In many cases investigators may be faced with a situation in which they have a potentially large historical control sample that they want to compare with a small experimental sample in terms of one or more endpoints. This is typically a problem in observational studies in which the individuals have not been randomized to the control and experimental groups. The question is, how does one control for the bias inherent in the observational nature of these data?
Perhaps the experimental participants have in some way been self-selected for their illness or the intervention that they have received. This is not a new issue. In fact, it is closely related to statistical thinking and research on analysis of observational data and causal inference. For example, as early as , William G.
Cochran considered the use of stratification and subclassification as a tool for removing bias in observational studies. In a now classic example, Cochran examined the relationship between mortality and smoking using data from a large medical database Cochran, The first row of Table shows that cigarette smoking is unrelated to mortality, but pipe smoking appears to be quite lethal.
The result of this early datamining exercise could have easily misled researchers for some time at the. It turns out that, at least at the time that these data were collected, pipe smokers were on average much older than cigarette smokers, hence the false association with an increased rate of mortality in the non-stratified group.
Cochran illustrated the effect that stratification i. It might be argued that a good data analyst would never have made this mistake because such an analyst would have tested for relevant interactions with important variables such as age. However, the simple statistical solution to this problem can also be misleading in an analysis of observational data.
tax-marusa.com/order/susijod/hacker-un-iphone-8-plus-vol.php For example, nothing in the statistical output alerts the analyst to a potential nonoverlap in the marginal distributions. An investigator may be comparing year-old smokers with year-old nonsmokers, whereas traditional statistical approaches assume that the groups have the same covariate distributions and the statistical analyses are often limited to linear adjustments and extrapolation. Cochran illustrated that some statistical approaches e.
Rosenbaum and Rubin extended the notion of subclassification to the multivariate case i.
In February , a draft guidance on adaptive design clinical trials by . trial utilizing a two-stage seamless adaptive design for evaluation of. The adaptive design trials are proposed to boost clinical research by cutting on .. J. FDA introduction comments: Clinical studies design and evaluation issues.
Propensity score matching allows the matching of cases and controls in terms of their propensities or probabilities of receiving the intervention on the basis of a number of potentially confounding variables. The result is a matched set of cases and controls that are, in terms of probability, equally likely to have received the treatment. The limitation is that the results from such a comparison will be.
In randomized experiments, ignoring important covariates increases the standard errors of the estimates.
By contrast, in observational studies bias can result and the standard errors can be underestimated, leading to an opportunity for a chance association and potentially misleading results. Such problems become more complex as the number of potential outcome variables increase beyond one.
Investigators in clinical trials use the method of masking or blinding , in which neither the participant nor the physician, investigator, or evaluator knows who is assigned to the placebo or control group and who will receive the experimental intervention. The purpose of masking is to minimize the occurrences of conscious and unconscious biases in the conduct of a clinical trial and in the interpretation of its results Pocock, The knowledge of whether a participant is receiving the intervention under study or is in the control group may have an effect on several aspects of a study, including the recruitment and allocation of participants, their subsequent care, the attitudes of the study participants toward the interventions, the assessment of outcomes, the handling of withdrawals, and the exclusion of data from analysis.
The essential aim of masking is to prevent identification of the interventions that individuals are receiving until all opportunities for biases have passed Pocock, Many randomized trials that have not used appropriate levels of masking show larger treatment effects than blinded studies Day and Altman, In a double-blind trial, neither the participants nor the research or medical staff responsible for the management or clinical evaluation of the individuals knows who is receiving the experimental intervention and who is in the control group. To achieve this, the interventions being compared during the trial must be disguised so that they cannot be distinguished in any way e.
Double-blind trials are thought to produce more objective results, because the expectations of the investigators and participants about the experimental intervention do not affect the outcome of the trial. Although a double-blind study is ideal for the minimization of bias in clinical trials, use of such a study design may not always be feasible.
The interventions may be so different that it is not possible to disguise one from. If sham surgery would be necessary to maintain blinding, ethical problems associated with the use of sham surgery may proscribe the use of a double-blind design. Two drugs may have different forms e. One way to design a double-blind trial in this instance is to use a double-dummy technique e.
An alternative design when a double-blind trial is not feasible is the single-blind trial. In a single blind trial the investigators and their colleagues are aware of the intervention but the research participant is not. When blinding is not feasible, an open-label trial, in which the identity of the intervention is known to both the investigator and the participants, is used.
One way to reduce bias in single blind and open-label trials is for those who conduct all clinical assessments to remain blinded to the assignment of interventions. In single-blind or open-label trials, it is important to place extra emphasis on the minimization of the various known sources of bias as much as possible.
Randomization is the process of assigning participants to intervention regimens by using a mechanism of allocation by chance. Random allocation for the comparison of different interventions has been a mainstay of experimental designs since the pioneering work of Ronald A. Fisher conducted randomized experiments in agriculture in which the experimental units were plots of land to which various crops and fertilizers were assigned in a random arrangement Fisher, Randomization guards against the use of judgment or systematic arrangements that would lead to biased results.
Randomization introduces a deliberate element of chance into the assignment of interventions to participants and therefore is intended to provide a sound statistical basis for the evaluation of the effects of the intervention Pocock, In clinical research, randomization protects against selection bias in treatment assignment and minimizes the differences among groups by optimizing the likelihood of equally distributing people with particular characteristics to the intervention and control arms of a trial.
In randomized experiments, ignoring important covariates, which can lead to differences between the groups, simply increases the standard errors; however, in observational studies, bias can result and the standard errors are underestimated. There are several different randomization methods Friedman, Furberg, and DeMets, Some of these procedures are designed to ensure balance among intervention groups with respect to important prognostic factors, and thus, the probability of assignment to a particular intervention may change over the course of the trial.
Thus, randomization does not always imply that an individual participant has a 50 percent chance of being assigned to a particular intervention. Clinical trials can use either randomized controls or nonrandomized controls.
In a trial with nonrandomized controls, the choice of intervention group and control group is decided deliberately. For example, patients with a specific disease characteristic are assigned to the experimental intervention, whereas those with another disease characteristic are assigned to the control arm. On scientific grounds it is easy to conclude that the use of a randomized control group is always preferred. The consensus view among clinical investigators is that, in general, the use of nonrandomized controls can result in biased and unreliable results Pocock, Randomization in combination with masking helps to avoid possible bias in the selection of participants, their assignment to an intervention or control, and the analysis of their response to the intervention.
The health outcomes assessed are pivotal for both the scientific and substantive credibilities of all trials—and are even more pivotal for small trials. The selection of outcomes should meet the guidelines for validity Tugwell and Bombardier, In psychology, the concepts of validity and reliability have been developed with the view that measurement is mainly done to discriminate between states and to prognosticate from a single measurement. For example, an intelligence test can be administered to children at the end of their primary school years to suggest the needed level of secondary education.
In clinical trials, however, measurement of change e. Thus, the concept of responsiveness or sensitivity to change becomes important, but its nomenclature and methodology have not been well developed. In the selection of outcome measures, validity is not the only issue—feasibility also determines which of the valid outcome measures can actually be applied. The most important criteria for selecting an endpoint include truth, discrimination and feasibility Boers, Brooks, Strand, et al.
Truth captures issues of fact, content, construct, and criterion validity. For example, is the measure truthful, does it measure what is intended? Is the result unbiased and relevant? Discrimination captures issues of reliability and responsiveness or sensitivity to change. For example, does the measure discriminate between situations of interest?
The situations can be states at one time for classification or prognosis or states at different times to measure change. Feasibility captures an essential element in the selection of measures, one that may be decisive in determining a measure's success. For example, can the measure be applied easily, given constraints of time, money, and interpretability?
Any clinical trial design requires precision in the process by which participants are determined to be eligible for inclusion. The objective is to ensure that participants in a clinical trial are representative of some future class of patients or individuals to whom the trial's findings might be applied Pocock, In the early phases of clinical trial development, research participants are often selected from a small subgroup of the population in which the intervention might eventually be used. This is done to maximize the chance of observing the specific clinical effects of interest.
In these early stages it is sometimes necessary to compromise and study a somewhat less representative group Pocock, Similarly, preliminary data collected from one population e. A standard approach asks five questions:. How small a treatment difference is it important to detect, and with what degree of certainty should that treatment difference be demonstrated? Statistical methods can then be developed around qualitative or quantitative outcomes. A critical aspect of trial design is to first make use of statistical methods to determine the population size needed to determine the feasibility of the clinical trial.
The number of participants in a clinical trial should always be large enough to provide a sufficiently precise answer to the question posed, but it should also be the minimum necessary to achieve this aim. A trial with only a small number of participants carries a considerable risk of failing to demonstrate a treatment difference when one is really present Type II error see the Glossary for explanations of Type I and Type II errors.
In general, small studies are more prone to variability and thus are likely to be able to detect only large intervention effects with adequate statistical power. Variance is a measure of the dispersion or variation of data within a population distribution. In the example of the effects of microgravity on bone mineral density loss during space travel see Box , there is a tendency to assume that the astronaut is the unit of analysis and hence to focus on components of variance across astronauts.
In this case, it becomes important to consider the other components of variance in addition to the among-person variance. In a study of bone mineral density loss among astronauts, the components of variance may include:.
It is reasonable to focus on true trends for a particular astronaut over time, which requires careful repeated measurements over time and which makes relevant the component of variance within a person rather than the component of variance among persons. Significance tests e. However, statistical significance is not the same as clinical or societal significance. Clinical or societal significance relevance must be assessed in terms of whether the magnitude of the observed effect is meaningful in the context of established clinical practice or public health. An increase of risk from 1 in 10 to 2 in 10 has a clinical implication different from that of an increase of 1 in 10, to 2 in 10,, even though the risk has doubled in each case.
In hypothesis testing, the null hypothesis and one's confidence in either its validation or refute are the issue:. The basic overall principle is that the researcher's theory is considered false until demonstrated beyond reasonable doubt to be true This is expressed as an assumption that the null hypothesis, the contradiction of the researcher's theory, is true A statistical test defines a rule that, when applied to the data, determines whether the null hypothesis can be rejected Both the significance level and the power of the test are derived by calculating with what probability a positive verdict would be obtained the null hypothesis rejected if the same trial were run over and over again Kraemer and Thiemann, , pp.
A clinical trial is often formulated as a hypothesis as to whether an experimental therapy is effective. However, confidence intervals may provide a better indication of the level of uncertainty. In the clinical trial setting, the hypothesis test is natural, because the goal is to determine whether an experimental therapy should be used. In clinical trials, confidence intervals are used in the same manner as hypothesis tests.
Thus, if the interval includes the null hypothesis, one concludes that the experimental therapy has not proved to be more effective than the control. To obtain power, repeat tests are done when the alternative hypothesis is correct. To compute power, the researcher must have developed from preliminary data a critical-effect size, that is, a measure of how strong the theory must minimally be to be important to the individual being offered the therapy or important to society Kraemer and Thiemann, , p. Changing designs or measures used or choosing one valid test over another changes the definition of effect size.
Moreover, the critical-effect size is individual- or population-specific as well as measurement-specific Kraemer and Thiemann, Modern clinical trials go back more than 40 years, and a wide variety of clinical trial designs have been developed and adapted over the past 25 years.