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University of Maine Deploys @M2SYS Palm Vein Scanners for Student Check-In at Dining Halls
Today we announced that the University of Maine has deployed the M2SYS palm vein scanner with our Hybrid Bio Snap-On™ Platform and Hybrid Bio-Hyperpliance Multi-Biometric Identification Matching Server software for student check-in at their on-campus dining halls. The goal for the deployment is to use the palm vein scanner to more effectively track dining hall traffic and eliminate shared cards where students share their meal plans at a price of $4,100 per academic year.
University of Maine officials chose the comprehensive M2SYS Hybrid Bio-SnapOn™ solution because of the fact that it instantly interfaced with their dining hall lunch line point-of-service (POS) software without any code level development on their part. In addition, due to the large size of their student enrollment database and their decision to deploy the palm vein scanner as their biometric hardware of choice, the University also chose to deploy Bio-Hyperpliance™ which is a scalable, hyper-threaded multi-biometric matching server designed to increase matching speeds and boost convenience for large scale deployments.
We are starting to see more and more Universities using the M2SYS palm vein scanner and other biometric identification (finger vein, fingerprint) on their campuses to help increase efficiency, reduce expenses and establish more accountability in areas like employee workforce management and student dining hall lunch line POS. As more Universities across the country catch on to the benefits of using biometrics for identification, we expect this growth to continue.
For a copy of the news release please click here.

subset: commoncrawl


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Here are a few important updates relating to Eric Dollard and his recent work…And thank you to the Eric Dollard Fan Club – your donations have helped to carry the lab's operations through some tough times – especially over the last 4-5 months! And thank you to all those who send donations directly to Eric and through PayPal. Without all of your generous support, EPD Laboratories could not function.
On Sunday, March 22nd, we're releasing the long anticipated book by Eric Dollard called Versor Algebra as Applied to Polyphase Power Systems.
Nikola Tesla developed the polyphase power system but originally it was four phases. Charles Proteus Steinmetz was hired by General Electrical to break Tesla's patent and to analyze it mathematically, which he was the first to do with Versor Algebra. But Steinmetz adapted Tesla's four phase system to a three phase system, which is still in use today. Conventional mathematics were not suitable for analyzing this three phase system without Dr. Fortescue's Symmetrical Coordinate method.
Although this made it possible to analyze any number of phases, there has never been a theoretical basis presented for this method until Eric Dollard created it – this book is actually Part 2 to this book and lecture from the 2013 Energy Science & Technology Conference called Four Quadrant Representation of Electricity. He also simplified the method of Symmetrical Coordinates beyond anything that has been done in engineering history while completely preserving it's functionality.
This book shows the methods that not only apply to power systems, but can actually be used to analyze ANYTHING that uses multiphases such as the music of Bach, which has multiple phases overlayed on top of each other. This math is also known as Sequence Algebra and we are only at the tip of the iceberg with what Eric made tangible for so many more engineers, those who study music and more.
There is an interview with Eric where he gives a great outline of what this book is about and why it is important and it is put into a YouTube video – this will be available on the homepage for the new book. We'll send a link to this on Sunday.
Please make sure to support Eric Dollard's work by purchasing a copy this coming Sunday – 70% of the sales goes to EPD Laboratories Inc. to help make the building payment (no longer rent since they're the official buyers now!), utilities and other building related costs.
On Wednesday, April 1st, we're launching a new fundraising campaign to help EPD Laboratories, Inc., which is a 501(c)(3) non-profit organization.
GOOD NEWS! What many people don't know is that Eric Dollard recently paid almost $50,000 to David Wittekind to help pay off his debt for the building. This money was provided by a private donor. Thank you J.!!!
EPD Laboratories, Inc. is officially the new buyer and for the organization to own the building outright, there is only a balance of $29,000 left! Paying off this debt will relieve a large monthly financial obligation, which will allow more rapid progress since the money can go to parts and services necessary for the work at the lab, the Advanced Seismic Warning System, etc.
It is important to remember – the Advanced Seismic Warning System is only ONE application of this long lines antenna project – Eric will be able to do many Tesla related experiments with this setup.
The other funds that are needed are for shelving so that Eric can start disassembling a lot of the electronics and organizing them, 1 year of operating capital for the building (utilities, taxes and insurance), a used "Lines Truck" for digging holes and planting telephone poles needed for the end of the seismic line (they're getting this truck for a fraction of what it is worth), an inexpensive water well drilling apparatus that will be used for for digging 30′ deep holes for the ground rods needed by each telephone pole for the seismic project (a few thousand dollars), enough parts for Eric and John Polakowski to build all the analog networks that plugs into the lines on the poles, gas money so Steve Hilsz ("The Glommeister" and director of EPD Laboratories) can come to Spokane to pick up a literal ton of Glom donated by Mark McKay (Spokane1 of Energetic Forum), etc.
We'll be sending a link soon…please support EPD Laboratories by donating to this campaign once it is launched on April 1st and please pass it on to your friends.
Imaginarium Labs is making a series of short videos to help build awareness for Eric Dollard's work. They're fans of Eric and want to do what they can to support him. Please check out this video and give it a thumbs up to show your support:
Eric's presentation is THE POWER OF THE AETHER AS RELATED TO MUSIC AND ELECTRICITY and is scheduled for 3 hours, but could run much longer.


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There is considerable debate as to the relative merits of using randomised controlled trial (RCT) data as opposed to observational data in systematic reviews of adverse effects. This meta-analysis of meta-analyses aimed to assess the level of agreement or disagreement in the estimates of harm derived from meta-analysis of RCTs as compared to meta-analysis of observational studies.
Methods and Findings
Searches were carried out in ten databases in addition to reference checking, contacting experts, citation searches, and hand-searching key journals, conference proceedings, and Web sites. Studies were included where a pooled relative measure of an adverse effect (odds ratio or risk ratio) from RCTs could be directly compared, using the ratio of odds ratios, with the pooled estimate for the same adverse effect arising from observational studies. Nineteen studies, yielding 58 meta-analyses, were identified for inclusion. The pooled ratio of odds ratios of RCTs compared to observational studies was estimated to be 1.03 (95% confidence interval 0.93–1.15). There was less discrepancy with larger studies. The symmetric funnel plot suggests that there is no consistent difference between risk estimates from meta-analysis of RCT data and those from meta-analysis of observational studies. In almost all instances, the estimates of harm from meta-analyses of the different study designs had 95% confidence intervals that overlapped (54/58, 93%). In terms of statistical significance, in nearly two-thirds (37/58, 64%), the results agreed (both studies showing a significant increase or significant decrease or both showing no significant difference). In only one meta-analysis about one adverse effect was there opposing statistical significance.
Empirical evidence from this overview indicates that there is no difference on average in the risk estimate of adverse effects of an intervention derived from meta-analyses of RCTs and meta-analyses of observational studies. This suggests that systematic reviews of adverse effects should not be restricted to specific study types.
Citation: Golder S, Loke YK, Bland M (2011) Meta-analyses of Adverse Effects Data Derived from Randomised Controlled Trials as Compared to Observational Studies: Methodological Overview. PLoS Med 8(5): e1001026. https://doi.org/10.1371/journal.pmed.1001026
Academic Editor: Jan P. Vandenbroucke, Leiden University Hospital, The Netherlands
Received: October 6, 2010; Accepted: March 15, 2011; Published: May 3, 2011
Copyright: © 2011 Golder et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was undertaken by Su Golder as part of an MRC fellowship. The views expressed in this presentation are those of the authors and not necessarily those of the MRC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The Academic Editor, Jan P. Vandenbroucke, has disclosed that he has worked with authors SG and YKL on another project unrelated to the study reported in this paper. He also discloses an intellectual competing interest in that he has previously published the theoretical viewpoint that estimates of harms outcomes in observational studies may be as valid as results of randomized trials, and may be more generalizable. The authors have no competing interests to declare.
Abbreviations: CI, confidence interval; DARE, Database of Abstracts of Reviews of Effects; HRT, hormone replacement therapy; OR, odds ratio; RCT, randomised controlled trial; ROR, ratio of odds ratios; RR, risk ratio; SE, standard error; VTE, venous thromboembolism
Editors' Summary
Whenever patients consult a doctor, they expect the treatments they receive to be effective and to have minimal adverse effects (side effects). To ensure that this is the case, all treatments now undergo exhaustive clinical research—carefully designed investigations that test new treatments and therapies in people. Clinical investigations fall into two main groups—randomized controlled trials (RCTs) and observational, or non-randomized, studies. In RCTs, groups of patients with a specific disease or condition are randomly assigned to receive the new treatment or a control treatment, and the outcomes (for example, improvements in health and the occurrence of specific adverse effects) of the two groups of patients are compared. Because the patients are randomly chosen, differences in outcomes between the two groups are likely to be treatment-related. In observational studies, patients who are receiving a specific treatment are enrolled and outcomes in this group are compared to those in a similar group of untreated patients. Because the patient groups are not randomly chosen, differences in outcomes between cases and controls may be the result of a hidden shared characteristic among the cases rather than treatment-related (so-called confounding variables).
Why Was This Study Done?
Although data from individual trials and studies are valuable, much more information about a potential new treatment can be obtained by systematically reviewing all the evidence and then doing a meta-analysis (so-called evidence-based medicine). A systematic review uses predefined criteria to identify all the research on a treatment; meta-analysis is a statistical method for combining the results of several studies to yield "pooled estimates" of the treatment effect (the efficacy of a treatment) and the risk of harm. Treatment effect estimates can differ between RCTs and observational studies, but what about adverse effect estimates? Can different study designs provide a consistent picture of the risk of harm, or are the results from different study designs so disparate that it would be meaningless to combine them in a single review? In this methodological overview, which comprises a systematic review and meta-analyses, the researchers assess the level of agreement in the estimates of harm derived from meta-analysis of RCTs with estimates derived from meta-analysis of observational studies.
What Did the Researchers Do and Find?
The researchers searched literature databases and reference lists, consulted experts, and hand-searched various other sources for studies in which the pooled estimate of an adverse effect from RCTs could be directly compared to the pooled estimate for the same adverse effect from observational studies. They identified 19 studies that together covered 58 separate adverse effects. In almost all instances, the estimates of harm obtained from meta-analyses of RCTs and observational studies had overlapping 95% confidence intervals. That is, in statistical terms, the estimates of harm were similar. Moreover, in nearly two-thirds of cases, there was agreement between RCTs and observational studies about whether a treatment caused a significant increase in adverse effects, a significant decrease, or no significant change (a significant change is one unlikely to have occurred by chance). Finally, the researchers used meta-analysis to calculate that the pooled ratio of the odds ratios (a statistical measurement of risk) of RCTs compared to observational studies was 1.03. This figure suggests that there was no consistent difference between risk estimates obtained from meta-analysis of RCT data and those obtained from meta-analysis of observational study data.
What Do These Findings Mean?
The findings of this methodological overview suggest that there is no difference on average in the risk estimate of an intervention's adverse effects obtained from meta-analyses of RCTs and from meta-analyses of observational studies. Although limited by some aspects of its design, this overview has several important implications for the conduct of systematic reviews of adverse effects. In particular, it suggests that, rather than limiting systematic reviews to certain study designs, it might be better to evaluate a broad range of studies. In this way, it might be possible to build a more complete, more generalizable picture of potential harms associated with an intervention, without any loss of validity, than by evaluating a single type of study. Such a picture, in combination with estimates of treatment effects also obtained from systematic reviews and meta-analyses, would help clinicians decide the best treatment for their patients.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001026.
- The US National Institutes of Health provide information on clinical research; the UK National Health Service Choices Web site also has a page on clinical trials and medical research
- The Cochrane Collaboration produces and disseminates systematic reviews of health-care interventions
- Medline Plus provides links to further information about clinical trials (in English and Spanish)
There is considerable debate regarding the relative utility of different study designs in generating reliable quantitative estimates for the risk of adverse effects. A diverse range of study designs encompassing randomised controlled trials (RCTs) and non-randomised studies (such as cohort or case-control studies) may potentially record adverse effects of interventions and provide useful data for systematic reviews and meta-analyses [1],[2]. However, there are strengths and weaknesses inherent to each study design, and different estimates and inferences about adverse effects may arise depending on study type [3].
In theory, well-conducted RCTs yield unbiased estimates of treatment effect, but there is often a distinct lack of RCT data on adverse effects [2],[4]–[7]. It is often impractical, too expensive, or ethically difficult to investigate rare, long-term adverse effects with RCTs [5],[7]–[16]. Empirical studies have shown that many RCTs fail to provide detailed adverse effects data, that the quality of those that do report adverse effects is poor [6],[17]–[31], and that the reporting may be strongly influenced by expectations of investigators and patients [32].
In general RCTs are designed and powered to explore efficacy [1],[3],[9],[30],[33]. As the intended effects of treatment are more likely to occur than adverse effects and to occur within the trial time frame, RCTs may not be large enough, or have a sufficient follow-up to identify rare, long-term adverse effects, or adverse effects that occur after the drug has been discontinued [1]–[3],[9],[13],[15],[16],[18],[19],[21],[26],[30],[33]–[53]. Moreover, generalisability of RCT data may be limited if, as is often the case, trials specifically exclude patients at high risk of adverse effects, such as children, the elderly, pregnant women, patients with multiple comorbidities, and those with potential drug interactions [1]–[3],[15],[38],[39],[41],[45],[46],[54]–[56].
Given these limitations it may be important to evaluate the use of data from non-randomised studies in systematic reviews of adverse effects. Owing to the lack of randomisation, all types of observational studies are potentially afflicted by an increased risk of bias (particularly from confounding) [8],[57] and may therefore be a much weaker study design for establishing causation [12]. Nevertheless, observational study designs may sometimes be the only available source of data for a particular adverse effect, and are commonly used in evaluating adverse effects [1],[9],[13],[52],[58],[59]. It is also debatable how important it is to control for confounding by indication for unanticipated adverse effects. Authors have argued that confounding is less likely to occur when an outcome is unintended or unanticipated than when the outcome is an intended effect of the exposure. This is because the potential for that adverse effect is not usually associated with the reasons for choosing a particular treatment, and therefore does not influence the prescribing decision [52],[59]–[62]. For instance, in considering the risk of venous thrombosis from oral contraceptives in healthy young women, the choice of contraceptive may not be linked to risk factors for deep venous thrombosis (an adverse effect that is not anticipated). Thus, any difference in rates of venous thrombosis may be due to a difference in the risk of harm between contraceptives [52],[62].
As both RCTs and observational studies are potentially valuable sources of adverse effects data for meta-analysis, the extent of any discrepancy between the pooled risk estimates from different study designs is a key concern for systematic reviewers. Previous research has tended to focus on differences in treatment effect between RCTs and observational studies [63]–[69]. However, estimates of beneficial effects may potentially be prone to different biases to estimates of adverse effects amongst the different study designs. Can the different study designs provide a consistent picture on the risk of harm, or are the results from different study designs so disparate that it would not be meaningful to combine them in a single review? This uncertainty has not been fully addressed in current methodological guidance on systematic reviews of harms [46], probably because the existing research has so far been inconclusive, with examples of both agreement and disagreement in the reported risk of adverse effects between RCTs and observational studies [1],[11],[15],[48],[51],[70]–[78]. In this meta-analysis of meta-analyses, we aimed to compare the estimates of harm (for specific adverse effects) reported in meta-analysis of RCTs with those reported in meta-analysis of observational studies for the same adverse effect.
Search Strategy
Broad, non-specific searches were undertaken in ten electronic databases to retrieve methodology papers related to any aspect of the incorporation of adverse effects into systematic reviews. A list of the databases and other sources searched is given in Text S1. In addition, the bibliographies of any eligible articles identified were checked for additional references, and citation searches were carried out for all included references using ISI Web of Knowledge. The search strategy used to identify relevant methodological studies in the Cochrane Methodology Register is described in full in Text S2. This strategy was translated as appropriate for the other databases. No language restrictions were applied to the search strategies. However, because of logistical constraints, only non-English papers for which a translation was readily available were retrieved.
Because of the limitations of searching for methodological papers, it was envisaged that relevant papers may be missed by searching databases alone. We therefore undertook hand-searching of selected key journals, conference proceedings, and Web sources, and made contact with other researchers in the field. In particular, one reviewer (S. G.) undertook a detailed hand search focusing on the Cochrane Database of Systematic Reviews and the Database of Abstracts of Reviews of Effects (DARE) to identify systematic reviews that had evaluated adverse effects as a primary outcome. A second reviewer (Y. K. L.) checked the included and excluded papers that arose from this hand search.
Inclusion Criteria
A meta-analysis or evaluation study was considered eligible for inclusion in this review if it evaluated studies of more than one type of design (for example, RCTs versus cohort or case-control studies) on the identification and/or quantification of adverse effects of health-care interventions. We were principally interested in meta-analyses that reported pooled estimates of the risk of adverse effects according to study designs that the authors stated as RCTs, as opposed to analytic epidemiologic studies such as case-control and controlled cohort studies (which authors may have lumped together as a single "observational" category). Our review focuses on the meta-analyses where it was possible to compare the pooled risk ratios (RRs) or odds ratios (ORs) from RCTs against those from other study designs.
Data Extraction
Information was collected on the primary objective of the meta-analyses; the adverse effects, study designs, and interventions included; the number of included studies and number of patients by study design; the number of adverse effects in the treatment and control arm or comparator group; and the type of outcome statistic used in evaluating risk of harm.
We relied on the categorisation of study design as specified by the authors of the meta-analysis. For example, if the author stated that they compared RCTs with cohort studies, we assumed that the studies were indeed RCTs and cohort studies.
Validity assessment and data extraction were carried out by one reviewer (S. G.), and checked by a second reviewer (Y. K. L.). All discrepancies were resolved after going back to the original source papers, with full consensus reached after discussion.
Validity Assessment
The following criteria were used to consider the validity of comparing risk estimates across different study designs. (1) Presence of confounding factors: Discrepancies between the results of RCTs and observational studies may arise because of factors (e.g., differences in population, administration of intervention, or outcome definition) other than study design. We recorded whether the authors of the meta-analysis checked if the RCTs and observational studies shared similar features in terms of population, interventions, comparators, and measurement of outcomes and whether they used methods such as restriction or stratification by population, intervention, comparators, or outcomes to improve the comparability of pooled risk estimates arising from different groups of studies. (2) Heterogeneity by study design: We recorded whether the authors of the meta-analysis explored heterogeneity of the pooled studies by study design (using measures such as Chi2 or I2). We assessed the extent of heterogeneity of each meta-analysis using a cut-off point of p < 0.10 for Chi2 test results, and we specifically looked for instances where I2 was reported as above 50%. In the few instances where both statistics were presented, the results of I2 were given precedence [79]. (3) Statistical analysis comparing study designs: We recorded whether the authors of the meta-analysis described the statistical methods by which the magnitude of the difference between study designs was assessed.
Data Analysis
A descriptive summary of the data in terms of confidence interval (CI) overlap between pooled sets of results by study design, and any differences in the direction of effect between study designs, were presented. The results were said to agree if both study designs identified a significant increase, a significant decrease, or no significant difference in the adverse effects under investigation.
Quantitative differences or discrepancies between the pooled estimates from the respective study designs for each adverse effect were illustrated by taking the ratio of odds ratios (ROR) from meta-analysis of RCTs versus meta-analysis of observational studies. We calculated ROR by using the pooled OR for the adverse outcome from RCTs divided by the pooled OR for the adverse outcome from observational studies. If the meta-analysis of RCTs for a particular adverse effect yielded exactly the same OR as the meta-analysis of observational studies (i.e., complete agreement, or no discrepancy between study designs), then the ROR would be 1.0 (and ln ROR = 0). Because adverse events are rare, ORs and RRs were treated as equivalent [80].
The estimated ROR from each "RCT versus observational study" comparison was then used in a meta-analysis (random effects inverse variance method; RevMan 5.0.25) to summarize the overall ROR between RCTs and observational studies across all the included reviews. The standard error (SE) of ROR can be estimated using the SEs for the RCT and observational estimates:(1)
SEs pertaining to each pooled OR(RCT) and OR(Observ) were calculated from the published 95% CI.
Statistical heterogeneity was assessed using I2 statistic, with I2 values of 30%–60% representing a moderate level of heterogeneity [81].
Included Studies
In total, 52 articles were identified as potentially eligible for this review. On further detailed evaluation, 33 of these articles either compared different types of observational studies to one another (for example, cohort studies versus case control studies) or compared only the incidence of adverse effects (without reporting the RR/OR) in those receiving the intervention according to type of study [57],[82]–[113].
We finally selected 19 eligible articles that compared the relative risk or ORs from RCTs and observational studies (Figure 1) [6],[114]–[131]. These 19 articles covering meta-analysis of 58 separate adverse effects will be the focus of this paper. The 58 meta-analyses included a total of over 311 RCTs and over 222 observational studies (comprising 57 cohort studies, 75 case-control studies, and at least 90 studies described as "observational" by the authors without specifying the exact type) (Table S1). (Exact numbers of RCTs and observational studies cannot be calculated as overlap in the included studies in McGettigan and Henry [127] could not be ascertained.)
Two of the 19 articles were methodological evaluations with the main aim of assessing the influence of study characteristics (including study design) on the measurement of adverse effects [6],[127], whereas the remaining 17 were systematic reviews within which subgroup analysis by study design was embedded [114]–[126],[128]–[131] (Table S1).
Adverse Effects
The majority of the articles compared the results from RCTs and observational studies using only one adverse effect (11/19, 58%) [114],[115],[117]–[119],[121],[122],[124],[125],[129],[130], whilst three included one type of adverse effect (such as cancer, gastrointestinal complications, or cardiovascular events) [116],[127],[128], and five articles included a number of specified adverse effects (ranging from two to nine effects) or any adverse effects [6],[120],[123],[126],[131].
Most (17/19, 89%) of the articles included only one type of intervention (such as hormone replacement therapy [HRT] or nonsteroidal anti-inflammatory drugs) [114]–[120],[122]–[131], whilst one article looked at two interventions (HRT and oral contraceptives) [121] and another included nine interventions [6]. Most of the analyses focused on the adverse effects of pharmacological interventions; however, other topics assessed were surgical interventions (such as bone marrow transplantation and hernia operations) [6],[120] and a diagnostic test (ultrasonography) [131].
Excluded Studies
Text S3 lists the 67 studies that were excluded from this systematic review during the screening and data extraction phases, with the reasons for exclusion.
Summary of Methodological Quality
Role of confounding factors.
Although many of the meta-analyses acknowledged the potential for confounding factors that might yield discrepant findings between study designs, no adjustment for confounding factors was reported in most instances [6],[114]–[116],[118]–[122],[124]–[126],[128]–[131]. However, a few authors did carry out subgroup analysis stratified for factors such as population characteristics, drug dose, or duration of drug exposure.
There were two instances where the authors of the meta-analysis performed some adjustment for potential confounding factors: one carried out meta-regression [123], and in the other methodological evaluation the adjustment method carried out was unclear [127].
Heterogeneity by study design.
Thirteen meta-analyses measured the heterogeneity of at least one set of the included studies grouped by study design using statistical analysis such as Chi2 or I2 [6],[115]–[117],[119],[121],[123]–[125],[127],[129]–[131].
The pooled sets of RCTs were least likely to exhibit any strong indication of heterogeneity; only five (15%) [6],[117],[130],[131] of the 33 [6],[115]–[117],[119],[121],[123],[124],[129]–[131] sets of pooled RCTs were significantly heterogeneous, and in two of these sets of RCTs the heterogeneity was only moderate, with I2 = 58.9% [117] and I2 = 58.8% [130].
Three of the four case-control studies, one of the four cohort studies, and 14 of the 25 studies described as "observational studies" also exhibited substantial heterogeneity.
Statistical analysis comparing study designs.
Authors of one meta-analysis explicitly tested for a difference between the results of the different study designs [6]. Two other analyses reported on the heterogeneity of the pooled RCTs, the pooled observational studies, and the pooled RCTs and observational studies, which can indicate statistical differences where the pooled study designs combined are significantly heterogenous but no significant heterogeneity is seen when the study designs are pooled separately.
Data Analysis
Text S4 documents the decisions made in instances where the same data were available in more than one format.
Size of studies.
In ten methodological evaluations the total number of participants was reported in each set of pooled studies by study design [114],[116],[118]–[120],[123],[125],[126],[130],[131], and in another five methodological evaluations the pooled number of participants was reported for at least one type of study design [6],[124],[127]–[129]. Studies described as "observational" by the authors contained the highest number of participants per study, 34,529 (3,798,154 participants/110 studies), followed by cohort studies, 33,613 (1,378,131 participants/41 studies). RCTs and case-control studies had fewer participants, 2,228 (821,954 participants/369 studies) and 2,144 (105,067 participants/49 studies), respectively.
Confidence interval overlap.
In almost all instances the CIs for the pooled results from the different study designs overlapped (Table 1). However, there were four pooled sets of results in three methodological evaluations where the CIs did not overlap [6],[119],[121].
Agreement and disagreement of results.
In most of the methodological evaluations the results of the treatment effect agreed between types of study design [6],[116],[118],[120],[121],[123]–[131]. Most studies that showed agreement between study designs did not find a significant increase or significant decrease in the adverse effects under investigation (Table 1).
There were major discrepancies in one pooled set of results. Col et al. [119] found an increase in breast cancer with menopausal hormone therapy in RCTs but a decrease in observational studies.
There were other instances where although the direction of the effect was not in opposing directions, apparently different conclusions may have been reached had a review been restricted to either RCTs or observational studies, and undue emphasis was placed on statistical significance tests. For instance, a significant increase in an adverse effect could be identified in an analysis of RCT data, yet pooling the observational studies may have identified no significant difference in adverse effects between the treatment and control group. Table 1 shows that the most common discrepancy between study types occurred when one set of studies identified a significant increase whilst another study design found no statistically significant difference. Given the imprecision in deriving estimates of rare events, this may not reflect any real difference between the estimates from RCTs and observational studies, and it would be more sensible to concentrate on the overlap of CIs rather than the variation in size of the p-values from significance testing.
Ratio of risk ratio or odds ratios estimates.
RRs or ORs from the RCTs were compared to those from the observational studies by meta-analysis of the respective ROR for each adverse effect.
RCTs versus all "observational" studies.
The overall ROR from meta-analysis using the data from all the studies that compared RCTs with either cohort studies or case-control studies, or that grouped studies under the umbrella of "observational" studies was estimated to be 1.03 (95% CI 0.93–1.15) with moderate heterogeneity (I2 = 56%, 95% CI 38%–67%) (Figure 2).
Studies are listed by first author's last name and year of publication (Loke 2008 is [124]; AHRQ 2002 is [114]). In some studies more than one outcome or intervention was assessed. In these instances, indicated by the lowercase letters after the study year, the data were entered in the meta-analysis separately. Other studies compared RCTs to cohort studies and case-control studies separately and therefore are listed twice (with no lowercase letter after the study year).
In Figure 3 we plotted the magnitude of discrepancy (ROR) from each meta-analysis against the precision of its estimates (1/SE), with the contour lines showing the extent of statistical significance for the discrepancy. Values on the x-axis show the magnitude of discrepancy, with the central ln ROR of zero indicating no discrepancy, or complete agreement between the pooled OR estimated from RCTs and observational studies. The y-axis illustrates the precision of the estimates (1/SE), with the data points at the top end having greater precision. This symmetrical distribution of the RORs of the various meta-analyses around the central ln ROR value of zero illustrates that random variation may be an important factor accounting for discrepant findings between meta-analyses of RCTs versus observational studies. If there had been any systematic and consistent bias that drove the results in a particular direction for certain study designs, the plot of RORs would likely be asymmetrical. The vertically tapering shape of the funnel also suggests that the discrepancies between RCTs and observational studies are less apparent when the estimates have greater precision. This may support the need for larger studies to assess adverse effects, whether they are RCTs or observational studies.
Both figures can be interpreted as demonstrating that there are no consistent systematic variations in pooled risk estimates of adverse effects from RCTs versus observational studies.
Sensitivity analysis: limiting to one review per adverse effect examined.
There are no adverse effects for which two or more separate meta-analyses have used exactly the same primary studies (i.e., had complete overlap of RCTs and observational studies) to generate the pooled estimates. This reflects the different time periods, search strategies, and inclusion and exclusion criteria that have been used by authors of these meta-analyses such that even though they were looking at the same adverse effect, they used data from different studies in generating pooled overall estimates. As it turns out, the only adverse effect that was evaluated in more than one review was venous thromboembolism (VTE). There was some, but not complete, overlap of primary studies in three separate reviews of VTE with HRT (involving three overlapping case-control studies from a total of 18 observational studies analysed) and two separate reviews of VTE with oral contraceptives (one overlapping RCT, six [of 13] overlapping cohort studies, and two [of 20] overlapping case-control studies).
For the sensitivity analysis, we removed the three older meta-analyses pertaining to VTE so that the modest overlap could be further reduced, with only one review per specific adverse effect for the sensitivity analysis. The most recent meta-analyses for VTE (Canonico et al. [117] for VTE with HRT, Douketis et al. [121] for VTE with oral contraceptives) were used for analysis of the RORs. This yields RORs that are very similar to the original estimates: 1.06 (95% CI 0.96–1.18) for the overall analysis RCTs versus all observational studies, 1.00 (95% CI 0.71–1.42) for RCTs versus case-control studies and 1.07 (95% CI 0.86–1.34) for RCTs versus cohort studies.
Our analyses found little evidence of systematic differences in adverse effect estimates obtained from meta-analysis of RCTs and from meta-analysis of observational studies. Figure 3 shows that discrepancies may arise not just from differences in study design or systematic bias, but possibly because of the random variation, fluctuations or noise, and imprecision in attempting to derive estimates of rare events. There was less discrepancy between the study designs in meta-analyses that generated more precise estimates from larger studies, either because of better quality, or because the populations were more similar (perhaps because large, long-term RCTs capture a broad population similar to observational studies). Indeed, the adverse effects with discrepant results between RCTs and observational studies were distributed symmetrically to the right and left of the line of no difference, meaning that neither study design consistently over- or underestimates risk of harm as compared to the other. It is likely that other important factors such as population and delivery of intervention are at play here—for instance, the major discrepancy identified in Col et al. [119] for HRT and breast cancer is already well documented. This discrepancy has also been explained by the timing of the start of treatment relative to menopause, which was different between trials and observational studies. After adjustment, the results from the different study designs have been found to no longer differ [132],[133].
Most of the pooled results from the different study designs concurred in terms of identifying a significant increase or decrease, or no significant difference in risk of adverse effects. On the occasions where a discrepancy was found, the difference usually arose from a finding of no significant risk of adverse effects with one study design, in contrast to a significant increase in adverse effects from the other study design. This may reflect the limited size of the included studies to identify significant differences in rare adverse effects.
The increased risk in adverse effects in some studies was not consistently related to any particular study design—RCTs found a significant risk of adverse effects associated with the intervention under investigation in eight instances, while observational studies showed a significantly elevated risk in 11 cases.
Although reasons for discrepancies are unclear, specific factors which may have led to differences in adverse effect estimates were discussed by the respective authors. The differences between observational studies and RCTs in McGettigan and Henry's meta-analysis of cardiovascular risk were thought to be attributable to different dosages of anti-inflammatory drugs used [127]. Differences in Papanikolaou et al. [6] and Col et al. [119] were attributed to differing study populations. Other methodological evaluations discussed the nature of the study designs themselves being a factor that may have led to differences in estimates. For example, some stated that RCTs may record a higher incidence of adverse effects because of closer monitoring of patients, longer duration of treatment and follow-up, and more thorough recording, in line with regulatory requirements [6],[128]. Where RCTs had a lower incidence of adverse effects, it was suggested that this could be attributed to the exclusion of high-risk patients [119] and possibly linked to support by manufacturers [6].
The overall ROR did not suggest any consistent differences in adverse effects estimates from meta-analysis of RCTs versus meta-analysis of observational studies. This interpretation is supported by the funnel plot in Figure 3, which shows that differences between the results of the two study designs are equally distributed across the range. Some discrepancies may arise by chance, or through lack of precision from limited sample size for detecting rare adverse effects. While there are a few instances of sizeable discrepancies, the pooled estimates in Figure 2 and Table 2 indicate that in the scheme of things (particularly where larger, more precise primary studies are available), meta-analysis of observational studies yield adverse effects estimates that broadly match those from meta-analysis of RCTs.
This systematic review of reviews and methodological evaluations has a number of limitations. When comparing the pooled results from different study designs it is important to consider any confounding factors that may account for any differences identified. For instance, if one set of studies was carried out on a younger cohort of patients, with a lower drug dosage, or with shorter duration of use, or relied on passive ascertainment of adverse effects data [6],[17],[52],[134], it might be expected that the magnitude of any adverse effects recorded would be lower. However, most of the methodological evaluations were not conducted with the primary aim of assessing differences in study design, but were systematic reviews with some secondary comparative evaluation of study design embedded.
Another constraint of our overview is that we accepted information and data as reported by the authors of the included meta-analyses. We did not attempt to source the primary studies contained in each meta-analysis, as this would have required extracting data from more than 550 papers. For instance, we relied on the authors' categorisation of study design but are aware that authors may not all have used the same definitions. This is a particular problem with observational studies, where it is often difficult to determine the methodology used in the primary study and categorise it appropriately. In order to overcome this limitation, we chose to base our analysis on RCTs compared to "all" observational studies (either cohort studies, case-control studies, or "observational" studies as defined by the author), with a subgroup analysis based on different types of observational designs.
Another important limitation to this review is the potentially unrepresentative sample used. Systematic reviews with embedded data comparing different study designs may have been missed. The search strategy used was limited to a literature search to identify methodological papers whose primary aim was to assess the influence of study design on adverse effects and to a sift of the full text of systematic reviews of adverse effects (as a primary outcome) from the Cochrane Database of Systematic Reviews and DARE. Nevertheless, it should be noted that the Cochrane Database of Systematic Reviews and DARE databases cover a large proportion of all systematic reviews and that systematic reviews in which adverse effects are included as a secondary aim are unlikely to present subgroup analysis by study design for the adverse effects data.
There was considerable heterogeneity between the comparisons of different studies, suggesting that any differences may be specific to particular types of interventions or adverse effects. It may be that particular types of adverse effects can be identified more easily via particular types of study designs [3],[14],[135],[136]. However, it was difficult to assess the methodological evaluations by type of adverse effects. This would be of interest, given that the literature suggests that RCTs may be better at identifying some types of adverse effects (such as common, anticipated, and short-term) than observational studies.
Future Research
Where no randomized data exist, observational studies may be the only recourse [137]. However, the potential value of observational data needs to be further demonstrated, particularly in specific situations where existing RCTs are short-term or based on highly selected populations. Comparisons of risk estimates from different types of observational studies (e.g., case-control as opposed to cohort) merit further assessment.
In addition, it would be useful (based on a case-control type of design) to carry out an in-depth examination of the meta-analyses (and their included primary studies) with substantial discrepancy amongst the RCTs and observational studies, as compared to other meta-analyses where RCTs and observational studies had close agreement. Any future research in this area should look into the role of confounding factors (such as different population selection and duration of drug exposure) between studies, and lack of precision in point estimates of risk for rare events that could have accounted for discrepant findings amongst RCTs and observational studies.
Our findings have important implications for the conduct of systematic reviews of harm, particularly with regards to selection of a broad range of relevant studies. Although there are strengths and weaknesses to each study design, empirical evidence from this overview indicates that there is no difference on average between estimates of the risk of adverse effects from meta-analyses of RCTs and of observational studies. Instead of restricting the analysis to certain study designs, it may be preferable for systematic reviewers of adverse effects to evaluate a broad range of studies that can help build a complete picture of any potential harm and improve the generalisability of the review without loss of validity.
Supporting Information
Figure S1.
Meta-analysis of RORs from RCTs versus cohort studies, case-control studies and studies described as "observational."
Table S1.
Characteristics of included studies.
Text S1.
Sources searched for included studies.
We thank Lindsey Myers of the Centre for Reviews and Dissemination (CRD) for her peer review comments on the literature searches and Jane Burch of CRD for her kind assistance in screening the titles and abstracts in the Endnote library. We would also like to thank Lesley Stewart of CRD for comments on an earlier draft.
Author Contributions
Conceived and designed the experiments: SG. Performed the study: SG YL. Analyzed the data: SG YL MB. Wrote the paper: SG YL MB. ICMJE criteria for authorship read and met: SG YL MB. Agree with the manuscript's results and conclusions: SG YL MB. Wrote the first draft of the paper: SG.
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Forces affecting change in crop production agriculture
Crop agriculture is clearly no stranger to change. This article examines the key forces affecting change in U.S. crop production.Concentration has been on a steady rise for several decades.Productivity increases have been significant.Producers are adopting larger pieces of equipment and more sophisticated technologies.
January 31, 2011
A number of factors are significantly reshaping crop production agriculture in the United States. Consider the following:
• Concentration has been on a steady rise for several decades. Today, 75 percent of the value of primary field crop production — corn, soybeans, wheat, cotton, rice, sorghum, and barley oats — is produced by 40 percent of U.S. farms (USDA-NASS, Census of Agriculture, 2007).
• Productivity increases have been significant. Corn yields increased from an average of 55 bushels per acre in 1960 to 165 bushels in 2009 — a 300 percent increase in 50 years. Wheat and soybean yields have seen 215 percent and 169 percent increases, respectively, over the same period. Meanwhile, according to the cost and returns survey of USDA's Economic Research Service, variable costs of corn production have declined on a real basis from $0.94 per bushel in 1975 to an estimated $0.63 per bushel in 2005.
• Producers are adopting larger pieces of equipment and more sophisticated technologies. Some estimate the time to plant and harvest the crop, two of the most time consuming operations, has been cut in half in the last decade, allowing producers to effectively manage more acres within one operation.
Crop agriculture is clearly no stranger to change. This article examines the key forces affecting change in U.S. crop production. We describe some of the major industry drivers of change and use Porter's Five Forces analysis to examine the economic conditions, opportunities, and threats facing this industry (Porter, 1979). Through this discussion, we describe how major crop production businesses are adapting to a changing competitive landscape. We begin with an overview of the drivers of change for the industry and then discuss the factors influencing profitability and the implications for the future.
Drivers of Change for the Industry
There are several key factors shaping the economic conditions of the crop agriculture industry. Four dominant forces are currently at work—Growing and Diversified Demand; Technology; Resource Availability; and Societal Influences. Each in isolation, and also in combination, has implications for the structure of crop production, including farm size, the business models used, and relationships to other parts of the industry.
Growing and Diversified Demand
The food, feed, and fiber industries are being challenged to meet a growing and diversified demand. According to the Population Reference Bureau's 2010 Population Data Sheet, the global population is expected to reach almost 9.5 billion by mid-2050, with the majority of the additional 2.6 billion people located in developing countries where the need for affordable, abundant supplies of basic plant and animal based nutrients is greatest.
In addition to growing demands for food, feed and fiber, industrial applications for agricultural production are emerging as well. The energy, polymers, chemicals, and pharmaceuticals industries are increasingly looking to the agricultural sector to supply renewable raw materials for their processes.
Monitoring and information technology, biotechnology, and a variety of other technologies are converging in agriculture to fundamentally change the way crops are grown. Today, yield monitors and GPS, global information systems (GIS), satellite or aerial photography and imagery, weather monitoring and measuring systems, and plant and soil sensing systems are commonly used tools.
Biotechnology applications shorten the cycle time to develop new hybrids and varieties with higher yield potential and stronger resistance to pests and environmental conditions. By combining biotechnology with mechanical and other technologies to control the growth environment—moisture, pest and disease infestation, etc.—the process control approach that defines the traditional mechanical manufacturing assembly line also transforms agriculture into a biological manufacturing industry.
Resource Availability
The availability and cost of natural resources for the agricultural sector has a significant impact on its capacity to respond to growing demand. In some cases, higher prices will be required to bring additional supplies onto the market or to use existing resources more intensively. This is the case for resources such as land, fertilizer, and irrigation. In contrast, supplies of phosphorus and potash are nonrenewable. As agricultural product demand increases, the owners of land, water, and fertilizer resources will benefit.
Societal Influences
The development of the "bioeconomy" and the growing use of renewables have intensified the discussion of the complementary or competitive nature of the economic motivation of creating value and the social motivation of environmental responsiveness and sustainability. Likewise, societal concerns over the use of genetically modified organisms have shown that public opinion can significantly influence the ability of agricultural operations to utilize new technologies in crop production.
Agribusinesses that rely heavily on natural resources cannot ignore the environmental and social issues that are prevalent today. Faced with increasing government regulations and strengthening public opinions, businesses are ever more accountable for their impacts on society and more transparent in their corporate social responsibility activities. In fact, Rankin (2010) found that 68 percent of agribusiness firms surveyed were either planning or actively implementing broad sustainability initiatives.
Factors influencing Crop Production Profitability
The above drivers will significantly shape the U.S. farming sector. The potential profitability of farmers is influenced in part by the economic characteristics of the industry. These economic characteristics can, in large part, be examined using Porter's five forces model. Porter posits that the key economic features of an industry can be identified by examining how suppliers, buyers, rivalry, substitution, and barriers to entry affect it. We use this framework to analyze the economic characteristics of crop production agriculture.
The major supplies, or inputs, for grain/oilseed producers are genetics, crop chemicals, equipment, fertilizers and land. Nonfamily labor on farms is becoming a more important input as well. However, in general, producers are able to substitute capital equipment for labor.
Input suppliers to grain/oilseed production tend to be dominated by large agribusiness firms that compete vigorously for farmer business. The substantial investment required to develop new genetics, crop protection chemicals, and automated equipment necessitates that the firms competing in this sector must achieve substantial economies of scale. The investments required to breed and engineer new crop varieties and traits require significant time and substantial costs for regulatory approval. In the short run, intellectual property rights may allow some firms to capture a significant amount of the value created by their technologies. However, the similarity of many competing seed traits and chemistries allows producers to switch products at relatively low costs, thus reducing the bargaining power of input suppliers.
Recent large price increases have drawn attention to consolidation in the fertilizer industries. In particular, the potash market has relatively few raw input suppliers. Grain/oilseed producers are subject to substantial price shocks as suppliers are able to pass cost increases in the short-term on to farmers. In the longer-term, there are alternatives that have the potential to reduce this supplier control. There is substantial potential for grain/oilseed producers to better recycle and more efficiently utilize livestock waste nutrients for crop production. However, those crop producers without access to these alternatives will likely continue to face pressure from volatile fertilizer markets.
Capital is a critical input to modern agricultural production. The U.S. capital markets are extremely efficient and competitive. This presents U.S. producers with a significant advantage over many of their foreign competitors. The establishment of the Farm Credit System was a strategic response to the competitive situation in agricultural lending. Today, agricultural credit is widely available to creditworthy producers.
The grain/oilseed sector markets its products to three major sets of customers: grain merchants and handlers, livestock producers, and renewable energy/industrial users. While exports are also critical to the sector, we focus on the domestic and international markets where grains/oilseeds are used.
The first key customers are grain merchants and handlers that aggregate farm output into meaningful quantities that can be delivered to end users and processors. These customers also have the key role of storing a crop that is harvested in a few months and consumed over the course of a year. These firms are typically private companies and traditional farmer cooperatives. Today, there is substantial concentration among the private grain handling and merchandising companies. Crop producers, however, are still able to market their products on reasonable terms. And, the U.S. Department of Justice has shown a willingness to eliminate potential market power advantages in this industry segment when necessary. Overall, the economic structure of this portion of the grain/oilseed supply chain is unlikely to adversely impact the profitability of grain/oilseed producers because they can easily switch between competing handlers and/or invest in their own storage and handling facilities.
The second key set of customers for grain/oilseed producers is the animal agriculture sector. Feed use currently accounts for roughly half of grain/oilseed demand. Although livestock farms, too, have undergone dramatic consolidation in recent decades, they remain, by and large, unable to exert significant pressure on grain/oilseed producers. However, the expansion of animal protein markets is important for expanding demand for grain/oilseed production. Beef, pork, and dairy producers have all recently experienced significant financial hardship as feed costs escalated from increasing overall grain/oilseed demand. In the future, a healthy and vibrant animal agriculture sector is critical to the long-term profitability of grain/oilseed producers.
The renewable energy and industrial food manufacturing sector is the third key customer of grain/oilseed producers. This sector has recently undergone dramatic growth and also significant concentration, with a number of mergers and acquisitions among ethanol and food manufacturers. However, because grains/oilseeds are traded as commodities, the ability of the sector to exert significant buyer power over producers is limited.
Customers likely will not exert significant, negative influences on industry profitability in the future due to traditional concerns over concentration. Instead, the impact of these industries will be driven by their fundamental profitability. Here, there is some cause for concern. The large increases in demand associated with renewable energy production, for example, are largely policy driven. Should the policy become less attractive to renewable energy production, there could be significant declines in biofuel demand. Likewise, these industries are highly competitive and dependent upon energy prices. Sustained low, energy prices would significantly reduce demand from these customers. Animal agriculture should significantly benefit from rapid population and economic growth in China and India. However, animal protein is generally a higher cost source of protein, and slowing economic growth in these countries would significantly reduce demand for animal protein and hurt grain/oilseed producers.
Agricultural production is characterized by a high degree of competitive rivalry. Efforts to develop branded or specialized products are quickly and effectively copied, and meaningful differentiation is difficult to achieve. The competitive rivalry plays out most clearly in bidding for productive resources. Here, producers typically bid most of their long-term potential profitability into the price of fixed assets such as farmland. As a result, rivalry has a very detrimental impact on individual profitability in the sector. However, it also encourages firms to be extremely efficient and productive as cost competition is the most likely source of competitive advantage. Rivalry has also clearly manifested itself on the global stage as South American agricultural production has rapidly increased to the point where Brazil and Argentina are key world soybean producers. Further, continuing genetic improvements, such as drought resistance, are allowing production of crops in regions once not suitable.
Substitute Goods
There are important substitution considerations for grain/oilseed producers. Movements away from or towards animal proteins in the diet can have a significant impact on grain consumption. Grain products also compete in a variety of industrial based markets, such as energy and bioplastics where they serve as a substitute for petroleum-based products. These markets are quite large, but are also highly competitive. The large quantities of these available substitutes will limit producer profitability.
Barriers to Entry
There are few meaningful barriers to entry in production agriculture. While the capital requirements can be substantial to a young person trying to begin a career in farming, these requirements are not prohibitive for most businesses considering large-scale entry. For larger investors and pension funds considering entry into agriculture, the barrier in the United States is typically finding enough land in one geographic area to make a significant investment. While still limited in number, there are more farm management companies pursuing large scale farmland investments operated through both internal and external management arrangements. Funding for these enterprises increasingly comes from equity markets. The relative ease with which parties with access to capital can enter crop production will limit the upside profitability potential for current producers.
Conclusions and Implications
An increasing and diverse demand, rapid adoption of new technologies, limitations on global agricultural resources, and a society with increasing expectations of agriculture to produce a safe, abundant, affordable — and now "sustainable" — supply of food, fiber, feed, and energy will all shape the future environment for crop producers. In addition, crop producers' ability to generate profits will change with the profitability prospects of input suppliers, customers, competitors, substitutes, and new entrants.
The drivers of change suggest it will be critical for crop producers to be diligent in their pursuit of ever increasing productivity. To meet the demands of a growing global population with limited natural resources and increasing societal requirements, producers will need to continue to increase yields per acre at increased rates. At the margin, total production can be increased by bringing new lands into production, but those available, productive lands are limited. Thus, technology adoption will continue to play a crucial role in enhancing the sector's productivity. The pace with which these technologies are developed and adopted will depend on both the economics of crop production and society's willingness to accept the new technologies. We believe it is not a matter of if new technologies will be adopted, just a matter of the speed with which they are adopted.
An analysis of Porter's Five Forces that affect profitability indicates that scale efficiencies will continue to be a critical driver of a crop producer's competitive position. In particular, while customer buying power and input supplier power are not considered to be major deterrents to profitability, inter-firm rivalry and the relative ease of entry into the sector will continue to place pressure on the industry. While competition in the customer segments will keep buyers from significantly influencing the general market, the number of producers in a given crop producing area and the lack of differentiation make bargaining against customers very difficult without significant scale. Bargaining power usually requires some form of scale which allows the amount of product controlled to be significant in the marketplace. This is either done through scale within the operation or formal collaborations with other firms to appear bigger to the marketplace. This may be in the form of cooperatives, partnerships, LLC's, etc. and may include local, regional, national, and/or international collaborations.
In addition, the competition for limited, available land is fierce, and those producers with greatest efficiency are at a competitive advantage in acquiring those resources. While scale efficiencies normally suggest expansion of the farming operation size, producers will also have to purposefully improve the productivity of the land they manage. This will require the adoption of technologies that allow producers to drive down costs per unit, including a combination of information, biologic and other technologies.
In the future, crop producers likely will also have increased opportunities to differentiate their businesses and commodity production. Synergistic activities including marketing fertilizer from livestock waste, providing services to other producers including grain storage and trucking, and even off-farm employment all could become viable business opportunities. Producers able to meet unique contract specifications or negotiate preferred supplier contracts may ultimately increase their profitability by reducing their reliance on traditional commodity production and markets.
For More Information
Land O'Lakes Member Services Interview with Great Lakes Manufacturing Experts. (2008).
Porter, M.E. (1979, March/April). How Competitive Forces Shape Strategy, Harvard Business Review, 57(2), 137-147.
Porter, M.E. (1980). Competitive Strategy. New York, New York. Free Press.
Porter, M.E. (2008, January). The Five Competitive Forces That Shape Strategy. Harvard Business Review, 86(1), 78-93.
Population Reference Bureau. (2010). World Population Data Sheet. Available online: http://www.prb.org/pdf10/10wpds_eng.pdf.
Rankin, A. (2010). Sustainability Strategies in Agribusiness: Understanding Key Drivers, Objectives, And Actions. Unpublished Thesis. Purdue University.
United States Department of Agriculture, National Agricultural Statistics Service (USDA-NASS). (2007). 2007 Census of Agriculture-United States Data. Available online: http://www.agcensus.usda.gov/Publications/2007/Full_Report/usv1.pdf.
Elizabeth A. Bechdol ([email protected]), is Director of Agribusiness Strategies, Ice Miller LLP, Indianapolis, Indiana. Alan Gray ([email protected]) is Professor Department of Agricultural Economics and Director of the Center for Food and Agribusiness and MS-MBA program, Purdue University, West Lafayette, Indiana. Brent Gloy ([email protected]), Associate Professor Department of Agricultural Economics and Associate Director of Research of the Center for Food and Agricultural Business, Purdue University, West Lafayette, Indiana.
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