Nested Randomized Trials in Large Cohorts and Biobanks is a research paper published in Epidemiology (2008). On theSindex it has a DataRank of 2.2. It has been cited 44 times, with 28 citing works in its 1-hop citation network.
Most diseases are likely to result largely from the interplay of lifestyle and genetic factors. However, both observational studies and randomized trials have faced major limitations in trying to address the impact of lifestyle on health. As large cohorts and biobanks are being developed, we need to find novel, efficient ways to address the effects of lifestyle interventions. We propose that this could be done using multiple lifestyle factorial experimental designs that combine characteristics of randomized trials and epidemiologic studies. Randomized trials of simple lifestyle interventions can be nested within large cohorts linked to reliable registries of outcomes. Participants can choose from a long list of simple lifestyle randomization options and many interventions may be tested concurrently with factorial randomization. Participants can tailor their own personal trial choosing several items among long laundry lists of randomization options. Participants are citizen-scientists rather than passive subjects and this may be attractive in modern societies of health-conscious people. These trials can use the existing machinery of the cohort for data collection and outcome linkage at no or minimal additional cost. We discuss a number of issues on the implementation of multiple lifestyle factorial experimental designs, as compared with the usual observational studies and randomized trials. These include participation, the number of allowed randomizations per participant, compliance/adherence, power, false-negatives, false-positives, composite lifestyle effects, selection of outcomes, follow-up and monitoring, masking and allocation concealment, age of participants, confounding, and cost. The aim should be to combine carefully the strengths of both observational epidemiology and randomized research without compounding their limitations.
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Base Score Contribution
0.571
From this paper's citation signal
Citation Network Contribution
1.6
From 24 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 26% comes from its base citations and 74% from the citation network (24 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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