Clinical trials, field trials, community trials and intervention studies

Experimental studies: Clinical trials, field trials, community trials, and intervention studies Barrie M Margetts and Ian L Rouse      Experimental studies differ from observational studies described /reported rather than simply to observe, the exposure of interest There are many different approaches used in experimental studies, from very tightly controlled laboratory experiments to large scale community intervention Experimental studies either focus on assessing change at the level of the individual or the group The most important aspect of experimental studies, no matter what study group is used., is to ensure that the allocation of the study group to the different treatments/ interventions / exposures under investigation is done randomly The development of the research protocol will then focus primarily on how to measure the effect of an exposure on an outcome with consideration of the effects of other factors (potential confounders as well as factors related to the efficacy of the delivery of the intervention) 16-1 Historical introduction Lind and Louis are two notable workers who used experimentation to attempt objectively to assess the effect of a treatment on a disease  In 1753, Lind described an experiment in which 12 sailors with scurvy were put on the same standardized diets, and then allocated to one of six treatment groups for 14 days →Those receiving oranges and lemons were much improved after six days   →His study must be considered one of the first controlled clinical trials  In 1834, Louis articulated further guidance to follow regarding study design   – the number of subjects required to show benefit of one treatment over another; (sample size) – The need to observe disease progress accurately in treated and controlled groups; (end point) – the need to define precisely disease state before the experiment; (inclusion criteria) – and the importance of observing deviations from intended treatments         The first randomized controlled trials were undertaken until the later 1940s by the Medical Research Council →These were trials of streptomycin in the treatment of pulmonary tuberculosis (1948) →These were trials of antihistamines for the treatment of the common cold (1950) double-blind, placebo-controlled trial In 1950, Cochran and Cox published an important textbook on experimental designs →This book was clearly and simply described the major statistical consideration relevant for experimental studies Bradford-Hill was also an important force in making the design of clinical trials more rigorous (Principles of medical statistics) In 1959, Truelove summarized the current thinking on experimental design where he clearly described the essential elements of a therapeutic trial as follows: The trial should be planned so that decisive answers can be given to one or more important questions (internal validity) Patients should be selected for inclusion in the trial before it is known into which group they will go After admission to the trial, patients should be allocated at random to one or other treatment group Systematic and pertinent observations should be made on patients so that relevant data are available for analysis at the conclusion of the trial When possible, trials should be so arranged that neither the physician nor the patient knows which treatment is being used - the so-called doubleblind system 16.2 Definitions of experimental studies    In broad terms there are two major types of experimental study Those where the unit of measurement and exposure is: the individual the population Individual-based experimental studies are sometimes subdivided on the basis of the level of the outcome as clinical trials (or therapeutic, secondary, or tertiary prevention trials), and field trials (primary prevention trials) where the subjects not have any defined level of outcome which may be classified as disease In addition, a third group of individual-based studies are called intervention studies, where the individuals who have the outcome of interest above a certain level at baseline are excluded (in a trial of vitamin A supplementation children with xerophthalmia are excluded) Experimental studies in whole populations (communities) are usually referred to as community trials or community intervention studies  Community trials focuses on mass education campaigns aimed at changing people’s knowledge and attitudes  Community intervention studies, the exposure is usually given to subjects (for example, by vector control to reduce malaria, pit latrines for clean water), or to reduce work load and/or to increase disposable income  These community intervention studies have also been characterized as: (1)explicitly nutritional (a) nutrition oriented food programs (b) feeding programs (c) weaning foods (d) fortification (e) nutrition education; (2) implicitly nutritional (a) health related, e.g immunization, sanitation; (b) economic, e.g income generation or substitution; (c) labor-saving, e.g cereal mills; (3) integrated  combinations of (1) and (2) above  效效  This character combination means effect, effectiveness,efficacy, and result – Meaning from the first character, which can mean efficacy, efficiency, merit, or benefit – Meaning from the second character, which can mean carry out, complete, end, finish, fruit, achieve, reward, or succeed        Experimental studied could include changes in knowledge, attitudes, or behavior (such as eating patterns) The outcome variable may be changed in a continuously distributed variable such as blood pressure or serum cholesterol or blood glucose, or changes in incidence or mortality from specific diseases or risk factors such as obesity, low birth weight babies, or hypertension (all derived from continuous variables) The outcome may be measured in individuals (clinical trials) or groups/populations (community intervention trials) Irrespective of the disease state or outcome measure being investigated, all subjects or groups should be measured in the same way, and allocation to treatment (exposure) groups should not be influenced by the disease state or level of the outcome measure of the subjects or groups in the study All eligible subjects or groups should be randomly allocated to treatments Whatever the type of study, the main objective is to explore an exposure-outcome cause-effect) relationship free from bias Table 16.3 summarizes the design of a selection of different experimental studies          16.3.6 Compliance They respect to participant effects relates to compliance Deviation from the protocol needs to be documented in all subjects, not just those on the treatment It may be that a comparison or control group alters their behavior so as to make them more like the treatment group in their exposure status Perhaps more commonly, participants will forget or deliberately fail to take drugs, or, if they have been placed on a dietary regime, they may occasionally 'break-out' and deviate from the protocol Measurement of compliance is essential in any clinical (dietary) trial The study must be designed so that all variables of importance can be measured during the trial with sufficient precision to give a sensitive and specific (valid) indication of the level of each variable Where possible, an independent measure of compliance should be used For example, measuring changes in the levels of fatty acids in serum, red blood cells, or a fat biopsy enables the researcher to assess the compliance with dietary advice to alter fat intake If a dietary intervention aims to increase fiber intake, it may be possible to include in the fiber diet a marker which can subsequently be measured in fecal samples The level in the fecal sample may give an indication of the amount of fiber supplement eaten From our experience it is helpful to tell participants that we are checking their compliance by taking blood or urine samples      It may be more difficult to measure individual compliance in a community trial, but by random sampling of subjects within each study community, it should be possible to measure at least whether subjects are aware of the community intervention and whether it has had any effect on their knowledge, attitudes, behavior, or levels of some outcome variables The efficiency of the treatment as measured by changes in community rates of disease may be adequate Not measuring change in levels of the exposure which was supposed to be changed in the study may lead to a false impression of the effect of the exposure on the outcome (either positive or negative) The use of a run-in or familiarization period may improve compliance It gives the subjects time to adjust to the rigors of the study protocol However, the diet being fed during this period should not have any effect on the outcome measure In theory it should be similar to the subject's usual diet If the trial is for a therapeutic agent, the run-in period should only use a placebo or usual care treatment The run-in period should be before randomization, so that any drop-outs which occur during this period not affect the internal validity of the study There are situations where it may not be possible or appropriate to have a run-in period For example, where the experiment is assessing the effect of treatment following an acute event         16.3.7 Ascertainment of exposure and outcome The aim of an experiment is to assess the effect of a defined change in exposure on the outcome of interest To assess whether the change in exposure has affected the outcome requires that some measure of each can be obtained to confirm the changes For studies where subjects are given advice as to how to change their diet, a measure of compliance with this advice is required; this will usually require an accurate assessment of an individual’s intake Irrespective of the measures require to assess exposure and outcome ,the protocol should be administered in the same way in all subjects or groups included; it is not acceptable to use different measures of exposure and outcome in intervention and control groups Ascertainment of exposure If diet is measured poorly, it may be impossible to detect the desired change in exposure which the study has sought, and the study may wrongly conclude that the subject's diet did not change significantly as a result of the intervention Studies aimed at achieving dietary change by giving people dietary advice, but without measuring diet, and where the advice has not lead to statistically significant change in the outcome measure, are open to the criticism that the reason the advice did not lead to change in the outcome measure was because the advice did not achieve the desired change in diet (as well as concerns about statistical power and length of follow-up)      For studies aiming to change people's behavior by changing people's knowledge, there is a need to consider the complex series of steps involved in going from knowledge to attitudes to behavior Intervention studies seeking to change behaviors by dietary advice have measured psychological factors related to the subjects' readiness to change (transtheoretical models of behavior in general, and in relation to change in fat intake) and taken this into account when exploring the outcome measure Because of the limitations of assessing dietary intakes by subject-based recording methods, alternative methods of assessing intake have been sought There is little point in precisely measuring, for example, a blood or urinary constituent that is not involved in or affected by the exposure of interest Potentially important confounding factors should also be measured during the study; the effects of measurement error or misclassification need to be considered when selecting the method for measuring the confounding factor  Ascertainment of outcomes  Outcomes in experimental studies are measured in the same way as in a cohort study The outcome measures may be routinely collected data sources (death certificates or hospital activity / medical records), may be collected by the participants themselves (by completion of a questionnaire), or may be collected by investigator(by personal interviewer or medical examination) The outcome of the study is dependent on the completeness and validity of the information obtained Where routinely collected data are to be used to measure outcome, it must be possible to ascertain for all subjects whether they have died or been admitted to hospital It may be relatively simple to determine vitaI status and obtain a death certificate where there is a central registry of deaths It may be much more difficult to obtain complete hospital admission data in the absence of a suitable computerized system          If the investigator finds that a subject has died or had an event of interest (in a hospital or elsewhere), they are then reliant upon the accurate ascertainment, (usually by some other person) of the cause of death or clinical details related to the hospital admission Where general practitioner’s records are to be used, the investigator must also be assured that subjects only attend that practice and that if an illness occurs they go to the same practitioner The more subjects and information lost to follow-up, the more likely that a biased result will occur Where outcome measures are obtained either by self-report or observer measurement, it is essential that information is obtained in the same way for all subjects Any under-ascertainment of out come will effect the validity of the study Observer blindness will reduce the risk of ascertainment bias and will also ensure at follow-up procedures to obtain outcome will not influenced differentially in treatment groups It is also essential that the measurement of outcome is precise enough to categorize subjects correctly The is no substitute, in designing an experiment with accurate ascertainment of outcome, to having a clear understanding of the biological process under investigation and potential errors associated with the outcome measure     16.3.8 Statistical power /sample size To estimate the statistical power for clinical trials, the investigator needs to be able to estimate the likely random errors in the measurements being used and the number of events or changes in an outcome measure to be expected The investigator also needs to specify the acceptable level of statistical significance and confidence The statistic power of community trials relates to the number of communities, not the number of individuals, in each group The power of community trials can be increased by matching intervention and control centres, stratifying on a baseline variable which is strongly related to the outcome, and also by increasing the number of times a community is observed         16.4 Analysis and interpretation This will ensure both that there are sufficient subjects available in subjects of the sample and that the data are collected in a way that is appropriate for the required analysis In general, the correct estimate of the effect of the intervention will be the difference in the change from baseline in the intervention compared with the control group, irrespective of the exposure or outcome measure The statistical significance can be expressed using the 95% confidence interval around the mean difference In clinical trials, with data collected at the level of the individual, the change from baseline can be measured for each subject and the average change assessed for all subjects For community trial, the analysis will be of the change in the population incidence or mortality Where measuring the outcome of interest (e.g.blood pressure) may itself influence the measurement, and may therefore be considered as an intervention in its own right, some researchers have argued that it is not appropriate to make this measurement at baseline In this situation, the analysis simply measures the difference between groups in the outcome at the end of the study, and assumes that because of randomization baseline differences will not affect the final differences seen       There are two major approaches to the consideration of the subjects in the analysis of the data for clinical trials One view is that once subjects have been randomly allocated to treatment groups they should be included in the analysis irrespective of whether their compliance was good or bad or whether they dropped out or not →This is sometimes referred to as analyzing on an ‘intention to treat’ basis Excluding subjects who have ‘measured’ compliance below a certain level is arbitrary and may give optimistically positive results A second view would argue that if the aim of the study was simply to demonstrate that a treatment can effect an outcome, then it may be acceptable to use a restricted subset (on the basis of compliance) of the data If this approach is taken, consideration must be given to the effect that breaking the balanced group allocation may have on any comparisons It may be that those who comply sufficiently well to included are either different in other important characteristics from those not adequately complying and/or the distribution of those characteristics may be different in treatment and control groups This latter question is more relevant to public health issues, where the investigator wants to know whether the treatment works in the community For more detailed consideration on statistical analysis readers are referred to other texts      16.5 Designs used in experimental studies A basic premise for all experimental studies is that the effect of any treatment on an outcome must be compared with the effect of a control treatment on outcome Uncontrolled studies of any design are very difficult to interpret →For example, in trials measuring blood pressure as the outcome, it is very common to see blood pressures falling in all groups throughout the study; without a control group it would be impossible to separate out the effect of this treatment from the general ‘familiarization’ effect In both the Stanford Five Town Study and the Minnesota Heart Health Program, there were secular trends both in the exposure and the outcome measures; without control communities, the real magnitude of the effect of the intervention would have included the secular trend plus the effect of the intervention There are two approaches used in allocating treatment and control regimes: either parallel or crossover →A parallel design is where subjects receive only one treatment and the change in outcome response in one group of subjects (receiving treatment of interest) is compared with that in another group of subjects receiving a different (or control) treatment → In a crossover design each subject receives both (all) treatments in a randomized order with suitable gaps between treatments (wash-out) and outcomes/ response is compared within subjects      An advantage of the crossover design over the parallel design 1.Subject characteristics are approximately constant for both treatment groups (exposure categories) 2.A crossover design is that, as all subjects receive the treatment under investigation, 3.The statistical power of the study is greater than in a parallel study of equivalent size, where only a proportion of the subjects receive the treatment under investigation 4.A crossover design may be suitable for a single-dose treatment of a micronutrient, but may not be suitable where the treatment is given continuously throughout the treatment period In a factorial experimental design, the effects of a number of different factors can be investigated at the same time The advantage : 1.more effective 2.cost-effective It may, however, be considerably more difficult for the researcher to keep control of the study and generally factorial designs are limited to only two factors →The basic design may be parallel or crossover The treatments are formed by all possible combinations that can be formed from the different factors →For example,Burr and colleagues assessed the effects of both a high fiber diet and a high fish oil diet on recurrence of infarction        In a crossover design, the response in period two will be a combination of the effect of the second treatment and an additional residual effect of treatment in the first period Some investigators use a wash-out period between treatments to minimize this carry over effect This may only be likely to occur for studies which assess the acute effects of feeding different diets on, for example, blood glucose levels It is hard to imagine many dietary based experiments aimed at assessing the effects of changing people's diets on risk factors such as lipids or blood pressure, or longer-term measures such as morbidity or mortality, where a crossover design would be free from the potential effects of carry over Senn has recently argued that it is virtually impossible to be sure that carry over effects are not present Balaam has suggested optimal designs which can be used to assess the effects of carry over, without the use of a wash-out period: these include groups of subjects with all combinations of treatments and in different periods →For example, in a two treatment design (A or B) subjects are randomly allocated to one of four groups: AA, BB, AB, BA The responses in each group are then compared For long-term trials, the outcome measure may alter during the study Disease status may progress, regress, or have a cyclical pattern of response If the subjects have been randomly allocated to groups (or blocked on disease state if disease state is considered important), these period effects are not likely to lead to a systematically biased outcome      16.6 Systematic reviews of clinical trials—the role of metaanalysis Over the last few years there has been a massive collaborative effort to bring together all the randomized controlled trial data from all over the world The Cochrane Collaboration has enabled researchers to meta-analysis of pooled data from many different studies These pooled analyses provide pooled estimates of effect with much smaller confidence intervals and provide more reliable estimates of the likely effect Some caution is required in the use of meta-analysis, particularly to ensure that all relevant studies have been included (no publication bias), that it is appropriate to pool data from different studies, and that the correct statistical methods are used        16.7 Concluding remarks A properly controlled randomized experiment offers the best test of causality If properly conducted it is less likely to give a biased estimate of the effect of an exposure on an outcome →Measurement error and the effects of confounding variables may still affect the outcome A clearly defined aim for the research is essential and establishes the structure for the research protocol The design used needs to be appropriate to the research question and population under study For clinical and field trials, all subjects, once included in the study, should be observed and followed-up in exactly the same way →Poor compliance, subjects dropping out, and incomplete ascertainment of outcome seriously affect the validity of the study →There should be sufficient subjects, observed for an adequate period of time, included in the study to allow appropriate analyses to be conducted For community trials or community intervention studies, there is the same need to pay close attention to the design of the study ... xerophthalmia are excluded) Experimental studies in whole populations (communities) are usually referred to as community trials or community intervention studies  Community trials focuses on mass education... essential and establishes the structure for the research protocol The design used needs to be appropriate to the research question and population under study For clinical and field trials, all... in a community trial, but by random sampling of subjects within each study community, it should be possible to measure at least whether subjects are aware of the community intervention and whether