DOCSLIB.ORG

Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects Causal Inference in Observational Studies with Complex Design: Multiple Arms, Complex Sampling and Intervention Effects DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Giovanni Nattino, M.S. Graduate Program in Biostatistics The Ohio State University 2019 Dissertation Committee: Dr. Bo Lu, Advisor Dr. Stanley Lemeshow, Co-Advisor Dr. Eloise Kaizar © Copyright by Giovanni Nattino 2019 Abstract Observational studies are major data sources to infer causal relationships. When using observational data to estimate causal effects, researchers must consider appro- priate statistical methodology to account for the non-random allocation of the units to the treatment groups. Such methodology is well-established when the research question involves two treatment groups and results do not need to be generalized to the population from which the study sample has been selected. Relatively few studies have focused on research questions that do not fit into this framework. The goal of this work is to introduce statistical methods to perform causal inference in complex designs. First, I introduce a matching design for estimating treatment effects in the presence of multiple treatment groups. I devise a novel matching algorithm, generating samples that are well-balanced with respect to pre-treatment variables, and discuss the post-matching statistical analyses. Second, I focus on the generaliza- tion of causal effects to the population level, specifically when the sample selection is based on complex survey designs. I discuss the extension of the propensity score methodology to survey data, describe a weighted estimator for the common two-stage cluster sample and study its asymptotic properties. Third, I consider the estimation of population intervention effects, which evaluate the impact of realistic changes in the distribution of the treatment in a cohort. I describe estimators for upper and lower bounds of effects of this type, highlighting the implications for policy makers. For each of these three areas of causal inference, I use Monte Carlo simulations to ii assess the reliability of the proposed methods and compare them with competing approaches. The new methods are illustrated with real-data applications. Finally, I discuss limitations and aspects requiring further work. iii Acknowledgments First of all, I would like to express my sincere gratitude to my advisors. Thanks to Dr. Stan Lemeshow, who has been the catalyst of this incredible journey. Without you, I would not be where I am now. I am grateful for your unconditional help, which often went beyond the university walls, and countless advices. An equal thanks goes to Dr. Bo Lu, who introduced me to the world of causal inference. Thank you for your guidance and trust, which simultaneously directed me to the finish line and left me space to set my own pace. Thanks for all the pragmatic suggestions and for helping me navigating the statistical conferences I have been fortunate to attend. Thanks to all the staff of the Government Resource Center, in particular to Lorin Ranbom and Colin Odden, for the continuous support and for the invaluable oppor- tunity of continuously working on the Infant Mortality Research Partnership project. A special thank you to all the researchers I was fortunate to meet within this project. Thank you \Task 4" members, especially to Dr. Pat and Steve Gabbe and Dr. Court- ney Hebert. Your enthusiasm and genuine devotion to impact on the well-being of our society have truly inspired me. I would also like to thank Dr. Henry Xiang and Dr. Junxin Shi, from Nationwide Children's Hospital, for their expert advice and the help with the trauma data, which motivated part of this work. Thanks to all the faculties and students I have met during my time at the Ohio State University. In particular, I would like to thank Dr. Elly Kaizar, for your iv valuable feedback on my work. I am grateful to Dr. Matt Pratola and Hengrui Luo and to Dr. Mike Pennell. Even though the results of our collaborations do not appear in these pages, working with you was a truly stimulating, refreshing and enjoyable experience. A special thanks also to Dr. Amy Ferketich and Dr. Mario Peruggia, for your friendly advice and for being my \Little Italy" in Columbus. I would like to thank the researchers of the Laboratory of Clinical Epidemiology at the Mario Negri Institute for Pharmacological Research, in Italy, where I developed my interests in research and in biostatistics. Thank you all, especially to Dr. Guido Bertolini, for helping me embarking on this journey. Thanks to all the friends who have been my Columbus family in these years. In particular, thank you Sebastian, Guilherme, Aziz, J´ulia,Armand, Shuyuan, Jason, Natalia, Jafar, Juli´an,Alejandro and Andreas. Thanks for all the dinners together, the Friday night gatherings, the endless barbecues, the bike rides, the rock climbing sessions, the racquetball and disc golf games. You will be missed. A special thanks to my parents, Daniela and Beppe, and my brothers, Francesco and Stefano. If I am where I am, this is because of your education, encouragement and love. Finally, a profound thank you to my fianc´ee,Melissa. You understood the impor- tance of this goal for me, despite the time together that I had to sacrifice along the way. Thanks for your patience and heartening words. I could not have asked for a better travel companion. v Vita 1987 . .Born in Lecco (LC), Italy Education 2009 . .B.S. Applied Mathematics, University of Milan, Milan, Italy 2011 . .M.S. Applied Mathematics, University of Milan, Milan, Italy 2014 . .Post-graduate certificate in Biomedical Research, Istituto di Ricerche Farma- cologiche Mario Negri IRCCS, Ranica (BG), Italy Professional Experience 2011-2015 . Research Associate, Laboratory of Clinical Epidemiology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Ranica (BG), Italy 2016-2019 . Graduate Research Associate, Divi- sion of Biostatistics, College of Pub- lic Health, The Ohio State University, Columbus, Ohio 2017-2019 . Graduate Research Associate, Ohio Colleges of Medicine Government Re- source Center, The Ohio State Univer- sity Wexner Medical Center, Colum- bus, Ohio vi Publications 1. Giovanni Nattino, Michael L Pennell, and Stanley Lemeshow. Assessing the goodness of fit of logistic regression models in large samples: a modification of the Hosmer-Lemeshow test. Submitted to Biometrics, 2019. 2. Giovanni Nattino, Bo Lu, Junxin Shi, Stanley Lemeshow, and Henry Xiang. Triplet matching for estimating causal effects with three treatment arms: a comparative study of mortality by trauma center level. Submitted to Journal of the American Statistical Association, 2019. 3. Courtney L Hebert, Giovanni Nattino, Steven G Gabbe, Patricia T Gabbe, Jason Benedict, Gary Phillips, and Stanley Lemeshow. A predictive model for very preterm birth: developing a point of care tool. Submitted to American Journal of Obstetrics and Gynecology, 2019. 4. Erinn M Hade, Giovanni Nattino, Heather A Frey, and Bo Lu. Propensity Score Matching for Treatment Delay Effects with Observational Survival Data. Submitted to Statistical Methods in Medical Research, 2019. 5. Giovanni Nattino and Bo Lu. Model assisted sensitivity analyses for hidden bias with binary outcomes. Biometrics, 74: 1141{1149, 2018. 6. Stefano Skurzak, Greta Carrara, Carlotta Rossi, Giovanni Nattino, Daniele Crespi, Michele Giardino, and Guido Bertolini. Cirrhotic patients admitted to the icu for medical reasons: Analysis of 5506 patients admitted to 286 icus in 8years. Journal of Critical Care, 45: 220{228, 2018. 7. Guido Bertolini, Giovanni Nattino, Carlo Tascini, Daniele Poole, Bruno Viaggi, Greta Carrara, Carlotta Rossi, Daniele Crespi, Matteo Mondini, Martin Langer, Gian Maria Rossolini, and Paolo Malacarne. Mortality attributable to different kleb- siella susceptibility patterns and to the coverage of empirical antibiotic therapy: a cohort study on patients admitted to the ICU with infection. Intensive Care Medicine, 44(10): 1709{1719, 2018. 8. Giovanni Nattino, Stanley Lemeshow, Gary Phillips, Stefano Finazzi, and Guido Bertolini. Assessing the calibration of dichotomous outcome models with the calibra- tion belt. Stata Journal, 17(4): 1003{1014, 2017. 9. Daniele Poole, Stefano Finazzi, Giovanni Nattino, Danilo Radrizzani, Giuseppe Gristina, Paolo Malacarne, Sergio Livigni, and Guido Bertolini. The prognostic im- portance of chronic end-stage diseases in geriatric patients admitted to 163 italian ICUs. Minerva Anestesiologica, 83: 1283{1293, 2017. vii 10. Giovanni Nattino, Stefano Finazzi, and Guido Bertolini. A new test and graphical tool to assess the goodness of fit of logistic regression models. Statistics in Medicine, 35(5): 709{720, 2016. 11. Daniele Poole, Giovanni Nattino, and Guido Bertolini. Overoptimism in the interpretation of statistics. Intensive Care Medicine, 40(12): 1927{1929, 2014. 12. Giovanni Nattino, Stefano Finazzi, and Guido Bertolini. Comments on `Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers' by Peter C. Austin and Ewout W. Steyerberg. Statistics in Medicine, 33(15): 2696{2698, 2014. 13. Giovanni Nattino, Stefano Finazzi, and Guido Bertolini. A new calibration test and a reappraisal of the calibration belt for the assessment of prediction models based on dichotomous outcomes. Statistics in Medicine, 33(14): 2390{2407, 2014. 14. Nicola Latronico, Giovanni Nattino, Bruno Guarneri,
Load more

Recommended publications
  • Choosing the Sample
    CHAPTER IV CHOOSING THE SAMPLE This chapter is written for survey coordinators and technical resource persons. It will enable you to: U Understand the basic concepts of sampling. U Calculate the required sample size for national and subnational estimates. U Determine the number of clusters to be used. U Choose a sampling scheme. UNDERSTANDING THE BASIC CONCEPTS OF SAMPLING In the context of multiple-indicator surveys, sampling is a process for selecting respondents from a population. In our case, the respondents will usually be the mothers, or caretakers, of children in each household visited,1 who will answer all of the questions in the Child Health modules. The Water and Sanitation and Salt Iodization modules refer to the whole household and may be answered by any adult. Questions in these modules are asked even where there are no children under the age of 15 years. In principle, our survey could cover all households in the population. If all mothers being interviewed could provide perfect answers, we could measure all indicators with complete accuracy. However, interviewing all mothers would be time-consuming, expensive and wasteful. It is therefore necessary to interview a sample of these women to obtain estimates of the actual indicators. The difference between the estimate and the actual indicator is called sampling error. Sampling errors are caused by the fact that a sample&and not the entire population&is surveyed. Sampling error can be minimized by taking certain precautions: 3 Choose your sample of respondents in an unbiased way. 3 Select a large enough sample for your estimates to be precise.
    [Show full text]
  • Computing Effect Sizes for Clustered Randomized Trials
    Computing Effect Sizes for Clustered Randomized Trials Terri Pigott, C2 Methods Editor & Co-Chair Professor, Loyola University Chicago [email protected] The Campbell Collaboration www.campbellcollaboration.org Computing effect sizes in clustered trials • In an experimental study, we are interested in the difference in performance between the treatment and control group • In this case, we use the standardized mean difference, given by YYTC− d = gg Control group sp mean Treatment group mean Pooled sample standard deviation Campbell Collaboration Colloquium – August 2011 www.campbellcollaboration.org 1 Variance of the standardized mean difference NNTC+ d2 Sd2 ()=+ NNTC2( N T+ N C ) where NT is the sample size for the treatment group, and NC is the sample size for the control group Campbell Collaboration Colloquium – August 2011 www.campbellcollaboration.org TREATMENT GROUP CONTROL GROUP TT T CC C YY,,..., Y YY12,,..., YC 12 NT N Overall Trt Mean Overall Cntl Mean T Y C Yg g S 2 2 Trt SCntl 2 S pooled Campbell Collaboration Colloquium – August 2011 www.campbellcollaboration.org 2 In cluster randomized trials, SMD more complex • In cluster randomized trials, we have clusters such as schools or clinics randomized to treatment and control • We have at least two means: mean performance for each cluster, and the overall group mean • We also have several components of variance – the within- cluster variance, the variance between cluster means, and the total variance • Next slide is an illustration Campbell Collaboration Colloquium – August 2011 www.campbellcollaboration.org TREATMENT GROUP CONTROL GROUP Cntl Cluster mC Trt Cluster 1 Trt Cluster mT Cntl Cluster 1 TT T T CC C C YY,...ggg Y ,..., Y YY11,..
    [Show full text]
  • Evaluating Probability Sampling Strategies for Estimating Redd Counts: an Example with Chinook Salmon (Oncorhynchus Tshawytscha)
    1814 Evaluating probability sampling strategies for estimating redd counts: an example with Chinook salmon (Oncorhynchus tshawytscha) Jean-Yves Courbois, Stephen L. Katz, Daniel J. Isaak, E. Ashley Steel, Russell F. Thurow, A. Michelle Wargo Rub, Tony Olsen, and Chris E. Jordan Abstract: Precise, unbiased estimates of population size are an essential tool for fisheries management. For a wide variety of salmonid fishes, redd counts from a sample of reaches are commonly used to monitor annual trends in abundance. Using a 9-year time series of georeferenced censuses of Chinook salmon (Oncorhynchus tshawytscha) redds from central Idaho, USA, we evaluated a wide range of common sampling strategies for estimating the total abundance of redds. We evaluated two sampling-unit sizes (200 and 1000 m reaches), three sample proportions (0.05, 0.10, and 0.29), and six sampling strat- egies (index sampling, simple random sampling, systematic sampling, stratified sampling, adaptive cluster sampling, and a spatially balanced design). We evaluated the strategies based on their accuracy (confidence interval coverage), precision (relative standard error), and cost (based on travel time). Accuracy increased with increasing number of redds, increasing sample size, and smaller sampling units. The total number of redds in the watershed and budgetary constraints influenced which strategies were most precise and effective. For years with very few redds (<0.15 reddsÁkm–1), a stratified sampling strategy and inexpensive strategies were most efficient, whereas for years with more redds (0.15–2.9 reddsÁkm–1), either of two more expensive systematic strategies were most precise. Re´sume´ : La gestion des peˆches requiert comme outils essentiels des estimations pre´cises et non fausse´es de la taille des populations.
    [Show full text]
  • Cluster Sampling
    Day 5 sampling - clustering SAMPLE POPULATION SAMPLING: IS ESTIMATING THE CHARACTERISTICS OF THE WHOLE POPULATION USING INFORMATION COLLECTED FROM A SAMPLE GROUP. The sampling process comprises several stages: •Defining the population of concern •Specifying a sampling frame, a set of items or events possible to measure •Specifying a sampling method for selecting items or events from the frame •Determining the sample size •Implementing the sampling plan •Sampling and data collecting 2 Simple random sampling 3 In a simple random sample (SRS) of a given size, all such subsets of the frame are given an equal probability. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results. SRS can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population. Systematic sampling 4 Systematic sampling (also known as interval sampling) relies on arranging the study population according to some ordering scheme and then selecting elements at regular intervals through that ordered list Systematic sampling involves a random start and then proceeds with the selection of every kth element from then onwards. In this case, k=(population size/sample size). It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the kth element in the list. STRATIFIED SAMPLING 5 WHEN THE POPULATION EMBRACES A NUMBER OF DISTINCT CATEGORIES, THE FRAME CAN BE ORGANIZED BY THESE CATEGORIES INTO SEPARATE "STRATA." EACH STRATUM IS THEN SAMPLED AS AN INDEPENDENT SUB-POPULATION, OUT OF WHICH INDIVIDUAL ELEMENTS CAN BE RANDOMLY SELECTED Cluster sampling Sometimes it is more cost-effective to select respondents in groups ('clusters') Quota sampling Minimax sampling Accidental sampling Voluntary Sampling ….
    [Show full text]
  • Introduction to Survey Sampling and Analysis Procedures (Chapter)
    SAS/STAT® 9.3 User’s Guide Introduction to Survey Sampling and Analysis Procedures (Chapter) SAS® Documentation This document is an individual chapter from SAS/STAT® 9.3 User’s Guide. The correct bibliographic citation for the complete manual is as follows: SAS Institute Inc. 2011. SAS/STAT® 9.3 User’s Guide. Cary, NC: SAS Institute Inc. Copyright © 2011, SAS Institute Inc., Cary, NC, USA All rights reserved. Produced in the United States of America. For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227-19, Commercial Computer Software-Restricted Rights (June 1987). SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513. 1st electronic book, July 2011 SAS® Publishing provides a complete selection of books and electronic products to help customers use SAS software to its fullest potential. For more information about our e-books, e-learning products, CDs, and hard-copy books, visit the SAS Publishing Web site at support.sas.com/publishing or call 1-800-727-3228.
    [Show full text]
  • Sampling and Household Listing Manual [DHSM4]
    SAMPLING AND HOUSEHOLD LISTING MANuaL Demographic and Health Surveys Methodology This document is part of the Demographic and Health Survey’s DHS Toolkit of methodology for the MEASURE DHS Phase III project, implemented from 2008-2013. This publication was produced for review by the United States Agency for International Development (USAID). It was prepared by MEASURE DHS/ICF International. [THIS PAGE IS INTENTIONALLY BLANK] Demographic and Health Survey Sampling and Household Listing Manual ICF International Calverton, Maryland USA September 2012 MEASURE DHS is a five-year project to assist institutions in collecting and analyzing data needed to plan, monitor, and evaluate population, health, and nutrition programs. MEASURE DHS is funded by the U.S. Agency for International Development (USAID). The project is implemented by ICF International in Calverton, Maryland, in partnership with the Johns Hopkins Bloomberg School of Public Health/Center for Communication Programs, the Program for Appropriate Technology in Health (PATH), Futures Institute, Camris International, and Blue Raster. The main objectives of the MEASURE DHS program are to: 1) provide improved information through appropriate data collection, analysis, and evaluation; 2) improve coordination and partnerships in data collection at the international and country levels; 3) increase host-country institutionalization of data collection capacity; 4) improve data collection and analysis tools and methodologies; and 5) improve the dissemination and utilization of data. For information about the Demographic and Health Surveys (DHS) program, write to DHS, ICF International, 11785 Beltsville Drive, Suite 300, Calverton, MD 20705, U.S.A. (Telephone: 301-572- 0200; fax: 301-572-0999; e-mail: [email protected]; Internet: http://www.measuredhs.com).
    [Show full text]
  • Taylor's Power Law and Fixed-Precision Sampling
    87 ARTICLE Taylor’s power law and fixed-precision sampling: application to abundance of fish sampled by gillnets in an African lake Meng Xu, Jeppe Kolding, and Joel E. Cohen Abstract: Taylor’s power law (TPL) describes the variance of population abundance as a power-law function of the mean abundance for a single or a group of species. Using consistently sampled long-term (1958–2001) multimesh capture data of Lake Kariba in Africa, we showed that TPL robustly described the relationship between the temporal mean and the temporal variance of the captured fish assemblage abundance (regardless of species), separately when abundance was measured by numbers of individuals and by aggregate weight. The strong correlation between the mean of abundance and the variance of abundance was not altered after adding other abiotic or biotic variables into the TPL model. We analytically connected the parameters of TPL when abundance was measured separately by the aggregate weight and by the aggregate number, using a weight–number scaling relationship. We utilized TPL to find the number of samples required for fixed-precision sampling and compared the number of samples when sampling was performed with a single gillnet mesh size and with multiple mesh sizes. These results facilitate optimizing the sampling design to estimate fish assemblage abundance with specified precision, as needed in stock management and conservation. Résumé : La loi de puissance de Taylor (LPT) décrit la variance de l’abondance d’une population comme étant une fonction de puissance de l’abondance moyenne pour une espèce ou un groupe d’espèces. En utilisant des données de capture sur une longue période (1958–2001) obtenues de manière cohérente avec des filets de mailles variées dans le lac Kariba en Afrique, nous avons démontré que la LPT décrit de manière robuste la relation entre la moyenne temporelle et la variance temporelle de l’abondance de l’assemblage de poissons capturés (peu importe l’espèce), que l’abondance soit mesurée sur la base du nombre d’individus ou de la masse cumulative.
    [Show full text]
  • METHOD GUIDE 3 Survey Sampling and Administration
    METHOD GUIDE 3 Survey sampling and administration Alexandre Barbosa, Marcelo Pitta, Fabio Senne and Maria Eugênia Sózio Regional Center for Studies on the Development of the Information Society (Cetic.br), Brazil November 2016 1 Table of Contents Global Kids Online ......................................................................................................................... 3 Abstract .......................................................................................................................................... 4 Key issues ...................................................................................................................................... 5 Main approaches and identifying good practice ......................................................................... 7 Survey frame and sources of information .................................................................................................................... 8 Methods of data collection ................................................................................................................................................ 8 Choosing an appropriate method of data collection.......................................................................................................... 9 Sampling plan.................................................................................................................................................................. 11 Target population .......................................................................................................................................................
    [Show full text]
  • Use of Sampling in the Census Technical Session 5.2
    Regional Workshop on the Operational Guidelines of the WCA 2020 Dar es Salaam, Tanzania 23-27 March 2020 Use of sampling in the census Technical Session 5.2 Eloi OUEDRAOGO Statistician, Agricultural Census Team FAO Statistics Division (ESS) 1 CONTENTS Complete enumeration censuses versus sample-based censuses Uses of sampling at other census stages Sample designs based on list frames Sample designs based on area frames Sample designs based on multiple frames Choice of sample design Country examples 2 Background As mentioned in the WCA 2020, Vol. 1 (Chapter 4, paragraph 4.34), when deciding whether to conduct a census by complete or sample enumeration, in addition to efficiency considerations (precision versus costs), one should take into account: desired level of aggregation for census data; use of the census as a frame for ongoing sampling surveys; data content of the census; and capacity to deal with sampling methods and subsequent statistical analysis based on samples. 3 Complete enumeration versus sample enumeration census Complete enumeration Sample enumeration Advantages 1. Reliable census results for the 1. Is generally less costly that a smallest administrative and complete enumeration geographic units and on rare events 2. Contributes to decrease the overall (such as crops/livestock types) response burden 2. Provides a reliable frame for the organization of subsequent regular 3. Requires a smaller number of infra-annual and annual sample enumerators and supervisors than a surveys. In terms of frames, it is census conducted by complete much less demanding in respect of enumeration. Consequently, the the holdings’ characteristics non-sampling errors can be 3.
    [Show full text]
  • How Big of a Problem Is Analytic Error in Secondary Analyses of Survey Data?
    RESEARCH ARTICLE How Big of a Problem is Analytic Error in Secondary Analyses of Survey Data? Brady T. West1*, Joseph W. Sakshaug2,3, Guy Alain S. Aurelien4 1 Survey Research Center, Institute for Social Research, University of Michigan-Ann Arbor, Ann Arbor, Michigan, United States of America, 2 Cathie Marsh Institute for Social Research, University of Manchester, Manchester, England, 3 Institute for Employment Research, Nuremberg, Germany, 4 Joint Program in Survey Methodology, University of Maryland-College Park, College Park, Maryland, United States of America * [email protected] Abstract a11111 Secondary analyses of survey data collected from large probability samples of persons or establishments further scientific progress in many fields. The complex design features of these samples improve data collection efficiency, but also require analysts to account for these features when conducting analysis. Unfortunately, many secondary analysts from fields outside of statistics, biostatistics, and survey methodology do not have adequate OPEN ACCESS training in this area, and as a result may apply incorrect statistical methods when analyzing these survey data sets. This in turn could lead to the publication of incorrect inferences Citation: West BT, Sakshaug JW, Aurelien GAS (2016) How Big of a Problem is Analytic Error in based on the survey data that effectively negate the resources dedicated to these surveys. Secondary Analyses of Survey Data? PLoS ONE 11 In this article, we build on the results of a preliminary meta-analysis of 100 peer-reviewed (6): e0158120. doi:10.1371/journal.pone.0158120 journal articles presenting analyses of data from a variety of national health surveys, which Editor: Lourens J Waldorp, University of Amsterdam, suggested that analytic errors may be extremely prevalent in these types of investigations.
    [Show full text]
  • Guidelines for Evaluating Prevalence Studies
    Evid Based Mental Health: first published as 10.1136/ebmh.1.2.37 on 1 May 1998. Downloaded from EBMH NOTEBOOK Guidelines for evaluating prevalence studies As stated in the first issue of Evidence-Based Mental Health, we are planning to widen the scope of the journal to include studies answering additional types of clinical questions. One of our first priorities has been to develop criteria for studies providing information about the prevalence of psychiatric disorders, both in the population and in specific clinical settings. We invited the following editorial from Dr Michael Boyle to highlight the key methodological issues involved in the critical appraisal of prevalence studies. The next stage is to develop valid and reliable criteria for selecting prevalence studies for inclusion in the jour- nal. We welcome our readers contribution to this process. You are a geriatric psychiatrist providing consultation and lation must be defined by shared characteristics assessed and care to elderly residents living in several nursing homes. measured accurately. Some of these characteristics include The previous 3 patients referred to you have met criteria age, sex, language, ethnicity, income, and residency. Invari- for depression, and you are beginning to wonder if the ably, subsets of the target population are too expensive or prevalence of this disorder is high enough to warrant screen- difficult to enlist because, for example, they live in places that ing. Alternatively, you are a child youth worker on a clinical are inaccessible to surveys (eg, remote areas, native reserves, service for disruptive behaviour disorders. It seems that all of military bases, shelters) or they speak languages not accom- the children being treated by the team come from economi- modated by data collection.
    [Show full text]
  • Accounting for Cluster Sampling in Constructing Enumerative Sequential Sampling Plans
    SAMPLING AND BIOSTATISTICS Accounting for Cluster Sampling in Constructing Enumerative Sequential Sampling Plans 1 2 A. J. HAMILTON AND G. HEPWORTH J. Econ. Entomol. 97(3): 1132Ð1136(2004) ABSTRACT GreenÕs sequential sampling plan is widely used in applied entomology. GreenÕs equa- tion can be used to construct sampling stop charts, and a crop can then be surveyed using a simple random sampling (SRS) approach. In practice, however, crops are rarely surveyed according to SRS. Rather, some type of hierarchical design is usually used, such as cluster sampling, where sampling units form distinct groups. This article explains how to make adjustments to sampling plans that intend to use cluster sampling, a commonly used hierarchical design, rather than SRS. The methodologies are illustrated using diamondback moth, Plutella xylostella (L.), a pest of Brassica crops, as an example. Downloaded from KEY WORDS cluster sampling, design effect, enumerative sampling, Plutella xylostella, sequential sampling http://jee.oxfordjournals.org/ WHEN CONSIDERING THE DISTRIBUTION of individuals in a it is more practicable to implement. Therefore, the population, the relationship between the population sampling design needs to be taken into account when variance and mean typically obeys a power law (Tay- constructing a sequential sampling plan. Most pub- lor 1961). According to TaylorÕs Power Law (TPL), lished enumerative plans do not do this, and thus the variance-mean relation can be described by the implicitly assume that the Þeld will be sampled ac- following equation: cording to SRS (e.g., Allsopp et al. 1992, Smith and Hepworth 1992, Cho et al. 1995). 2 ϭ ៮b s ax [1] In this article, we show how to account for cluster where s2 and x៮ are the sample variance and sample sampling (Fig.
    [Show full text]