Researchers conducting randomized clinical trials with two treatment groups sometimes wish to determine whether biomarkers are predictive and/or prognostic. They can use regression models with interaction terms to ass...Researchers conducting randomized clinical trials with two treatment groups sometimes wish to determine whether biomarkers are predictive and/or prognostic. They can use regression models with interaction terms to assess the role of the biomarker of interest. However, although the interaction term is undoubtedly a suitable measure for prediction, the optimal way to measure prognosis is less clear. In this article, we define causal measures that can be used for prognosis and prediction based on biomarkers. The causal measure for prognosis is defined as the average of two differences in status between biomarker-positive and -negative subjects under treatment and control conditions. The causal measure for prediction is defined as the difference between the causal effect of the treatment for biomarker-positive and biomarker-negative subjects. We also explain the relationship between the proposed measures and the regression parameters. The causal measure for prognosis corresponds to the terms for the biomarker in a regression model, where the values of the dummy variables representing the explanatory variables are -1/2 or 1/2. The causal measure for prediction is simply the causal effect of the interaction term in a regression model. In addition, for a binary outcome, we express the causal measures in terms of four response types: always-responder, complier, non-complier, and never-responder. The causal measure for prognosis can be expressed as a function of always- and never-responders, and the causal measure for prediction as a function of compliers and non-compliers. This enables us to demonstrate that the proposed measures are plausible in the case of a binary outcome. Our causal measures should be used to assess whether a biomarker is prognostic and/or predictive.展开更多
The main purpose in many randomized trials is to make an inference about the average causal effect of a treatment. Therefore, on a binary outcome, the null hypothesis for the hypothesis test should be that the causal ...The main purpose in many randomized trials is to make an inference about the average causal effect of a treatment. Therefore, on a binary outcome, the null hypothesis for the hypothesis test should be that the causal risks are equal in the two groups. This null hypothesis is referred to as the weak causal null hypothesis. Nevertheless, at present, hypothesis tests applied in actual randomized trials are not for this null hypothesis;Fisher’s exact test is a test for the sharp causal null hypothesis that the causal effect of treatment is the same for all subjects. In general, the rejection of the sharp causal null hypothesis does not mean that the weak causal null hypothesis is rejected. Recently, Chiba developed new exact tests for the weak causal null hypothesis: a conditional exact test, which requires that a marginal total is fixed, and an unconditional exact test, which does not require that a marginal total is fixed and depends rather on the ratio of random assignment. To apply these exact tests in actual randomized trials, it is inevitable that the sample size calculation must be performed during the study design. In this paper, we present a sample size calculation procedure for these exact tests. Given the sample size, the procedure can derive the exact test power, because it examines all the patterns that can be obtained as observed data under the alternative hypothesis without large sample theories and any assumptions.展开更多
The vaccination of one person may prevent another from becoming infected, either because the vaccine may prevent the first person from acquiring the infection and thereby reduce the probability of transmission to the ...The vaccination of one person may prevent another from becoming infected, either because the vaccine may prevent the first person from acquiring the infection and thereby reduce the probability of transmission to the second, or because, if the first person is infected, the vaccine may impair the ability of the infectious agent to initiate new infections. The former mechanism is referred as a contagion effect and the latter is referred as an infectiousness effect. By applying a principal stratification approach, the conditional infectiousness effect has been defined, but the contagion effect is not defined using this approach. Recently, new definitions of unconditional infectiousness and contagion effects were provided by applying a mediation analysis approach. In addition, a simple relationship between conditional and unconditional infectiousness effects was found under a number of assumptions. These two infectiousness effects can be assessed by very simple estimation and sensitivity analysis methods under the assumptions. Nevertheless, such simple methods to assess the contagion effect have not been discussed. In this paper, we review the methods of assessing infectiousness effects, and apply them to the inference of the contagion effect. The methods provided here are illustrated with hypothetical vaccine trial data.展开更多
文摘Researchers conducting randomized clinical trials with two treatment groups sometimes wish to determine whether biomarkers are predictive and/or prognostic. They can use regression models with interaction terms to assess the role of the biomarker of interest. However, although the interaction term is undoubtedly a suitable measure for prediction, the optimal way to measure prognosis is less clear. In this article, we define causal measures that can be used for prognosis and prediction based on biomarkers. The causal measure for prognosis is defined as the average of two differences in status between biomarker-positive and -negative subjects under treatment and control conditions. The causal measure for prediction is defined as the difference between the causal effect of the treatment for biomarker-positive and biomarker-negative subjects. We also explain the relationship between the proposed measures and the regression parameters. The causal measure for prognosis corresponds to the terms for the biomarker in a regression model, where the values of the dummy variables representing the explanatory variables are -1/2 or 1/2. The causal measure for prediction is simply the causal effect of the interaction term in a regression model. In addition, for a binary outcome, we express the causal measures in terms of four response types: always-responder, complier, non-complier, and never-responder. The causal measure for prognosis can be expressed as a function of always- and never-responders, and the causal measure for prediction as a function of compliers and non-compliers. This enables us to demonstrate that the proposed measures are plausible in the case of a binary outcome. Our causal measures should be used to assess whether a biomarker is prognostic and/or predictive.
文摘The main purpose in many randomized trials is to make an inference about the average causal effect of a treatment. Therefore, on a binary outcome, the null hypothesis for the hypothesis test should be that the causal risks are equal in the two groups. This null hypothesis is referred to as the weak causal null hypothesis. Nevertheless, at present, hypothesis tests applied in actual randomized trials are not for this null hypothesis;Fisher’s exact test is a test for the sharp causal null hypothesis that the causal effect of treatment is the same for all subjects. In general, the rejection of the sharp causal null hypothesis does not mean that the weak causal null hypothesis is rejected. Recently, Chiba developed new exact tests for the weak causal null hypothesis: a conditional exact test, which requires that a marginal total is fixed, and an unconditional exact test, which does not require that a marginal total is fixed and depends rather on the ratio of random assignment. To apply these exact tests in actual randomized trials, it is inevitable that the sample size calculation must be performed during the study design. In this paper, we present a sample size calculation procedure for these exact tests. Given the sample size, the procedure can derive the exact test power, because it examines all the patterns that can be obtained as observed data under the alternative hypothesis without large sample theories and any assumptions.
文摘The vaccination of one person may prevent another from becoming infected, either because the vaccine may prevent the first person from acquiring the infection and thereby reduce the probability of transmission to the second, or because, if the first person is infected, the vaccine may impair the ability of the infectious agent to initiate new infections. The former mechanism is referred as a contagion effect and the latter is referred as an infectiousness effect. By applying a principal stratification approach, the conditional infectiousness effect has been defined, but the contagion effect is not defined using this approach. Recently, new definitions of unconditional infectiousness and contagion effects were provided by applying a mediation analysis approach. In addition, a simple relationship between conditional and unconditional infectiousness effects was found under a number of assumptions. These two infectiousness effects can be assessed by very simple estimation and sensitivity analysis methods under the assumptions. Nevertheless, such simple methods to assess the contagion effect have not been discussed. In this paper, we review the methods of assessing infectiousness effects, and apply them to the inference of the contagion effect. The methods provided here are illustrated with hypothetical vaccine trial data.
