Published:
I was recently asked to do sample size and power calculations in the setting of a 1-way ANOVA with unequal group sizes. There are solutions to do this when the group sizes are equal, but the answer is not as commonly available for the case where group sizes are unequal.
Published:
Just reminding myself why 2x2 factorial designs give “two trials for the price of one.”
Published:
Doing some math to show that the area under the receiver-operating characteristic curve (AUROC or AUC) for a non-informative or “no-skill” risk score or classifier (e.g., coin flip) is 0.5, and the area under the precision-recall curve (AUPRC) is the outcome prevalence.
Published:
Doing some math to show that the area under the receiver-operating characteristic curve (AUROC or AUC) for a non-informative or “no-skill” risk score or classifier (e.g., coin flip) is 0.5, and the area under the precision-recall curve (AUPRC) is the outcome prevalence.
Published:
Doing some math to show that the area under the receiver-operating characteristic curve (AUROC or AUC) for a non-informative or “no-skill” risk score or classifier (e.g., coin flip) is 0.5, and the area under the precision-recall curve (AUPRC) is the outcome prevalence.
Published:
A collaborator recently asked me to do power calculations for the population attributable fraction (PAF) of a dichotomous exposure, $F$, for an incident outcome, $D$. In a previous iteration, we had focused instead on using standard sample size and power calcuation approaches to get a minimum detectable relative risk (RR) assuming known sample size, outcome rate, exposure prevalence, and 80% power. While there is a paper by Browner and Newman (1989) for sample size and power for the PAF, I think an alternate approach could use the minimum detectable RR that I had already computed and the relationship between the PAF and RR.
Published:
A collaborator recently asked me to do power calculations for the population attributable fraction (PAF) of a dichotomous exposure, $F$, for an incident outcome, $D$. In a previous iteration, we had focused instead on using standard sample size and power calcuation approaches to get a minimum detectable relative risk (RR) assuming known sample size, outcome rate, exposure prevalence, and 80% power. While there is a paper by Browner and Newman (1989) for sample size and power for the PAF, I think an alternate approach could use the minimum detectable RR that I had already computed and the relationship between the PAF and RR.
Published:
I was recently asked to do sample size and power calculations in the setting of a 1-way ANOVA with unequal group sizes. There are solutions to do this when the group sizes are equal, but the answer is not as commonly available for the case where group sizes are unequal.
Published:
A collaborator recently asked me to do power calculations for the population attributable fraction (PAF) of a dichotomous exposure, $F$, for an incident outcome, $D$. In a previous iteration, we had focused instead on using standard sample size and power calcuation approaches to get a minimum detectable relative risk (RR) assuming known sample size, outcome rate, exposure prevalence, and 80% power. While there is a paper by Browner and Newman (1989) for sample size and power for the PAF, I think an alternate approach could use the minimum detectable RR that I had already computed and the relationship between the PAF and RR.
Published:
Doing some math to show that the area under the receiver-operating characteristic curve (AUROC or AUC) for a non-informative or “no-skill” risk score or classifier (e.g., coin flip) is 0.5, and the area under the precision-recall curve (AUPRC) is the outcome prevalence.
Published:
Doing some math to show that the area under the receiver-operating characteristic curve (AUROC or AUC) for a non-informative or “no-skill” risk score or classifier (e.g., coin flip) is 0.5, and the area under the precision-recall curve (AUPRC) is the outcome prevalence.
Published:
A collaborator recently asked me to do power calculations for the population attributable fraction (PAF) of a dichotomous exposure, $F$, for an incident outcome, $D$. In a previous iteration, we had focused instead on using standard sample size and power calcuation approaches to get a minimum detectable relative risk (RR) assuming known sample size, outcome rate, exposure prevalence, and 80% power. While there is a paper by Browner and Newman (1989) for sample size and power for the PAF, I think an alternate approach could use the minimum detectable RR that I had already computed and the relationship between the PAF and RR.
Published:
Just reminding myself why 2x2 factorial designs give “two trials for the price of one.”
Published:
I was recently asked to do sample size and power calculations in the setting of a 1-way ANOVA with unequal group sizes. There are solutions to do this when the group sizes are equal, but the answer is not as commonly available for the case where group sizes are unequal.
Published:
A collaborator recently asked me to do power calculations for the population attributable fraction (PAF) of a dichotomous exposure, $F$, for an incident outcome, $D$. In a previous iteration, we had focused instead on using standard sample size and power calcuation approaches to get a minimum detectable relative risk (RR) assuming known sample size, outcome rate, exposure prevalence, and 80% power. While there is a paper by Browner and Newman (1989) for sample size and power for the PAF, I think an alternate approach could use the minimum detectable RR that I had already computed and the relationship between the PAF and RR.
Published:
A collaborator recently asked me to do power calculations for the population attributable fraction (PAF) of a dichotomous exposure, $F$, for an incident outcome, $D$. In a previous iteration, we had focused instead on using standard sample size and power calcuation approaches to get a minimum detectable relative risk (RR) assuming known sample size, outcome rate, exposure prevalence, and 80% power. While there is a paper by Browner and Newman (1989) for sample size and power for the PAF, I think an alternate approach could use the minimum detectable RR that I had already computed and the relationship between the PAF and RR.
Published:
Doing some math to show that the area under the receiver-operating characteristic curve (AUROC or AUC) for a non-informative or “no-skill” risk score or classifier (e.g., coin flip) is 0.5, and the area under the precision-recall curve (AUPRC) is the outcome prevalence.
Published:
Just reminding myself why 2x2 factorial designs give “two trials for the price of one.”
Published:
I was recently asked to do sample size and power calculations in the setting of a 1-way ANOVA with unequal group sizes. There are solutions to do this when the group sizes are equal, but the answer is not as commonly available for the case where group sizes are unequal.