436 research outputs found
Current sample size conventions: Flaws, harms, and alternatives
No full textAbstract Background The belief remains widespread that medical research studies must have statistical power of at least 80% in order to be scientifically sound, and peer reviewers often question whether power is high enough. Discussion This requirement and the methods for meeting it have severe flaws. Notably, the true nature of how sample size influences a study's projected scientific or practical value precludes any meaningful blanket designation of value of information methods, simple choices based on cost or feasibility that have recently been justified, sensitivity analyses that examine a meaningful array of possible findings, and following previous analogous studies. To promote more rational approaches, research training should cover the issues presented here, peer reviewers should be extremely careful before raising issues of "inadequate" sample size, and reports of completed studies should not discuss power. Summary Common conventions and expectations concerning sample size are deeply flawed, cause serious harm to the research process, and should be replaced by more rational alternatives.</p
Unexamined assumptions in explorations of upper limit for cases of variant Creutzfeldt-Jakob disease
No full text
Age and Variant Creutzfeldt-Jakob Disease
No full textThe young and stable median age of those who die of variant Creutzfeldt-Jakob disease has been attributed to age-dependent infection rates. This analysis shows that an influence of age on risk for death after infection better explains age patterns, suggesting that biologic factors peaking in the third decade of life may hasten disease
Estimating the Incubation Period of AIDS by Comparing Population Infection and Diagnosis Patterns
No full textThe incidence of AIDS virus infections over time among gay men in San Francisco is nonparametrically estimated from interval-censored data by using the EM algorithm to maximize a roughness-penalized likelihood. Because the distribution of AIDS diagnoses is the convolution of the infection and incubation distributions, the incubation distribution can be estimated by comparing the estimated infection distribution and the observed pattern of diagnoses. This is again accomplished by nonparametrically maximizing a roughness-penalized likelihood using the EM algorithm. The optimal degree of smoothness for the estimates is chosen using external data and subjective assessments of plausibility. Three prospective studies of initially uninfected men produce comparable estimated infection rates and are merged to produce an overall estimate, which shows rates increasing until mid-1982 and then falling sharply. The estimated incubation period hazard function is near zero for two years following infection and then increases until it flattens out at about seven years after infection. Bootstrap simulations are used to gauge the variability of the estimates. Because infections were concentrated in the years 1980 to 1982, the incubation estimate is fairly accurate. Inclusion of the roughness penalty in the criteria to be optimized greatly reduces the variability of the estimates while also greatly speeding the convergence of the algorithms
- …
