Study Design notes.pdf

STUDY DESIGN: NOTES TO ACCOMPANY ONLINE LECTURES1 Randomised controlled trials Introduction A randomized controlled trial (RCT) is an intervention study where subjects are randomly allocated to treatment options. Randomized controlled trials (RCTs) are the accepted ‘gold standard’ of individual re...

STUDY DESIGN: NOTES TO ACCOMPANY ONLINE LECTURES1 Randomised controlled trials Introduction A randomized controlled trial (RCT) is an intervention study where subjects are randomly allocated to treatment options. Randomized controlled trials (RCTs) are the accepted ‘gold standard’ of individual research studies. They provide sound evidence about treatment efficacy which is only bettered when several RCTs are pooled in a meta-analysis. Choice of comparison group The choice of the comparison group affects how we interpret evidence from a trial. A comparison of an active agent with an inert substance or placebo is likely to give a more favourable result than comparison with another active agent. Comparison of an active agent against placebo when an existing active agent is available is generally regarded as unethical. Comparison with ‘usual care’ When an intervention is a programme of care, for example an integrated care pathway for the management of stroke, it is common practice for the comparison group to receive the usual care. Randomization in RCTs Why randomize? Randomization ensures that the subjects’ characteristics do not affect which treatment they receive. The allocation to treatment is unbiased In this way, the treatment groups are balanced by subject characteristics in the long run and differences between the groups in the trial outcome can be attributed as being caused by the treatments alone This provides a fair test of efficacy for the treatments which is not confounded by patient characteristics Randomisation makes blindness possible Randomizing between treatment groups The usual way to do random allocation is by using a computer programme based on random numbers. Blinding in RCTs Concealing the allocation Blinding is when the treatment allocation is concealed from either the subject or assessor or both It is done to avoid conscious or unconscious bias in reported outcomes A trial is double-blind if neither the subject nor the assessor knows which treatment is being given A trial is single blind if the treatment allocation is concealed from either the subject or the assessor but not both Note that randomization makes blindness possible and is its most important role 1 Examples A subject who knows that he is receiving a new treatment for pain which he expects to be beneficial may perceive or actually feel less pain than he would do if he thought he was receiving the old treatment. An assessor who knows that a subject is receiving the new steroid treatment for COPD which he expects to work better than the old one, may tend to round up measurements of lung function. If the treatment allocation is concealed, then both the patient and assessor will make unbiased assessments of the effects of the treatments being tested. Placebo An inert treatment which is indistinguishable from the active treatment In drug trials it is often possible to use a placebo drug for the control which looks and tastes exactly like the active drug The use of a placebo makes it possible for both the subject and assessor to be blinded When blinding is not possible In some situations blinding is not possible such as in trials of technologies where concealment is impossible. For example in trials comparing different types of ventilators, it is impossible to blind the clinician, and similarly in trials of surgery versus chemotherapy. Intention to treat analysis Introduction The statistical analysis of RCTs is straightforward where there are complete data. The primary analysis is a direct comparison of the treatment groups and this is performed with subjects being included in the group to which they were originally allocated. This is known as analysing according to the intention-to-treat (ITT) and is the only way in which there can be certainty about the balance of the treatment groups with respect to characteristics of the subjects. ITT analysis therefore provides an unbiased comparison of the treatments. Change of treatment If patients change treatment they should still be analyzed together with patients in their original, randomly allocated group since change of treatment may be related to the treatment itself. If patient’s data are analyzed as if they were in their new treatment group, the balance in patient characteristics which was present after random allocation will be lost. Sometimes a per-protocol analysis, where patients are analysed according to the treatment they have actually received, may be useful in addition to ITT analysis if some patients have stopped or changed treatment. 2 Example of RCT evaluating whether introducing allergenic food to babies at 3 months vs 6 months affects allergy2 ABSTRACT from published paper Background: We evaluated whether early introduction of allergenic foods into the diet of breastfed infants would protect against the development of food allergy. Methods: We randomly assigned 1303 exclusively breastfed three month old infants recruited from the general population to introduce six allergenic foods (peanut, cooked egg, cow’s milk, sesame, white fish and wheat) (early-introduction-group ) or to follow the United Kingdom recommendation of exclusive breastfeeding to around six months of age (standard-introduction-group). The primary outcome was the proportion of participants with food allergy to one or more of the six foods, by three years of age. Results: In the intention-to-treat analysis 7.1% (42/595) of the standard-introduction-group and 5.6% (32/567) of the early-introduction-group developed food allergy to one or more of the six intervention foods (p=0.32). In the per-protocol analysis there was a greater reduction in any food allergy of 7.3% versus 2.4% (p=0.01), for peanut allergy of 2.5% versus 0% (p=0.003) and for egg allergy 5.5% versus 1.4% (p=0.009) in the standard and early-introduction-groups respectively; there were no statistically significant effects for milk, sesame, fish or wheat. Two grams per week of peanut or egg white protein consumption was associated with less of these allergies respectively. Early introduction of all six foods was not easily achieved but was safe. Conclusion: The study did not show efficacy in an intention-to-treat analysis. Further analysis raises the question whether food allergy prevention through early introduction of multiple allergenic foods is dose- dependent.(Funded by the Food Standards Agency and the Medical Research Council and others; International Standard Randomized Controlled Trial Number Register number, 14254740.) Case-control studies Observational studies In observational studies the subjects receive no additional intervention beyond what would normally constitute usual care. Subjects are therefore observed in their natural state. Case-control study This study investigates causes of disease, or factors associated with a condition It starts with the disease (or condition) of interest and selects patients with that disease for inclusion, the ‘cases’ A comparison group without the disease is then selected, ‘controls’, and cases and controls are compared to identify possible causal factors Case-control studies are usually retrospective in that the data relating to risk factors are collected after the disease has been identified. This has consequences which are discussed below Limitations of design The choice of control group affects the comparisons between cases and controls Exposure to risk factor data is usually collected retrospectively and may be incomplete, inaccurate or biased 3 Example of case control study: Genitourinary infections in the month before conception to the end of the first trimester, and gastroschisis A recent study investigated the association between genitourinary infections in the month before conception to the end of the first trimester, and gastroschisis. Subjects were 505 babies with gastroschisis (the ‘cases’), and 4924 healthy liveborn infants as controls. The study reported data (table below) showing a positive relationship between exposure to genitourinary infections and gastroschisis: odds ratio = 2.02 (95% CI 1.54, 2.63). ________________________________________________________ Exposed to infection? Cases Controls ________________________________________________________ Yes 81/505 (16%) 425/4924 (9%) No 424/505 (84%) 4499/4924 (91%) Cohort studies A cohort study is an observational study that aims to investigate causes of disease or factors related to a condition but, unlike a case-control study, it is longitudinal and starts with an unselected group of individuals who are followed up for a set period of time. Cohort studies are sometimes used to confirm the findings of case-control studies such as happened when Doll and Hill observed a relationship between smoking and lung cancer in a case-control study and subsequently established the longitudinal study of doctors in the UK. Design of a cohort study This starts with an unselected group of ‘healthy’ individuals The subjects are followed up to monitor the disease or condition of interest and potential risk factors The length of follow up is chosen to allow sufficient subjects to get the disease and risk factors to be explored In the simplest case, where there is a single risk factor that is either present or absent, the incidence of disease can be related directly to the presence of the risk factor Usually prospective, with the risk factor data being recorded before the disease is confirmed Can be retrospective but requires that full risk factor data is obtained on all individuals with and without the disease of interest using data which was recorded prospectively 4 Difficulties with cohort studies A large number subjects is needed to obtain enough individuals who get the disease or condition, particularly if it is uncommon The length of follow up may be substantial to get enough diseased individuals and so the cohort study is not feasible for rare diseases There is difficulty in maintaining contact with subjects, particularly if the follow-up is lengthy The resources required may be very high Example of a cohort study A cohort study examined the relationship between BMI and all-cause mortality in 527,265 U.S. men and women in the National Institutes of Health–AARP cohort who were 50 to 71 years old at enrollment in 1995–199623. BMI was calculated from self-reported weight and height. The study found that among those who had never smoked, excess body weight during midlife was associated with a higher risk of death. The table gives results for men who had never smoked. __________________________________ BMI at age 50 Relative risk __________________________________