Small Studies: Strengths and Limitations
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Descriptive Statistics (Part 2): Interpreting Study Results
Statistical Notes II Descriptive statistics (Part 2): Interpreting study results A Cook and A Sheikh he previous paper in this series looked at ‘baseline’. Investigations of treatment effects can be descriptive statistics, showing how to use and made in similar fashion by comparisons of disease T interpret fundamental measures such as the probability in treated and untreated patients. mean and standard deviation. Here we continue with descriptive statistics, looking at measures more The relative risk (RR), also sometimes known as specific to medical research. We start by defining the risk ratio, compares the risk of exposed and risk and odds, the two basic measures of disease unexposed subjects, while the odds ratio (OR) probability. Then we show how the effect of a disease compares odds. A relative risk or odds ratio greater risk factor, or a treatment, can be measured using the than one indicates an exposure to be harmful, while relative risk or the odds ratio. Finally we discuss the a value less than one indicates a protective effect. ‘number needed to treat’, a measure derived from the RR = 1.2 means exposed people are 20% more likely relative risk, which has gained popularity because of to be diseased, RR = 1.4 means 40% more likely. its clinical usefulness. Examples from the literature OR = 1.2 means that the odds of disease is 20% higher are used to illustrate important concepts. in exposed people. RISK AND ODDS Among workers at factory two (‘exposed’ workers) The probability of an individual becoming diseased the risk is 13 / 116 = 0.11, compared to an ‘unexposed’ is the risk. -
Meta-Analysis of Proportions
NCSS Statistical Software NCSS.com Chapter 456 Meta-Analysis of Proportions Introduction This module performs a meta-analysis of a set of two-group, binary-event studies. These studies have a treatment group (arm) and a control group. The results of each study may be summarized as counts in a 2-by-2 table. The program provides a complete set of numeric reports and plots to allow the investigation and presentation of the studies. The plots include the forest plot, radial plot, and L’Abbe plot. Both fixed-, and random-, effects models are available for analysis. Meta-Analysis refers to methods for the systematic review of a set of individual studies with the aim to combine their results. Meta-analysis has become popular for a number of reasons: 1. The adoption of evidence-based medicine which requires that all reliable information is considered. 2. The desire to avoid narrative reviews which are often misleading. 3. The desire to interpret the large number of studies that may have been conducted about a specific treatment. 4. The desire to increase the statistical power of the results be combining many small-size studies. The goals of meta-analysis may be summarized as follows. A meta-analysis seeks to systematically review all pertinent evidence, provide quantitative summaries, integrate results across studies, and provide an overall interpretation of these studies. We have found many books and articles on meta-analysis. In this chapter, we briefly summarize the information in Sutton et al (2000) and Thompson (1998). Refer to those sources for more details about how to conduct a meta- analysis. -
Effect Size (ES)
Lee A. Becker <http://web.uccs.edu/lbecker/Psy590/es.htm> Effect Size (ES) © 2000 Lee A. Becker I. Overview II. Effect Size Measures for Two Independent Groups 1. Standardized difference between two groups. 2. Correlation measures of effect size. 3. Computational examples III. Effect Size Measures for Two Dependent Groups. IV. Meta Analysis V. Effect Size Measures in Analysis of Variance VI. References Effect Size Calculators Answers to the Effect Size Computation Questions I. Overview Effect size (ES) is a name given to a family of indices that measure the magnitude of a treatment effect. Unlike significance tests, these indices are independent of sample size. ES measures are the common currency of meta-analysis studies that summarize the findings from a specific area of research. See, for example, the influential meta- analysis of psychological, educational, and behavioral treatments by Lipsey and Wilson (1993). There is a wide array of formulas used to measure ES. For the occasional reader of meta-analysis studies, like myself, this diversity can be confusing. One of my objectives in putting together this set of lecture notes was to organize and summarize the various measures of ES. In general, ES can be measured in two ways: a) as the standardized difference between two means, or b) as the correlation between the independent variable classification and the individual scores on the dependent variable. This correlation is called the "effect size correlation" (Rosnow & Rosenthal, 1996). These notes begin with the presentation of the basic ES measures for studies with two independent groups. The issues involved when assessing ES for two dependent groups are then described. -
FORMULAS from EPIDEMIOLOGY KEPT SIMPLE (3E) Chapter 3: Epidemiologic Measures
FORMULAS FROM EPIDEMIOLOGY KEPT SIMPLE (3e) Chapter 3: Epidemiologic Measures Basic epidemiologic measures used to quantify: • frequency of occurrence • the effect of an exposure • the potential impact of an intervention. Epidemiologic Measures Measures of disease Measures of Measures of potential frequency association impact (“Measures of Effect”) Incidence Prevalence Absolute measures of Relative measures of Attributable Fraction Attributable Fraction effect effect in exposed cases in the Population Incidence proportion Incidence rate Risk difference Risk Ratio (Cumulative (incidence density, (Incidence proportion (Incidence Incidence, Incidence hazard rate, person- difference) Proportion Ratio) Risk) time rate) Incidence odds Rate Difference Rate Ratio (Incidence density (Incidence density difference) ratio) Prevalence Odds Ratio Difference Macintosh HD:Users:buddygerstman:Dropbox:eks:formula_sheet.doc Page 1 of 7 3.1 Measures of Disease Frequency No. of onsets Incidence Proportion = No. at risk at beginning of follow-up • Also called risk, average risk, and cumulative incidence. • Can be measured in cohorts (closed populations) only. • Requires follow-up of individuals. No. of onsets Incidence Rate = ∑person-time • Also called incidence density and average hazard. • When disease is rare (incidence proportion < 5%), incidence rate ≈ incidence proportion. • In cohorts (closed populations), it is best to sum individual person-time longitudinally. It can also be estimated as Σperson-time ≈ (average population size) × (duration of follow-up). Actuarial adjustments may be needed when the disease outcome is not rare. • In an open populations, Σperson-time ≈ (average population size) × (duration of follow-up). Examples of incidence rates in open populations include: births Crude birth rate (per m) = × m mid-year population size deaths Crude mortality rate (per m) = × m mid-year population size deaths < 1 year of age Infant mortality rate (per m) = × m live births No. -
CHAPTER 3 Comparing Disease Frequencies
© Smartboy10/DigitalVision Vectors/Getty Images CHAPTER 3 Comparing Disease Frequencies LEARNING OBJECTIVES By the end of this chapter the reader will be able to: ■ Organize disease frequency data into a two-by-two table. ■ Describe and calculate absolute and relative measures of comparison, including rate/risk difference, population rate/risk difference, attributable proportion among the exposed and the total population and rate/risk ratio. ■ Verbally interpret each absolute and relative measure of comparison. ■ Describe the purpose of standardization and calculate directly standardized rates. ▸ Introduction Measures of disease frequency are the building blocks epidemiologists use to assess the effects of a disease on a population. Comparing measures of disease frequency organizes these building blocks in a meaningful way that allows one to describe the relationship between a characteristic and a disease and to assess the public health effect of the exposure. Disease frequencies can be compared between different populations or between subgroups within a population. For example, one might be interested in comparing disease frequencies between residents of France and the United States or between subgroups within the U.S. population according to demographic characteristics, such as race, gender, or socio- economic status; personal habits, such as alcoholic beverage consump- tion or cigarette smoking; and environmental factors, such as the level of air pollution. For example, one might compare incidence rates of coronary heart disease between residents of France and those of the United States or 57 58 Chapter 3 Comparing Disease Frequencies among U.S. men and women, Blacks and Whites, alcohol drinkers and nondrinkers, and areas with high and low pollution levels. -
Understanding Relative Risk, Odds Ratio, and Related Terms: As Simple As It Can Get Chittaranjan Andrade, MD
Understanding Relative Risk, Odds Ratio, and Related Terms: As Simple as It Can Get Chittaranjan Andrade, MD Each month in his online Introduction column, Dr Andrade Many research papers present findings as odds ratios (ORs) and considers theoretical and relative risks (RRs) as measures of effect size for categorical outcomes. practical ideas in clinical Whereas these and related terms have been well explained in many psychopharmacology articles,1–5 this article presents a version, with examples, that is meant with a view to update the knowledge and skills to be both simple and practical. Readers may note that the explanations of medical practitioners and examples provided apply mostly to randomized controlled trials who treat patients with (RCTs), cohort studies, and case-control studies. Nevertheless, similar psychiatric conditions. principles operate when these concepts are applied in epidemiologic Department of Psychopharmacology, National Institute research. Whereas the terms may be applied slightly differently in of Mental Health and Neurosciences, Bangalore, India different explanatory texts, the general principles are the same. ([email protected]). ABSTRACT Clinical Situation Risk, and related measures of effect size (for Consider a hypothetical RCT in which 76 depressed patients were categorical outcomes) such as relative risks and randomly assigned to receive either venlafaxine (n = 40) or placebo odds ratios, are frequently presented in research (n = 36) for 8 weeks. During the trial, new-onset sexual dysfunction articles. Not all readers know how these statistics was identified in 8 patients treated with venlafaxine and in 3 patients are derived and interpreted, nor are all readers treated with placebo. These results are presented in Table 1. -
Outcome Reporting Bias in COVID-19 Mrna Vaccine Clinical Trials
medicina Perspective Outcome Reporting Bias in COVID-19 mRNA Vaccine Clinical Trials Ronald B. Brown School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L3G1, Canada; [email protected] Abstract: Relative risk reduction and absolute risk reduction measures in the evaluation of clinical trial data are poorly understood by health professionals and the public. The absence of reported absolute risk reduction in COVID-19 vaccine clinical trials can lead to outcome reporting bias that affects the interpretation of vaccine efficacy. The present article uses clinical epidemiologic tools to critically appraise reports of efficacy in Pfzier/BioNTech and Moderna COVID-19 mRNA vaccine clinical trials. Based on data reported by the manufacturer for Pfzier/BioNTech vaccine BNT162b2, this critical appraisal shows: relative risk reduction, 95.1%; 95% CI, 90.0% to 97.6%; p = 0.016; absolute risk reduction, 0.7%; 95% CI, 0.59% to 0.83%; p < 0.000. For the Moderna vaccine mRNA-1273, the appraisal shows: relative risk reduction, 94.1%; 95% CI, 89.1% to 96.8%; p = 0.004; absolute risk reduction, 1.1%; 95% CI, 0.97% to 1.32%; p < 0.000. Unreported absolute risk reduction measures of 0.7% and 1.1% for the Pfzier/BioNTech and Moderna vaccines, respectively, are very much lower than the reported relative risk reduction measures. Reporting absolute risk reduction measures is essential to prevent outcome reporting bias in evaluation of COVID-19 vaccine efficacy. Keywords: mRNA vaccine; COVID-19 vaccine; vaccine efficacy; relative risk reduction; absolute risk reduction; number needed to vaccinate; outcome reporting bias; clinical epidemiology; critical appraisal; evidence-based medicine Citation: Brown, R.B. -
Certification Accredited Schools List
= Academic Clinical Research Programs Which Currently Meet Waiver Requirements for the Academy of Clinical Research Professional (Updated October 15, 2015) This list includes academic clinical research programs which, as of the above date, have been evaluated and found to meet the current waiver requirements established by the Academy of Clinical Research Professionals (Academy) Board of Trustees. Programs that meet the waiver requirements allow a candidate to waive 1,500 hours of hands-on experience performing the Essential Duties of the program to which the candidate has made application. This list is not to be considered exhaustive and is not designed to represent all the academic offerings available. Appearance on this list is not to be construed as an endorsement of its content or format by the Academy or a guarantee that a candidate will be eligible for certification. All individuals interested in enrolling in a program of study should evaluate any program on the basis of his or her personal needs. (* - programs marked with an asterisk MAY be accepted but acceptance will be dependent on the complete listing of courses completed) Individuals seeking additional information about each program should contact the institution directly. Academic Institution Program Name ( Click Title for Web Navigation) Academy of Applied Pharmaceutical Sciences Clinical Research Diploma Program American University of Health Sciences Master of Science in Clinical Research Anoka-Ramsey Community College Clinical Research Professional - Certificate Graduate -
Placebo-Controlled Trials of New Drugs: Ethical Considerations
Reviews/Commentaries/Position Statements COMMENTARY Placebo-Controlled Trials of New Drugs: Ethical Considerations DAVID ORENTLICHER, MD, JD als, placebo controls are not appropriate when patients’ health would be placed at significant risk (8–10). A placebo- controlled study for a new hair- uch controversy exists regarding sufficiently more effective than placebo to thickening agent could be justified; a the ethics of placebo-controlled justify its use. Finally, not all established placebo-controlled study for patients M trials in which an experimental therapies have been shown to be superior with moderate or severe hypertension therapy will compete with an already es- to placebo. If newer drugs are compared would not be acceptable (11). Similarly, if tablished treatment (or treatments). In with the unproven existing therapies, an illness causes problems when it goes such cases, argue critics, patients in the then patients may continue to receive untreated for a long period of time, a 52- control arm of the study should receive an drugs that are harmful without being week study with a placebo control is accepted therapy rather than a placebo. helpful. much more difficult to justify than a By using an active and effective drug, the Moreover, say proponents of placebo 6-week study (12). control patients would not be placed at controls, patients can be protected from When David S.H. Bell (13) explains risk for deterioration of their disease, and harm by “escape” criteria, which call for why placebo controls are unacceptable the study would generate more meaning- withdrawal from the trial if the patient for new drugs to treat type 2 diabetes, he ful results for physicians. -
Statistical Power, Sample Size, and Their Reporting in Randomized Controlled Trials
Statistical Power, Sample Size, and Their Reporting in Randomized Controlled Trials David Moher, MSc; Corinne S. Dulberg, PhD, MPH; George A. Wells, PhD Objective.\p=m-\Todescribe the pattern over time in the level of statistical power and least 80% power to detect a 25% relative the reporting of sample size calculations in published randomized controlled trials change between treatment groups and (RCTs) with negative results. that 31% (22/71) had a 50% relative Design.\p=m-\Ourstudy was a descriptive survey. Power to detect 25% and 50% change, as statistically significant (a=.05, relative differences was calculated for the subset of trials with results in one tailed). negative Since its the of which a was used. Criteria were both publication, report simple two-group parallel design developed Freiman and has been cited to trial results as or and to the outcomes. colleagues2 classify positive negative identify primary more than 700 times, possibly indicating Power calculations were based on results from the primary outcomes reported in the seriousness with which investi¬ the trials. gators have taken the findings. Given Population.\p=m-\Wereviewed all 383 RCTs published in JAMA, Lancet, and the this citation record, one might expect an New England Journal of Medicine in 1975, 1980, 1985, and 1990. increase over time in the awareness of Results.\p=m-\Twenty-sevenpercent of the 383 RCTs (n=102) were classified as the consequences of low power in pub¬ having negative results. The number of published RCTs more than doubled from lished RCTs and, hence, an increase in 1975 to 1990, with the proportion of trials with negative results remaining fairly the reporting of sample size calcula¬ stable. -
Generating Real-World Evidence by Strengthening Real-World Data Sources
Generating Real-World Evidence by Strengthening Real-World Data Sources Using “real-world evidence” to bring new “Every day, health care professionals are updating patients’ treatments to patients as part of the 21st electronic health records with data on clinical outcomes Century Cures Act (the “Cures Act”) is a key resulting from medical interventions used in routine clinical priority for the Department of Health and practice. As our experience with new medical products Human Services (HHS). Specifically, the Cures expands, our knowledge about how to best maximize their Act places focus on the use of real-world data benefits and minimize potential risks sharpens with each data to support regulatory decision-making, including point we gather. Every clinical use of a product produces data the approval of new indications for existing that can help better inform us about its safety and efficacy.” drugs in order to make drug development faster jacqueline corrigan-curay, md, jd and more efficient. As such, the Cures Act director of the office of medical policy in fda’s center for drug evaluation and research has tasked the Food and Drug Administration (FDA) to develop a framework and guidance for evaluating real-world evidence in the context of drug regulation.1 Under the FDA’s framework, real-world evidence (RWE) is generated by different study designs or analyses, including but not limited to, randomized trials like large simple trials, pragmatic trials, and observational studies. RWE is the clinical evidence about the use, potential benefits, and potential risks of a medical product based on an analysis of real-world data (RWD). -
Studies of Staphylococcal Infections. I. Virulence of Staphy- Lococci and Characteristics of Infections in Embryonated Eggs * WILLIAM R
Journal of Clinical Investigation Vol. 43, No. 11, 1964 Studies of Staphylococcal Infections. I. Virulence of Staphy- lococci and Characteristics of Infections in Embryonated Eggs * WILLIAM R. MCCABE t (From the Research Laboratory, West Side Veterans Administration Hospital, and the Department of Medicine, Research and Educational Hospitals, University of Illinois College of Medicine, Chicago, Ill.) Many of the determinants of the pathogenesis niques still require relatively large numbers of and course of staphylococcal infections remain staphylococci to produce infection (19). Fatal imprecisely defined (1, 2) despite their increas- systemic infections have been equally difficult to ing importance (3-10). Experimental infections produce in animals and have necessitated the in- in suitable laboratory animals have been of con- jection of 107 to 109 bacteria (20-23). A few siderable assistance in clarifying the role of host strains of staphylococci have been found that are defense mechanisms and specific bacterial virulence capable of producing lethal systemic infections factors with a variety of other infectious agents. with inocula of from 102 to 103 bacteria (24) and A sensitive experimental model would be of value have excited considerable interest (25-27). The in defining the importance of these factors in virulence of these strains apparently results from staphylococcal infections, but both humans and an unusual antigenic variation (27, 28) which, the usual laboratory animals are relatively re- despite its interest, is of doubtful significance in sistant. Extremely large numbers of staphylo- human staphylococcal infection, since such strains cocci are required to produce either local or sys- have been isolated only rarely from clinical in- temic infections experimentally.