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Lipitor

Y. Kliff. California State University, San Bernardino. 2019.

Studies with small sample sizes can frequently be underpowered to detect difference order lipitor 20 mg mastercard. Precision: The likelihood of random errors in the results of a study buy discount lipitor 5 mg on line, meta-analysis order 20 mg lipitor, or measurement. The greater the precision, the less the random error. Confidence intervals around the estimate of effect are one way of expressing precision, with a narrower confidence interval meaning more precision. Prospective study: A study in which participants are identified according to current risk status or exposure and followed forward through time to observe outcome. Prevalence: How often or how frequently a disease or condition occurs in a group of people. Prevalence is calculated by dividing the number of people who have the disease or condition by the total number of people in the group. Long-acting opioid analgesics 51 of 74 Final Update 6 Report Drug Effectiveness Review Project Probability: The likelihood (or chance) that an event will occur. In a clinical research study, it is the number of times a condition or event occurs in a study group divided by the number of people being studied. Publication bias: A bias caused by only a subset of the relevant data being available. The publication of research can depend on the nature and direction of the study results. Studies in which an intervention is not found to be effective are sometimes not published. Because of this, systematic reviews that fail to include unpublished studies may overestimate the true effect of an intervention. In addition, a published report might present a biased set of results (for example, only outcomes or subgroups for which a statistically significant difference was found). P value: The probability (ranging from zero to one) that the results observed in a study could have occurred by chance if the null hypothesis was true. Q-statistic: A measure of statistical heterogeneity of the estimates of effect from studies. It is calculated as the weighted sum of the squared difference of each estimate from the mean estimate. Random-effects model: A statistical model in which both within-study sampling error (variance) and between-studies variation are included in the assessment of the uncertainty (confidence interval) of the results of a meta-analysis. When there is heterogeneity among the results of the included studies beyond chance, random-effects models will give wider confidence intervals than fixed-effect models. Randomization: The process by which study participants are allocated to treatment groups in a trial. Adequate (that is, unbiased) methods of randomization include computer generated schedules and random-numbers tables. Randomized controlled trial: A trial in which two or more interventions are compared through random allocation of participants. Regression analysis: A statistical modeling technique used to estimate or predict the influence of one or more independent variables on a dependent variable, for example, the effect of age, sex, or confounding disease on the effectiveness of an intervention. Relative risk: The ratio of risks in two groups; same as a risk ratio. Retrospective study: A study in which the outcomes have occurred prior to study entry. Risk: A way of expressing the chance that something will happen. It is a measure of the association between exposure to something and what happens (the outcome). Risk is the same as probability, but it usually is used to describe the probability of an adverse event. It is the rate of events (such as breast cancer) in the total population of people who could have the event (such as women of a certain age). Risk difference: The difference in size of risk between two groups. In intervention studies, it is the ratio of the risk in the intervention group to the risk in the control group. A risk ratio of 1 indicates no difference between comparison groups. For undesirable outcomes, a risk ratio that is <1 indicates that the intervention was effective in reducing the risk of that outcome. Long-acting opioid analgesics 52 of 74 Final Update 6 Report Drug Effectiveness Review Project Run-in period: Run in period: A period before randomization when participants are monitored but receive no treatment (or they sometimes all receive one of the study treatments, possibly in a blind fashion). The data from this stage of a trial are only occasionally of value but can serve a valuable role in screening out ineligible or non-compliant participants, in ensuring that participants are in a stable condition, and in providing baseline observations. A run-in period is sometimes called a washout period if treatments that participants were using before entering the trial are discontinued. This term (or the term ‘‘safe’’) should not be used when evidence on harms is simply absent or is insufficient. Sample size: The number of people included in a study. In research reports, sample size is usually expressed as "n. Larger sample sizes also increase the chance that rare events (such as adverse effects of drugs) will be detected. Sensitivity analysis: An analysis used to determine how sensitive the results of a study or systematic review are to changes in how it was done. Sensitivity analyses are used to assess how robust the results are to uncertain decisions or assumptions about the data and the methods that were used. Side effect: Any unintended effect of an intervention. Side effects are most commonly associated with pharmaceutical products, in which case they are related to the pharmacological properties of the drug at doses normally used for therapeutic purposes in humans. Standard deviation (SD): A measure of the spread or dispersion of a set of observations, calculated as the average difference from the mean value in the sample. Standard error (SE): A measure of the variation in the sample statistic over all possible samples of the same size. The standard error decreases as the sample size increases. Standard treatment: The treatment or procedure that is most commonly used to treat a disease or condition.

By contrast buy generic lipitor 10mg on line, human subtype H1 retains the ances- tral avian residues at 226 and 228 generic lipitor 10mg free shipping, but has changes in positions 138 discount 5mg lipitor mastercard, 186, 190, 194, and 225 (see fig. Thus, different human lineages have followed different pathways of adaptation to receptor binding. Experimental evolution studies of the H3 subtype support the phylo- genetic data. Horse serum contains α(2, 6)-linked sialic acid, which binds to human strains of influenza and interferes with the viral life cycle. The horse serum therefore selects strongly foraltered binding to α(2, 3)-linked sialic acid (Matrosovich et al. This substitution changed the leucine of human H3 to a glutamine residue, the same residue found in the ancestral avian H3 subtype. This substitution caused the modi- fied virus to avoid α(2, 6) binding and interference by horse serum and allowed binding to α(2, 3)-bearing receptors as in the ancestral avian type. They began with aduckH3isolate that had glutamine at position 226 and favored bind- ing to α(2, 3) sialic acid linkages. Binding to erythrocytes selected vari- ants that favor the α(2, 6) linkage. Viruses bound to erythrocytes were eluted and used to infect Madin-Darby canine kidney (MDCK) cells, a standard culture vehicle for human influenza isolates. This selection process caused replacement of glutamine at position 226 by leucine, which inturnfavoredbindingofα(2, 6)-overα(2, 3)-linked sialic acid. The same sort of experimental evolution on H1 isolates would be very interesting. If selection of avian H1 for a change from α(2, 3) to α(2, 6) binding causes the same substitutions as occurred in the human H1 lin- eage, then the different genetic background of avian H1 compared with H3 would be implicated in shaping the particular amino acid substitu- tions. By contrast, if experimental evolution favors a change at posi- tion 226 as in H3, then the evolution of human H1 receptor binding may have followed a more complex pathway than simple selection for α(2, 6)-linked sialic acid. Various steps have been proposed for adaptation of aquatic bird iso- latestohumans. NA re- moves sialic acid from HA receptors, apparently facilitating release of viral progeny from the surface of host cells. If a viral lineage switches its HA specificity from the avian α(2, 3) to the human α(2, 6) form, but NA retains the avian specificity, then the lineage may have difficulty spread- ing in humans. Complex pathways may be required for joint adaptation of HA and NA (Matrosovich et al. These studies raise the general problem of evolutionary pathways by which pathogens change host receptors. If two or more pathogen func- tions must change simultaneously, then changes in receptor affinity may be rare. The need for joint change may cause significant constraint on amino acid substitutions in receptor binding factors. In an experimen- tal setting, one begins with a particular, defined genotype as the genetic back- ground for further analysis. One then obtains single amino acid substitutions or small numbers of substitutions derived from the original background ge- notype. Substitutions may be obtained by imposing selective pressures such as antibodies in an experimental evolution regime or by imposing site-directed or random mutagenesis. Substitutions affect various components of fitness as described in the text. Each of these processes relates fitness to different kinetic aspects of surface binding. First, changes in cell binding and entry affect the performance of in- tracellular pathogens. The relationship between binding kinetics and fitness may be rather complex. In that figure, the substitutions 190 E→A, 225 G→R, and 228 S→Gallhavestronger binding affinity than the common wild type. HA has a relatively low affinity for its host-cell receptors (Skehel and Wiley 2000). The fact that some substitutions raise affinity suggests that binding has been adjusted by selection to an intermediate rate. It may be possible to test this idea in various experimental systems by competing viruses with different cell binding kinetics. Robertson (1993, 1999) reviews experimental evolution work on the adaptation of influenza to culture conditions in chicken eggs and Madin- Darby canine kidney (MDCK) cells. Those in vitro systems allow study of competition between different viral genotypes (Robertson et al. EXPERIMENTAL EVOLUTION: INFLUENZA 219 Simple in vitro culture conditions may select for higher binding affin- itybetween pathogen and host cells (Robertson et al. It would be interesting to compare the fitnesses in vivo between wild type and mutants selected for higher binding affinity in vitro. The second role of substitutions arises from binding that interferes with viral fitness. Too high affinity of HA for the primary host-cell re- ceptor may impair release of progeny viruses. High affinity may also ag- gregate viruses in localized regions, interfering with infectious spread. Again, it would be interesting to compete variants with different affini- ties under various in vitro and in vivo conditions. Receptor binding sites may also be strongly selected to avoid binding molecules similar to the host-cell receptor. For example, the nonim- mune component of horse serum attracts influenza particles that bind the α(2, 6) linkage of sialic acid (Matrosovich et al. Selection fa- vors equine influenza strains that both bind α(2, 3) linkages and avoid α(2, 6) linkages. By contrast, mucins of human lungs contain α(2, 3)- linked sialic acid, favoring human lineages that avoid the α(2, 3) linkage (Couceiro et al. Thus, host fluids or host tissues different from the primary infection target can cull viruses from circulation. The ki- netics of such fitness losses must be balanced against kinetic gains in receptor binding and avoidance of antibodies. The third fitness effect of surface substitutions arises from changes in antibody binding. A few studies have related different aspects of antibody-virus binding kinetics to the neutralization (killing) of viruses (Schofield et al.