A method for determining a test panel for disease diagnosis, wherein a multivariate predictive model for diagnosing a disease for which a plurality of test methods are individually inadequate is constructed, said method comprising: (a) performing a panel of laboratory tests for diagnosing said disease on a test population comprising a statistically significant sample of individuals with at least one objective sign of disease and a statistically significant control sample of healthy individuals or persons with cross-reacting medical conditions; (b) generating a score function from a linear combination of said test panel results, said linear combination expressed as β T Y, wherein D is the disease; Y t,..., Y k is a set of K diagnostic tests for D; Y is a vector of diagnostic test results {Y 1,..., Y k}; D' = not D; P is a vector of coefficients {β 1,..., β k} for Y; and β T is the transpose of β; (c) performing a receiver operating characteristic regression of the score function, wherein the test panel is selected and β coefficients are calculated simultaneously to maximize the area under the curve of the empirical ROC as approximated by: AUC β = 1 n D ⋅ n H ∑ i ∈ D, j ∈ H I β T Y i > β T Y j, wherein I is the indicator function, N = the number of study subjects, n D in the number of patients with disease D, n H is the number of healthy controls, n D + n H =N; i = 1,..., n D, i ∈ D are patients with disease; j = 1,..., n H, j ∈ H are healthy controls. (d) calculating for each individual the pretest odds of disease; generating a diagnostic likelihood ratio of disease by determining the frequency of each individual's test score in said diseased population relative to said control population; and multiplying said pretest odds by said likelihood ratio to determine the posttest odds of disease for each individual; (e) converting a set of posttest odds into posttest probabilities for disease D and creating an ROC curve by altering the posttest probability cutoff value; (f) identifying a posttest probability cutoff value by finding that point on the-ROC curve tangent to a line with a slope of (1-p)·C/p·B, where p is the population prevalence of disease D, B is the loss of productive life associated with failing to treat patients with disease D, as defined by a cohort of patients with disease D using a visual analog method to assign a numerical value to this loss, and C is the loss of productive life associated with treating a patient without disease; and (g) displaying, by computer, the test panel selected for disease diagnosis; the weighted result for each member of the test panel, defined as the product of given test result for a given patient multiplied by its respective β coefficient; the score function value for a given patient; and the score function cutoff value against which positive or negative diagnoses are to be made; and (h) wherein said disease D is Lyme disease. (a) performing a panel of laboratory tests for diagnosing said disease on a test population comprising a statistically significant sample of individuals with at least one objective sign of disease and a statistically significant control sample of healthy individuals or persons with cross-reacting medical conditions; (b) generating a score function from a linear combination of said test panel results, said linear combination expressed as β T Y, wherein D is the disease; Y t,..., Y k is a set of K diagnostic tests for D; Y is a vector of diagnostic test results {Y 1,..., Y k}; D' = not D; P is a vector of coefficients {β 1,..., β k} for Y; and β T is the transpose of β; (c) performing a receiver operating characteristic regression of the score function, wherein the test panel is selected and β coefficients are calculated simultaneously to maximize the area under the curve of the empirical ROC as approximated by: AUC β = 1 n D ⋅ n H ∑ i ∈ D, j ∈ H I β T Y i > β T Y j, wherein I is the indicator function, N = the number of study subjects, n D in the number of patients with disease D, n H is the number of healthy controls, n D + n H =N; i = 1,..., n D, i ∈ D are patients with disease; j = 1,..., n H, j ∈ H are healthy controls. (d) calculating for each individual the pretest odds of disease; generating a diagnostic likelihood ratio of disease by determining the frequency of each individual's test score in said diseased population relative to said control population; and multiplying said pretest odds by said likelihood ratio to determine the posttest odds of disease for each individual; (e) converting a set of posttest odds into posttest probabilities for disease D and creating an ROC curve by altering the posttest probability cutoff value; (f) identifying a posttest probability cutoff value by finding that point on the-ROC curve tangent to a line with a slope of (1-p)·C/p·B, where p is the population prevalence of disease D, B is the loss of productive life associated with failing to treat patients with disease D, as defined by a cohort of patients with disease D using a visual analog method to assign a numerical value to this loss, and C is the loss of productive life associated with treating a patient without disease; and (g) displaying, by computer, the test panel selected for disease diagnosis; the weighted result for each member of the test panel, defined as the product of given test result for a given patient multiplied by its respective β coefficient; the score function value for a given patient; and the score function cutoff value against which positive or negative diagnoses are to be made; and (h) wherein said disease D is Lyme disease.