Selected Risk Factors in Coronary Disease

Cornfield, Jerome; Mitchell, Shiela C.

doi:10.1080/00039896.1969.10666860

Cited by 85 publications

(8 citation statements)

References 16 publications

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“…He estimates that the above mentioned ratio can vary from two to ten with domiciliary hospitals as the units of randomization (9). However, there is reason to believe that when one uses large political units, such as cities within a country or state, the clustering effect with regard to biological variables such as mortality rates is small, probably no more than 1.9 (David R. Jacobs, Ph.D., personal communication).…”

Section: Methods: the Modelmentioning

confidence: 97%

Some considerations for the planning of total-community prevention trials-when is sample size adequate?

Gillum¹,

Williams²,

Sondik³

1980

J Community Health

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Despite the large accumulated experience of statisticians with sample-size calculations for clinical trials, little information is available on extending this methodology to total community trials, in which the units of randomization are total communities and surveillance methods are used to assess event rates. As in clinical trials, the asymptotic formula for total sample size is used. However, the assumptions underlying the usual method of computing the expected T-year even rate for the experimental group, pe, are no longer valid in total community trials: all emigrants (dropouts) from large communities cannot practically be identified or followed. Immigrants, similar to dropouts in clinical trials in that they are exposed to the treatment for only a portion of the period but are followed to the end of the study, present an additional problem. This paper presents a method for the computation of pe in total community trials, taking into account in- and out-migration as well as the determinants usually considered in clinical trials. Sample computations are presented, and general problems of design, execution and data analysis of total community prevention trials are briefly discussed.

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Section: Methods: the Modelmentioning

confidence: 97%

Some considerations for the planning of total-community prevention trials-when is sample size adequate?

Gillum¹,

Williams²,

Sondik³

1980

J Community Health

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show abstract

“…They require expert statistical evaluation. Thus, for example, in an excellent critique of statistical methods used to evaluate studies on selected risk factors in coronary disease, Cornfield & Mitchell (1969) stated: 'Despite a very considerable scientific effort and some tantalizingly suggestive results, we have no clear-cut generally accepted answers to the question of whether cholesterol lowering measures can affect coronary disease'. Sixteen years later, Ahrens (1985) came to a similar conclusion.…”

Section: The 'Defensive' Positionmentioning

confidence: 99%

Diet and cardiovascular disease: how should the industry respond?*

Gurr

1987

Int J of Dairy Tech

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“…Men were selected to participate in M R F I T on the basis of three screening visits that identified them in the upper 10% of the population distribution for risk of coronary heart disease. Criteria for risk status are based on the multiple logistic prediction function from the Framingham Heart Study prediction of risk (Cornfield, 1962;Cornfield & Mitchell, 1969). Of the men selected, 50% were randomized into a Special Intervention program designed to help participants lower their risk factors, and 50y0 were referred to their usual source of medical care.…”

Section: Subjectsmentioning

confidence: 99%

“…A wealth of epidemiologic evidence links cigarette smoking, high blood pressure, and elevated serum cholesterol to an increased risk of coronary heart disease (Blackburn, 1974;Chapman & Ailassey, 1964;Cornfield, 1962;Cornfield & Mitchell, 1969; Dawber, Kannel, & Lyell, 1963;Doyle, 1963;NHI Framingham Study, 1968). As a result of this evidence and the magnitude of the problem of coronary heart disease (CHD) in developed countries, in 1970 the Inter-Society Commission for Heart Disease Resources, and in 1971 the Task Force on Arteriosclerosis, recommended that studies be conducted to examine the effects of modifying risk factors on the primary prevention of the complications of arteriosclerosis (NHLBI, 1971).…”

mentioning

confidence: 99%

Coronary risk factor intervention: Characteristics associated with change

1982

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Predicting change in coronary heart disease (CHD) risk factors (cigarette smoking, hypertension, and cholesterol) was undertaken in participants randomized to the treatment group of the Harvard Center of the Multiple Risk Factor Intervention Trial. The predictor variables of interest were psychosocial characteristics, responses to the initial group sessions intervention strategy, and change in weight. The Harvard sample consists of 652 men identified at high risk of death from CHD. Fifty percent were randomized into an intervention group, and 50% were referred to their usual source of medical care. For the analyses, men were stratified by their baseline smoking status and whether they were taking medication for hypertension. The analyses revealed that there was no single model that explained changes in all three risk factors. For example, cigarette smokers were significantly more likely to stop smoking if they attended the group sessions (p ≤ 0.001). An internal locus of control predicted a lower blood pressure by the end of the first year independent of medication (p ≤ 0.05). If a participant entered the trial with serum cholesterol as the only risk factor of concern, attending the group sessions was associated with a significant decrease by the end of the first year (p ≤ 0.05).

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Selected Risk Factors in Coronary Disease

Cited by 85 publications

References 16 publications

Some considerations for the planning of total-community prevention trials-when is sample size adequate?

Some considerations for the planning of total-community prevention trials-when is sample size adequate?

Diet and cardiovascular disease: how should the industry respond?*

Coronary risk factor intervention: Characteristics associated with change

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