Experimental Design and Ethics

Marketing Research Methods in SAS Experimental Design, Choice, Conjoint, and Graphical Techniques Warren F. Kuhfeld October 1, 2010 SAS 9.2 Edition MR-2010 Copyright c  2010 by SAS Institute Inc., Cary, NC, USA This information is provided by SAS as a service to its users. The te xt, macros, and code are provided “as is.” There are no warranties, expressed or implied, as to merchantability or fitness for a particular purpose regarding the accuracy of the materials or code contained herein. SAS r  , SAS/AF r  , SAS/ETS r  , SAS/GRAPH r  , SAS/IML r  , SAS/QC r  , and SAS/STAT r  are trade- marks or registered trademarks of SAS in the USA and other countries. r  indicates USA registration. Contents Overview Marketing Research: Uncovering Competitive Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . 27–40 This chapter is based on a SUGI (SAS Users Group International) paper and provides a basic intro- duction to perceptual mapping, biplots, multidimensional preference analysis (MDPREF), preference mapping (PREFMAP or external unfolding), correspondence analysis, multidimensional scaling, and conjoint analysis. Introducing the Market Research Analysis Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41–52 This SUGI paper discusses a point-and-click interface for conjoint analysis, correspondence analysis, and multidimensional scaling. Experimental Design: Efficiency, Coding, and Choice Designs . . . . . . . . . . . . . . . . . . . . . 53–241 This chapter discusses experimental design including full-factorial designs, fractional-factorial designs, orthogonal arrays, nonorthogonal designs, choice designs, conjoint designs, design efficiency, orthogon- ality, balance, and co ding. If you are interested in choice modeling, read this chapter first. Efficient Exp eri mental Design with Marketing Research Applications . . . . . . . . . . . 243–265 This chapter is based on a Journal of Marketing Research paper and disc usse s D-efficient experimental designs for conjoint and discrete-choice studies, orthogonal arrays, nonorthogonal designs, relative efficiency, and nonorthogonal design algorithms. A General Method for Constructing Efficient Choice Designs . . . . . . . . . . . . . . . . . . . . 265–283 This chapter discusses efficient designs for choice experiments. Discrete Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285–663 This chapter discusses the multinomial logit model and discrete choice experiments. This is the longest chapter in the book, and it contains numerous examples covering a wide range of choice experiments and choice designs. Study the chapter Experimental Design: Effici ency, Coding, and Choice Designs before tackling this chapter. Multinomial Logit Mo del s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665–680 This SUGI paper discusses the multinomial logit model. A travel example is discussed. Conjoint Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681–801 This chapter discusses conjoint analysis. Examples range from simple to complicated. Topics include design, data collection, analysis, and simulation. PROC TRANSREG documentation that describes just those options that are most likely to be used in a conjoint analysis is included. The Macros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803–1211 This chapter provides e xamples and documentation for all of the autocall macros used Experimental Design and Ethics Experimental Design and Ethics By: OpenStaxCollege Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments In this module, you will learn important aspects of experimental design Proper study design ensures the production of reliable, accurate data The purpose of an experiment is to investigate the relationship between two variables When one variable causes change in another, we call the first variable the explanatory variable The affected variable is called the response variable In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable The different values of the explanatory variable are called treatments An experimental unit is a single object or individual to be measured You want to investigate the effectiveness of vitamin E in preventing disease You recruit a group of subjects and ask them if they regularly take vitamin E You notice that the subjects who take vitamin E exhibit better health on average than those who not Does this prove that vitamin E is effective in disease prevention? It does not There are many differences between the two groups compared in addition to vitamin E consumption People who take vitamin E regularly often take other steps to improve their health: exercise, diet, other vitamin supplements, choosing not to smoke Any one of these factors could be influencing health As described, this study does not prove that vitamin E is the key to disease prevention Additional variables that can cloud a study are called lurking variables In order to prove that the explanatory variable is causing a change in the response variable, it is necessary to isolate the explanatory variable The researcher must design her experiment in such a way that there is only one difference between groups being compared: the planned treatments This is accomplished by the random assignment of experimental units to treatment groups When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups At this point the only difference 1/12 Experimental Design and Ethics between groups is the one imposed by the researcher Different outcomes measured in the response variable, therefore, must be a direct result of the different treatments In this way, an experiment can prove a cause-and-effect connection between the explanatory and response variables The power of suggestion can have an important influence on the outcome of an experiment Studies have shown that the expectation of the study participant can be as important as the actual medication In one study of performance-enhancing drugs, researchers noted: Results showed that believing one had taken the substance resulted in [performance] times almost as fast as those associated with consuming the drug itself In contrast, taking the drug without knowledge yielded no significant performance increment McClung, M Collins, D “Because I know it will!”: placebo effects of an ergogenic aid on athletic performance Journal of Sport & Exercise Psychology 2007 Jun 29(3):382-94 Web April 30, 2013 When participation in a study prompts a physical response from a participant, it is difficult to isolate the effects of the explanatory variable To counter the power of suggestion, researchers set aside one treatment group as a control group This group is given a placebo treatment–a treatment that cannot influence the response variable The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments Of course, if you are participating in a study and you know that you are receiving a pill which contains no actual medication, then the power of suggestion is no longer a factor Blinding in a randomized experiment preserves the power of suggestion When a person involved in a research study is blinded, he does not know who is receiving the active treatment(s) and who is receiving the placebo treatment A double-blind experiment is one in which both the subjects and the researchers involved with the subjects are blinded Researchers want to investigate whether taking aspirin regularly reduces the risk of heart attack Four hundred men between the ages of 50 and 84 are recruited as participants The men are divided randomly into two groups: one group will take aspirin, and the other group will take a placebo Each man takes one pill each day for three years, but he does not know whether he is taking aspirin or the placebo At the end of the study, researchers count the number of men in each group who have had heart attacks Identify the following values for this study: population, sample, experimental units, explanatory variable, response variable, treatments The population is men aged ...MINIREVIEW Systems biology: experimental design Clemens Kreutz and Jens Timmer Physics Department, University of Freiburg, Germany Introduction The development of new experimental techniques allowing for quantitative measurements and the pro- ceeding level of knowledge in cell biology allows the application of mathematical modeling approaches for testing and validation of hypotheses and for the prediction of new phenomena. This approach is the promising idea of systems biology. Along with the rising relevance of mathematical modeling, the importance of experimental design issues increases. The term ‘experimental design’ or ‘design of experiments’ (DoE) refers to the process of planning the experiments in a way that allows for an efficient statistical inference. A proper experimen- tal design enables a maximum informative analysis of the experimental data, whereas an improper design cannot be compensated by sophisticated anal- ysis methods. Learning by experimentation is an iterative process [1]. Prior knowledge about a system based on literature and/or preliminary tests is used for planning. Improve- ment of the knowledge based on first results is followed by the design and execution of new experi- ments, which are used to refine such knowledge (Fig. 1A). During the process of planning, this sequen- tial character has to be kept in mind. It is more effi- cient to adapt designs to new insights than to plan a single, large and comprehensive experiment. Moreover, it is recommended to spend only a limited amount of the available resources (e.g. 25% [2]) in the first experi- mental iteration to ensure that enough resources are available for confirmation runs. Experimental design considerations require that the hypotheses under investigation and the scope of the study are stated clearly. Moreover, the methods intended to be applied in the analysis have to be speci- fied [3]. The dependency on the analysis is one reason Keywords confounding; experimental design; mathematical modeling; model discrimination; Monte Carlo method; parameter estimation; sampling; systems biology Correspondence C. Kreutz, Physics Department, University of Freiburg, 79104 Freiburg, Germany Fax: +49 761 203 5754 Tel: +49 761 203 8533 E-mail: ckreutz@fdm.uni-freiburg.de (Received 8 April 2008, revised 13 August 2008, accepted 11 September 2008) doi:10.1111/j.1742-4658.2008.06843.x Experimental design has a long tradition in statistics, engineering and life sciences, dating back to the beginning of the last century when optimal designs for industrial and agricultural trials were considered. In cell biol- ogy, the use of mathematical modeling approaches raises new demands on experimental planning. A maximum informative investigation of the dynamic behavior of cellular systems is achieved by an optimal combina- tion of stimulations and observations over time. In this minireview, the existing approaches concerning this optimization for parameter estimation and model discrimination are summarized. Furthermore, the relevant clas- sical aspects of experimental design, such as randomization, replication and confounding, are reviewed. Abbreviation AIC, Akaike Information Criterion. FEBS Journal 276 (2009) 923–942 ª 2009 The Authors Journal compilation ª 2009 FEBS 923 for the wide range of experimental design methodolo- gies in statistics. In this minireview, we provide theoreticians with a starting point into the experimental design issues that are relevant for systems biological approaches. For the experimentalists, the minireview should give a deeper insight into the requirements of the experimental data that should be used for mathematical modeling. The aspects of experimental planning discussed here are shown in Fig. 1B. One of the main aspects Genome Biology 2007, 8:R44 comment reviews reports deposited research refereed research interactions information Open Access 2007Dabney and StoreyVolume 8, Issue 3, Article R44 Method Normalization of two-channel microarrays accounting for experimental design and intensity-dependent relationships Alan R Dabney * and John D Storey † Addresses: * Department of Statistics, Texas A&M University, College Station, TX 77843, USA. † Department of Biostatistics and Department of Genome Sciences, University of Washington, WA 98195, USA. Correspondence: John D Storey. Email: jstorey@washington.edu © 2007 Dabney and Storey; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Normalization of two-channel microarrays<p>eCADS is a new method for multiple array normalization of two-channel microarrays that takes into account general experimental designs and intensity-dependent relationships and allows for a more efficient dye-swap design that requires only one array per sample pair.</p> Abstract In normalizing two-channel expression arrays, the ANOVA approach explicitly incorporates the experimental design in its model, and the MA plot-based approach accounts for intensity- dependent biases. However, both approaches can lead to inaccurate normalization in fairly common scenarios. We propose a method called efficient Common Array Dye Swap (eCADS) for normalizing two-channel microarrays that accounts for both experimental design and intensity- dependent biases. Under reasonable experimental designs, eCADS preserves differential expression relationships and requires only a single array per sample pair. Background The two-channel microarray continues to be an important platform for characterizing genomewide expression levels. For example, a two-channel array technology using inkjet printing techniques was recently introduced by Agilent Labo- ratories (Palo Alto, California) that combines some of the favorable properties of single-channel oligonucleotide arrays and two-channel cDNA arrays. A recent paper compared one- channel and two-channel platforms, and concluded that the two approaches are basically equivalent in terms of reproduc- ibility, sensitivity, and specificity [1]. In comparison with the single-channel platform, then, the two-channel platform basically doubles the number of comparisons that can be made between two groups using a fixed number of arrays, when the efficient dye-swap design proposed here is employed. As with all high throughput array-based technologies, it is necessary to preprocess two-channel gene expression arrays to account for systematic biases [2-7]. In particular, there is evidence of dye bias, or systematic differences between the incorporation rates of the fluorescent dyes used for labeling targets. There may also be systematic differences between expression measurements on the same sample but different arrays, representing array effects. Other sources of bias include spatial trends on arrays and so-called batch effects. In order to make reliable conclusions based on these data, it is necessary to take into account all sources of signal, both biological and systematic. Early work carried out preprocess- ing and statistical inference simultaneously [8]. BioMed Central Page 1 of 10 (page number not for citation purposes) Health and Quality of Life Outcomes Open Access Research Public telesurveillance service for frail elderly living at home, outcomes and cost evolution: a quasi experimental design with two follow-ups Claude Vincent* †1,2 , Daniel Reinharz †1,3 , Isabelle Deaudelin †2 , Mathieu Garceau †2 and Lise R Talbot 4 Address: 1 Department of rehabilitation, Laval University, Pavillon Ferdinand-Vandry, Quebec City (Quebec), G1K7P4, Canada, 2 Center of Interdisciplinary Research in Rehabilitation & Social Integration (CIRRIS), Quebec City, Institut de réadaptation en déficience physique de Québec, 525 bvld Wilfrid-Hamel east, Quebec City, Quebec, G1M 2S8, Canada, 3 Department of Preventive and Social medicine, Laval University, Pavillon de l'est, Québec City (Quebec), G1K 7P4, Canada and 4 Department of Nursing, Faculty of Medicine and Health Sciences, Sherbrooke University, 3001, 12thavenue, Sherbrooke (Quebec), Canada Email: Claude Vincent* - claude.vincent@rea.ulaval.ca; Daniel Reinharz - Daniel.reinharz@msp.ulaval.ca; Isabelle Deaudelin - isabelle.deaudelin@rea.ulaval.ca; Mathieu Garceau - salutgoglu@hotmail.com; Lise R Talbot - lise.talbot@USherbrooke.ca * Corresponding author †Equal contributors Abstract Background: Telesurveillance is a technologically based modality that allows the surveillance of patients in the natural setting, mainly home. It is based on communication technologies to relay information between a patient and a central call center where services are coordinated. Different types of telesurveillance systems have been implemented, some being staffed with non-health professionals and others with health professional, mainly nurses. Up to now, only telesurveillance services staffed with non-health professionals have been shown to be effective and efficient. The objective of this study was to document outcomes and cost evolution of a nurse-staffed telesurveillance system for frail elderly living at home. Methods: A quasi experimental design over a nine-month period was done. Patients (n = 38) and caregivers (n = 38) were selected by health professionals from two local community health centers. To be eligible, elders had to be over 65, live at home with a permanent physical, slight cognitive or motor disability or both and have a close relative (the caregiver) willing to participate to the study. These disabilities had to hinder the accomplishment of daily life activities deemed essential to continue living at home safely. Three data sources were used: patient files, telesurveillance center's quarterly reports and personal questionnaires (Modified Mini-Mental State, Functional Autonomy Measurement System, Life Event Checklist, SF-12, Life-H, Quebec User Evaluation of Satisfaction with Assistive Technology, Caregiver Burden). The telesurveillance technology permitted, among various functionalities, bi-directional communication (speaker- receiver) between the patient and the response center. Results: A total of 957 calls for 38 registered clients over a 6-month period was recorded. Only 48 (5.0%) of the calls were health-related. No change was reported in the elders' quality of life and daily activity abilities. Satisfaction was very high. Caregivers' psychological burden decreased substantially. On a 3 months period, length of hospital stays dropped from 13 to 4 days, and home care services decreased from 18 to 10 Genome Biology 2007, 8:R44 comment reviews reports deposited research refereed research interactions information Open Access 2007Dabney and StoreyVolume 8, Issue 3, Article R44 Method Normalization of two-channel microarrays accounting for experimental design and intensity-dependent relationships Alan R Dabney * and John D Storey † Addresses: * Department of Statistics, Texas A&M University, College Station, TX 77843, USA. † Department of Biostatistics and Department of Genome Sciences, University of Washington, WA 98195, USA. Correspondence: John D Storey. Email: jstorey@washington.edu © 2007 Dabney and Storey; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Normalization of two-channel microarrays<p>eCADS is a new method for multiple array normalization of two-channel microarrays that takes into account general experimental designs and intensity-dependent relationships and allows for a more efficient dye-swap design that requires only one array per sample pair.</p> Abstract In normalizing two-channel expression arrays, the ANOVA approach explicitly incorporates the experimental design in its model, and the MA plot-based approach accounts for intensity- dependent biases. However, both approaches can lead to inaccurate normalization in fairly common scenarios. We propose a method called efficient Common Array Dye Swap (eCADS) for normalizing two-channel microarrays that accounts for both experimental design and intensity- dependent biases. Under reasonable experimental designs, eCADS preserves differential expression relationships and requires only a single array per sample pair. Background The two-channel microarray continues to be an important platform for characterizing genomewide expression levels. For example, a two-channel array technology using inkjet printing techniques was recently introduced by Agilent Labo- ratories (Palo Alto, California) that combines some of the favorable properties of single-channel oligonucleotide arrays and two-channel cDNA arrays. A recent paper compared one- channel and two-channel platforms, and concluded that the two approaches are basically equivalent in terms of reproduc- ibility, sensitivity, and specificity [1]. In comparison with the single-channel platform, then, the two-channel platform basically doubles the number of comparisons that can be made between two groups using a fixed number of arrays, when the efficient dye-swap design proposed here is employed. As with all high throughput array-based technologies, it is necessary to preprocess two-channel gene expression arrays to account for systematic biases [2-7]. In particular, there is evidence of dye bias, or systematic differences between the incorporation rates of the fluorescent dyes used for labeling targets. There may also be systematic differences between expression measurements on the same sample but different arrays, representing array effects. Other sources of bias include spatial trends on arrays and so-called batch effects. In order to make reliable conclusions based on these data, it is necessary to take into account all sources of signal, both biological and systematic. Early work carried out preprocess- ing and statistical inference simultaneously [8]. However, it has become common practice to carry out 'normalization' as a preprocessing step, adjusting the raw expression profiles so that all systematic biases have been removed and carrying out all subsequent analyses without consideration for the pre- processing [9,10]. A standard normalization method involves smoothing so- called MA plots [2-4]. An MA plot compares differential expression to overall intensity. MA methods such as lowess smoothing of MA plots remove any observed ... see the brand of juice as samples are poured for a taste test Twenty-five percent of participants prefer Brand X, 33% prefer Brand Y and 42% have no preference between the two brands Brand X references... 4/12 Experimental Design and Ethics truth,” he said He described his behavior as an addiction that drove him to carry out acts of increasingly daring fraud, like a junkie seeking a bigger and. .. on her route She does not record the addresses and does not return at a later time to try to find residents at home 6/12 Experimental Design and Ethics She skips four houses on her route because