  
Population Data ◽  
Simple Random Sampling ◽  
Target Population ◽  
Skilled Birth Attendance ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Financial Barriers ◽  
Maternal Deaths ◽  
Birth Attendance

Background: Low utilization and poor accessibility of hospital based maternal services in low and middle-income countries (LMIC) are evident and financial barriers is a major bottleneck. Globally, an estimated 600,000 maternal deaths occur yearly with over 90% of these deaths occurring in LMICs. In Kenya, maternal mortality is still relatively high with 362 maternal deaths per 100,000 live births. Gaps in the quality of maternal health services exist due to high costs, poor staffing and inaccessibility. However, Utilization of hospital based maternal services enhances skilled delivery and consequently reduces maternal and child mortalities and morbidities. The aim of this study was to establish the trends of utilization of free maternal services (FMS) before and after implementation in counties of Nyanza, Kenya.Methods: The study adopted an analytical cross-sectional study utilizing mixed methods of data collection. Secondary quantitative data on the rate of utilization between June 2011 and May 2015 was compared. Qualitative data was collected from key informants and focused group discussants. Purposive and simple random sampling were used to select target population. Data was analysed using both parametric and non-parametric statistical methods.Results: In maternal services utilization, Kisumu county recorded the highest 98.7%. Overall, in Nyanza, there was tremendous growth on trends of FMS utilization of 53.4% from 36.7% before implementation of FMS with cases of still births, maternal deaths and neonatal deaths.Conclusion: The upsurge of FMS utilization encouraged skilled birth attendance but also caused enormous constrains to health system and reduced the quality of FMS.

scholarly journals Study Designs for Extending Causal Inferences from a Randomized Trial to a Target Population

2020 ◽  
Author(s):  
Issa J Dahabreh ◽  
Sebastien J P A Haneuse ◽  
James M Robins ◽  
Sarah E Robertson ◽  
Ashley L Buchanan ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Study Design ◽  
Target Population ◽  
Inverse Probability Weighting ◽  
Trial Participation ◽  
Probability Weighting ◽  
Causal Inferences ◽  
Inverse Probability ◽  
Study Designs

Abstract We examine study designs for extending (generalizing or transporting) causal inferences from a randomized trial to a target population. Specifically, we consider nested trial designs, where randomized individuals are nested within a sample from the targetpopulation, and non-nested trial designs, including composite dataset designs, where a randomized trial is combined with a separately obtained sample of non-randomized individuals from the target population. We show that the counterfactual quantities that can be identified in each study design depend on what is known about the probability of sampling non-randomized individuals. For each study design, we examine identification of counterfactual outcome means via the g-formula and inverse probability weighting. Last, we explore the implications of the sampling properties underlying the designs for the identification and estimation of the probability of trial participation.

scholarly journals Generalizability of Randomized Trial Results to Target Populations

2017 ◽  
Vol 28 (5) ◽  
pp. 532-537 ◽  
Author(s):  
Elizabeth A. Stuart ◽  
Benjamin Ackerman ◽  
Daniel Westreich
Keyword(s):  
Social Work ◽  
Randomized Trial ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Internal Validity ◽  
Target Population ◽  
Average Treatment Effect ◽  
Social Work Research ◽  
Average Treatment ◽  
Social Work Program

Randomized trials play an important role in estimating the effect of a policy or social work program in a given population. While most trial designs benefit from strong internal validity, they often lack external validity, or generalizability, to the target population of interest. In other words, one can obtain an unbiased estimate of the study sample average treatment effect from a randomized trial; however, this estimate may not equal the target population average treatment effect if the study sample is not fully representative of the target population. This article provides an overview of existing strategies to assess and improve upon the generalizability of randomized trials, both through statistical methods and study design, as well as recommendations on how to implement these ideas in social work research.

scholarly journals Brazilian Portuguese version of the CORE-OM: cross-cultural adaptation of an instrument to assess the efficacy and effectiveness of psychotherapy

2015 ◽  
Vol 37 (4) ◽  
pp. 227-231 ◽  
Author(s):  
Márcia Rosane Moreira Santana ◽  
Marília Marques da Silva ◽  
Danielle Souza de Moraes ◽  
Cláudia Cristina Fukuda ◽  
Lucia Helena Freitas ◽  
...  
Keyword(s):  
Semantic Analysis ◽  
Outcome Measurement ◽  
Population Data ◽  
Target Population ◽  
Brazilian Portuguese ◽  
Core System ◽  
Semantic Equivalence ◽  
The Core ◽  
Back Translation ◽  
Mental Health Treatments

Introduction: The Clinical Outcome in Routine Evaluation - Outcome Measurement (CORE-OM) was developed in the 1990s, with the aim of assessing the efficacy and effectiveness of mental health treatments. Objective: To adapt the CORE-OM for use in the Brazilian population. Method: The instrument was translated and adapted based on the international protocol developed by the CORE System Trust which contains seven steps: translation, semantic equivalence analysis, synthesis of the translated versions, pre-testing in the target population, data analysis and back translation. Results: After semantic analysis, modifications were necessary in seven of the 34 original items. Changes were made to avoid repetition of words and the use of terms difficult to understand. Internal consistency analysis showed evidence of score stability in the CORE-OM adapted to Brazilian Portuguese. Conclusion: The instrument was successfully adapted to Brazilian Portuguese, and its semantic and conceptual properties were equivalent to those of the original instrument.

scholarly journals Design-Based Approach for Analysing Survey Data in Veterinary Research

2021 ◽  
Vol 8 (6) ◽  
pp. 105
Author(s):  
D. Aaron Yang ◽  
Richard A. Laven
Keyword(s):  
Survey Data ◽  
Random Sampling ◽  
Sampling Methods ◽  
Survey Design ◽  
Target Population ◽  
Cluster Sampling ◽  
Valuable Insight ◽  
Complex Survey ◽  
Model Based ◽  
Veterinary Research

Sample surveys are an essential approach used in veterinary research and investigation. A sample obtained from a well-designed sampling process along with robust data analysis can provide valuable insight into the attributes of the target population. Two approaches, design-based or model-based, can be used as inferential frameworks for analysing survey data. Compared to the model-based approach, the design-based approach is usually more straightforward and directly makes inferences about the finite target population (such as the dairy cows in a herd or dogs in a region) rather than an infinite superpopulation. In this paper, the concept of probability sampling and the design-based approach is briefly reviewed, followed by a discussion of the estimations and their justifications in the context of several different elementary sampling methods, including simple random sampling, stratified random sampling, and one-stage cluster sampling. Finally, a concrete example of a complex survey design (involving multistage sampling and stratification) is demonstrated, illustrating how finding unbiased estimators and their corresponding variance formulas for a complex survey builds on the techniques used in elementary sampling methods.

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