Formulating a Research Question

February 06, 2023

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In this first video, we discuss the PICO criteria to guide you in framing a comparative research question.

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00:02<v Maria>My name is Maria Ciarleglio</v>
00:04and I'm a faculty member
00:05in the Department of Biostatistics
00:08at the Yale School of Public Health.
00:11In this video series I will introduce the clinical research
00:14process to prepare you to collaborate with a statistician.
00:20In this first video we'll discuss what is often
00:24the first step of the research process,
00:26formulating a research question.
00:31The first step in the research process
00:33is to convert the need for information
00:36into an answerable question or hypothesis.
00:40A well formulated research question is specific and precise.
00:45The research question guides the study design
00:49and other design-related study characteristics,
00:52the data that are collected, the data analysis
00:56and ultimately determines what you can conclude
00:59at the end of the study.
01:02The PICO criteria can be used to guide you
01:05in framing a comparative research question.
01:09The PICO framework begins by specifying the population
01:12of interest, then the intervention being studied,
01:16the control or comparator group,
01:19and the outcomes of interest.
01:23Begin by specifying the population of interest.
01:26For example,
01:27patients with non-alcoholic fatty liver disease.
01:31The target population is the group of patients
01:34to which you would like to generalize your study findings.
01:38The study population is the group
01:40of patients to which you have access.
01:44The study population may be a subset
01:47of the target population.
01:50For example, your goal may be to generalize
01:53to all adult Americans
01:55with non-alcoholic fatty liver disease.
01:58However, you may be limited to a patient population
02:02from a certain state or medical center.
02:06In our case, we may only have access to patients
02:09with non-alcoholic fatty liver disease
02:12followed in the liver clinic from 2015 to 2020.
02:18In this case, you could either collect data
02:20from all individuals in the available study population
02:24if it's feasible to do that.
02:26Otherwise, if the study population is too large
02:29you could select a random sample
02:32from that available study population.
02:35If you choose a representative random sample
02:39your results are generalizable to that study population.
02:45Next, specify the main intervention,
02:49which is the exposure test treatment
02:52or the main prognostic factor
02:54that you are interested in studying.
02:57For example, lifestyle modification to achieve weight loss
03:02or if studying liver cancer,
03:04your intervention of interest could be serafenib
03:07to prolonged survival.
03:12If you're interested in performing a comparison,
03:15the next step is to specify a control
03:18or comparison intervention or exposure.
03:23This can be, for example,
03:24a placebo control or the current standard of care.
03:30Finally, we must specify the clinical outcome
03:33or primary endpoint of your study.
03:37This includes the element of time, if that's appropriate,
03:40and this would apply if you're looking
03:42at a fixed follow up time period post-intervention.
03:47Say three month survival following surgery
03:51or NAFLD resolution one year following
03:54a certain percentage reduction in total body weight.
04:01Let's run through an example of the type of study
04:03we often perform using medical record data.
04:07The research question asks
04:10among Hepatitis B infected persons,
04:12what factors tests best identify individuals
04:17at highest risk of progression,
04:19as well as those at low risk of progression?
04:23The population studied is Hepatitis B infected persons
04:28treated at the Yale Liver Center between 2011 and 2021.
04:35The interventions of interest
04:37are different patient characteristics.
04:39Specifically, the study will look at different permutations
04:43of key baseline exposures or risk factors
04:47identified in previous studies of Hepatitis B prognosis.
04:52Here, the investigators will look at age
04:55presence of fibrosis, presence of cirrhosis,
04:58elevated ALT, and detectable viral load.
05:05The comparator group for each of these factors
05:08is absence of the baseline factor.
05:12The outcomes of interest are liver related morbidity,
05:16progression of liver disease
05:17and mortality during up to 10 years of follow up.
05:22Now, this is more of an exploratory study
05:25looking for signals of association, but even still,
05:29it has a clearly defined population,
05:31intervention or exposures of interest,
05:34control or reference levels of the exposures
05:37and outcomes of interest.
05:40Sitting down and thinking through the PICO criteria
05:43forces you to make decisions
05:45and pre-specify important aspects of your study.
05:51As we saw in the last example,
05:53there are often multiple clinical endpoints of interest.
05:57Endpoints are classified as clinical or nonclinical.
06:03Clinical endpoints describe outcomes
06:05involving how a patient feels, functions or survives.
06:10They may be assessed by a clinician
06:12and involve clinical judgment,
06:14such as the occurrence of stroke or MI.
06:17They may also be measured by a standard performance measure
06:21such as a pulmonary function test
06:23or they can be patient-reported,
06:25such as self-reported symptoms or quality of life.
06:30Nonclinical endpoints include biomarkers
06:33that may not directly relate to how a patient feels,
06:37however they're thought to be important indicators
06:39of the disease process.
06:41These endpoints can include blood tests, imaging
06:45or other physiological measures such as blood pressure.
06:49A good primary outcome should directly align
06:52with the primary aim of the study.
06:55The endpoint should be accurate
06:57and precise, quantifiable, validated, and reproducible.
07:02We generally include a single primary endpoint.
07:06The goal should be to choose a primary endpoint
07:09that will influence decision making in practice.
07:13The most significant and impactful endpoint that addresses
07:17the research question is chosen as the primary endpoint
07:21and additional important endpoints may be designated
07:25as secondary or tertiary.
07:28Secondary endpoints may not be considered sufficient
07:32to influence decision making alone,
07:35but may help support the claim of efficacy.
07:38Tertiary endpoints
07:39are sometimes called exploratory endpoints.
07:43If included, they are generally used
07:45to test exploratory hypotheses.
07:50Again, we generally use a single primary outcome.
07:54Using multiple primary endpoints may lead
07:57to an unfocused research question and can present problems
08:01with interpretation if the treatment effect is observed
08:04to differ across the multiple outcomes.
08:08However, multiple endpoints may be needed
08:11when a clinical benefit depends
08:13on more than one aspect of the disease.
08:16For example, in Alzheimer's, we may require an effect
08:20on both cognition and function,
08:23so there may be situations where multiple endpoints
08:26are necessary for demonstrating efficacy.
08:30The statistical issue with multiple endpoints
08:32is what we call multiplicity.
08:36When we conduct statistical analysis
08:38and perform hypothesis tests,
08:40there's a chance that we conclude
08:42a significant difference exists between the arms tested
08:47when in truth, there is no difference.
08:49This is due to random variation in the data
08:52that we can observe, but this is a mistake in error,
08:56and we refer to this type of error as a type one error
09:02or the alpha level of the test.
09:05We like to keep this type of error low,
09:08so we typically set the type one error of our tests at 5%.
09:13So when you're testing a single endpoint,
09:16you can maintain a type one error of 5%.
09:20However, suppose we're testing two primary endpoints
09:23and success on either endpoint would lead
09:26to a conclusion of a treatment difference.
09:30The type one error rate on each endpoint compounds
09:34and there's an inflation of the overall type one error
09:36probability above 5%.
09:40This increases the chance of false conclusions
09:43regarding the efficacy of the intervention.
09:46Special statistical testing procedures
09:49need to be used to control the type one error rate
09:52for the study with multiple endpoints.
09:56Multiple primary endpoints occur in three ways.
10:00The first is when there are multiple endpoints
10:03and each endpoint could be sufficient
10:05on its own to establish the efficacy
10:07of the intervention being tested.
10:10These multiple endpoints correspond
10:11to multiple chances of success,
10:14so failure to adjust for multiplicity
10:17can lead to type one error rate inflation
10:20and a false conclusion of effectiveness.
10:23The second option is when the determination of effectiveness
10:27depends on success on all primary endpoints
10:31when there are two or more primary endpoints.
10:34In this setting, there are no multiplicity issues related
10:37to the primary endpoints
10:40as there is only one path that leads
10:42to a successful outcome for the trial and therefore,
10:46no concern with type one error rate inflation.
10:50The third option combines several aspects
10:52of effectiveness into a single primary composite endpoint.
10:57This avoids multiple endpoint related multiplicity issues.
11:01In many cardiovascular studies
11:04it's common to combine several endpoints.
11:06For example, cardiovascular death, heart attack and stroke
11:11into a single composite primary endpoint.
11:15In this case, death is considered on its own
11:18as a secondary endpoint.
11:19If any one of the elements
11:21of the composite outcome is observed,
11:23then the endpoint has occurred for that patient.
11:27It's important that the endpoints included
11:29in the composite endpoint
11:31are of similar clinical importance.
11:34Using a composite endpoint is helpful
11:37when the components are individually rare
11:41so choosing a composite endpoint allows you to
11:43observe more events.
11:45A limitation of using a composite endpoint is that
11:49given the sample size of the study,
11:51there may not be adequate statistical power
11:55to test each component of the endpoint separately.
11:59We'll discuss statistical power in a future video
12:02on elements of sample size calculations.
12:05We'll also discuss endpoints and variables in general,
12:09from a data collection perspective in a future video.
12:15In this video, we discussed important things
12:18to consider when formulating your research question.
12:22From the research question will flow
12:24the specific statistical hypotheses to be tested,
12:28the design of the study, including the sample size,
12:31the data necessary to answer the research question,
12:35the statistical analysis that will be performed
12:38and the conclusions that can be made.
12:41The next video, which is the second video in this series,
12:45will give you an overview
12:46of study designs commonly used in clinical research.
12:51In video three, we will discuss the data collection process
12:55and formally define different variable types.
12:58This video will prepare us
13:00for video four on sample size determination.