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Research Objective, Study Design and Statistical Approach

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Research Objective, Study Design and Statistical Approach Why are we here? Intro to study design Intro to biostatistics Research objective, study design and statistical approach Assoc. Prof. Cameron Hurst [email protected] QIMR Berghofer Medical Research Insitute, Queensland, Australia 2nd November, 2020 1/28 Why are we here? Intro to study design Intro to biostatistics Good scientific research Before I go further I want to talk about what represents strong research (i.e. a manuscript likely to be accepted in a high impact journal). Strong research is: 1 Scientifically significant: It addresses an important research question that clearly needs answering 2 It has high scientific quality: We believe the findings presented. In other words, the paper provides strong quality of evidence As a methodologist, it is this second issue that interests me. It's also the only one we have any real power over, anyway. Also, to the expert, the first issue: scientific importance (and novelty), should be obvious. 2/28 Why are we here? Intro to study design Intro to biostatistics Scientific significance vs scientific quality To get the point across about scientific significance and scientific quality I would like to give you an example of two different studies. Study 1: Prevalence of blood sugar control in rural Type 2 diabetes (T2D) outpatients: A retrospective cross-sectional study Single-center (a community hospital) involving 120 patients Record glycated hemoglobin (HbA1c) along with a some patient socio-demographic factors from medical records In our sample, approx. 34% of patients had HbA1c ≤ 7% Note: Around the world (other studies), Type 2 diabetes patient blood sugar control ranges in prevalence from about 30% to %40 1. Important/novel?? ... 2. Strength of evidence?? 3/28 Why are we here? Intro to study design Intro to biostatistics Scientific significance vs scientific quality Study 2: Impact of a 'patient empowerment program' on blood sugar control in T2D patients: A cluster randomized implementation trial A study involving 25 Diabetes clinics from across the country Implementation trial of a 'patient empowerment' intervention that seeks to enhance patient self-management through improving patient diabetes management self-efficacy Randomized implementation sequence for centers using a stepped-wedge design for this prospectively powered study Results demonstrated that Blood sugar control went from 35% (at pre-intervention) to 53% post-intervention (diff=18%, 95%CI : 13.8%, 22.2%, p < 0:0001) Recommend that all diabetes clinics (nationally) adopt this program for their outpatients. 1. Important/novel?? ... 2. Strength of evidence?? 4/28 Why are we here? Intro to study design Intro to biostatistics Scientific quality: The quality of evidence Now let's think about the quality of evidence. Can we believe: The results are generalizable: Does the sample represent a sufficiently representative and broad subgroup of the target population that the findings are important for others The results are reproducible: Is the sample sufficently large and diverse that we would be see similar results if study repeated Results were unbiased: Was sufficient care taken in the study design to minimize bias. If it was experimental, was there randomization, blinding? Were there problems with patient compliance? If there is confounding bias (e.g. observational study), were appropriate statistical models employed that may control this bias? 5/28 Why are we here? Intro to study design Intro to biostatistics The idea of Quality of Evidence As an epidemiologist, I like to think of quality of evidence issues in the same way as we think about risk factors for a disease. Some risk factors are non-modifiable (there is nothing we can do about them), and some risk factors are modifiable. For example: Non-modifiable risk factors include things like genetics, gender and ethnicity; and Modifiable risk factors include factors like smoking, BMI, dietry salt intake, etc. In a similar way, it is useful to think about quality of evidence issues in clinical research as either non-modifiable (forgivable) or modifiable (unforgivable). Design issues that are forgivable should be acknowledged (limitations section), and those that are unforgivable should be designed/modeled out. 6/28 Why are we here? Intro to study design Intro to biostatistics Enter "Clinical epidemiology and Biostatistics" So, why am I here today??? Am I here to simply inflict another painful biostatistics lecture on you? Answer: No! I promise I won't mention even one statistical model! INSTEAD, I would like to emphasize the importance of clinical epidemiology and biostatistics in the health sciences. I would like to show you how it can help you move from mediocre research, to producing (concieving, designing, conducting and disseminating) strong clinical research. 7/28 Why are we here? Intro to study design Intro to biostatistics Epidemiology Vs Biostatistics The disciplines of Epidemiology and Biostatistics are inextricably related. They both relate to the 'research' end of the health discipline ...as such, a basis in epi and biostats is essential for health and (bio)medical researchers Definition: Epidemiology is the study of the distribution and determinants of disease, health, or injury outcomes in human populations and the use of the knowledge we gain (from this study) to control these health problems Clinical epidemiology focuses on those who are already sick (i.e. clinical populations) ....which can make it considerably easier to collect representative samples 8/28 Why are we here? Intro to study design Intro to biostatistics Another definition of Clinical epidemiology (in practice) In practice, clinical epidemiology is about the generic research methods (regardless of clinical sub-discipline) we use to produce strong research, that will we hope will (eventually) makes it's way into clinical practice or policy (and improve patient outcomes); evidence-based medicine. So all we need to do now is produce strong research.....EASY? But how do we do this? So (again) let's reiterate what is meant by strong research? 9/28 Why are we here? Intro to study design Intro to biostatistics The 'strong' study So a reminder ... A strong study is one that: 1 is Contextually (i.e. clinically) significant: That is, it needs to address an important research question (one that fills a gap in our knowledge). If a paper does not address an interesting issue, readers (and therefore, a good journal) is unlikely to be interested in it. 2 has High scientific quality: It is a well conceived study, with an effective study design, an appropriate analysis, AND all of this is then packaged into a well written paper. 10/28 Why are we here? Intro to study design Intro to biostatistics Yet another definition of Clinical Epidemiology With this in mind, I would like to put forward another PRACTICAL definition of Clinical epidemiology. Clinical epidemiology ) Generic clinical research skills Clinical epidemiology is about the processes and methods underpinning clinical research practice and has four main components: 1 Problem formulation (relates to the clinical science) 2 Design (epidemiology) 3 Methods (biostatistics) 4 Dissemination (academic research communication skills) This is to be distinguished from the clinical sciences (e.g. nephrology, opthalmolgy etc); areas of clinical expertise. 11/28 Why are we here? Intro to study design Intro to biostatistics A bit of Epi101: Study design We will start by examining some of the (classical) health and medical study designs The main factors that govern our choice of study design are the: 1. research question, 2. target population (and our ability to sample it) and (correspondingly) 3. our ability to control sources of bias Fortunately (for you) most clinical studies (especially those involving a 'treatment') tend to be up the stronger end of health study designs (more on this later) Study designs in clinical studies: Design vs analytical approach Some clinical studies (especially clinical trials) try to control sources of bias through design, allowing the use of simple statistical analysis. Where we can't do this, we need to use more sophisticated statistical approaches 12/28 Why are we here? Intro to study design Intro to biostatistics Health and medical study designs (epi101) Study design Randomized controlled trials Non-randomized trials Cohort Cross-sectional Case-control Ecological Case series Case study 13/28 Why are we here? Intro to study design Intro to biostatistics Randomized controlled trials Widely considered to be the gold standard of study designs Provide the strongest evidence Double blinding and randomization attempt to minimize selection and confounding bias As a true experiment, RCTs always involve an intervention (or treatment) that we (as the researcher) impose on the patients That is, experimental manipulation by the researcher Often not possible in epidemiological studies, especially those involving the study of risk factors (unethical to impose a protocol of risky behaviour) 14/28 Why are we here? Intro to study design Intro to biostatistics Non-randomized trials Many study don't (or can't) include a randomization process, but still involve an intervention For example, it may be unethical or impractical to randomize patients, or it might be impossible to avoid contamination between different experimental arms Example might be comparing the different treatments used at two different hospitals This is called a 'non-equivelent group design' Patients are not randomized to the hospitals and may differ in many ways (e.g demographics, health coverage, exposure etc) What problems might this cause??? Important to realize that like RCTs, NRTs are prospective. 15/28 Why are we here? Intro to study design Intro to biostatistics Cohort studies Cohort studies are considered the strongest (in terms of evidence) of the observational studies. Observational designs: Researcher has no control over group membership ('outsider' who can only observe what happens) Their main strength (above something like a cross-sectional study) is that the risk factors (exposures) are collected before the outcome (e.g. disease) Cohort studies can be retrospective: data already exists and NOT collected for the purposes of the study (i.e.
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