# Calculate Effect Sizes for meta.nsue Objects These functions create objects of class `"nsue"`, ready to be used by `meta` and `leave1out`. ```r nsue(y, y_lo = -y_up, y_up, aux, y2var, mi, backtransf = .backtransf_identity, measure = "effect size", labels = "study") smc_from_t(t, n, alpha = 0.05, labels = "study") smd_from_t(t, n1, n2, alpha = 0.05, labels = "study") zcor_from_r(r, n, alpha = 0.05, labels = "study") ``` ## Arguments - `y`: a vector to specify the effect-sizes. Use NA in studies with Non-statistically Significant Unreported Effects (NSUEs). - `t`: a vector to specify the t-values of the studies. Use NA in studies with Non-statistically Significant Unreported Effects (NSUEs). - `r`: a vector to specify the correlation coefficients of the studies. Use NA in studies with Non-statistically Significant Unreported Effects (NSUEs). - `y_lo`: a vector to specify the effect-sizes corresponding to the lower statistical threshold. - `y_up`: a vector to specify the effect-sizes corresponding to the upper statistical threshold. - `aux`: a data.frame to specify information required for `y2var`, `mi` and / or `backtransf`. - `n`: a vector to specify the sample sizes of the studies. - `n1`: a vector to specify the sample sizes of the first group (e.g. patients) of studies. - `n2`: a vector to specify the sample sizes of the second group (e.g. controls) of the studies. - `y2var`: a function to derive the variances of the effect sizes. - `mi`: a function to multiply impute effect sizes. - `backtransf`: a function to back-transform the effect sizes. - `measure`: a description of the effect-size measure used. - `labels`: a vector to specify the labels of the studies. - `alpha`: a vector to specify the p-value thresholds used in the studies (e.g. 0.05). ## Details Use `nsue` for creating an object of class `"nsue"`. Use `smc_from_t` for creating an object of class `"nsue"` for standardized mean changes from the t-values of the paired Student t-tests, e.g. in repeated-measures studies analyzing the amount of change in within a group. Use `smd_from_t` for creating an object of class `"nsue"` for standardized mean differences from t-values of the two-sample Student t-tests, e.g. in studies comparing a quantitative (normally-distributed) variable between two groups. Use `zcor_from_r` for creating an object of class `"nsue"` for Pearson correlation coefficients (using the Fisher's transform), e.g. in studies examining the association between two quantitative (normally-distributed) variables. ## Returns `nsue`, `smc_from_t`, `smd_from_t`, and `zcor_from_r` return objects of class `"nsue"`. The function `print` may be used to print a summary of the results. The function `subset` returns the subset of studies that meets a condition. An object of class `"nsue"` is a list containing the following components: - **y**: the effect-sizes. - **y_lo**: the effect-sizes corresponding to the lower statistical threshold. - **y_up**: the effect-sizes corresponding to the upper statistical threshold. - **aux**: information required for `y2var`, `mi` and / or `backtransf`. - **y2var**: a function to derive the variances of the effect sizes. - **mi**: a function to multiply impute effect sizes. - **backtransf**: a function to back-transform the effect sizes. - **measure**: a description of the effect-size measure used. - **labels**: the labels of the studies. Users can create their objects of class `"nsue"` for effect sizes not included in the package. ## References Radua, J., Schmidt, A., Borgwardt, S., Heinz, A., Schlagenhauf, F., McGuire, P., Fusar-Poli, P. (2015) Ventral striatal activation during reward processing in psychosis. A neurofunctional meta-analysis. **JAMA Psychiatry**, 72 , 1243--51, doi:10.1001/jamapsychiatry.2015.2196. Albajes-Eizagirre, A., Solanes, A, Radua, J. (2019) Meta-analysis of non-statistically significant unreported effects. **Statistical Methods in Medical Research**, 28 , 3741--54, doi:10.1177/0962280218811349. ## Author(s) Joaquim Radua ## See Also `meta` for conducting a meta-analysis. `leave1out` for computing leave-one-out diagnostics. ## Examples ```r # Standardized mean change in one sample: t <- c(3.4, NA, NA, NA, 3.2, 2.8, 2.1, 3.1, 2.0, 3.4) n <- c(40, 20, 22, 24, 18, 30, 25, 30, 16, 22) smc <- smc_from_t(t, n) m0 <- meta(smc) smc m0 # Standardized mean difference between two samples: t <- c(4.8, 3.2, NA, NA, NA, 3.2, 2.0, 2.3, 2.7, 3.1) n1 <- c(40, 20, 22, 24, 18, 30, 25, 30, 16, 22) n2 <- c(38, 20, 22, 25, 20, 28, 25, 30, 18, 23) smd <- smd_from_t(t, n1, n2) m1 <- meta(smd) smd m1 # Pearson correlation: r <- c(0.80, NA, NA, NA, 0.32, 0.45, 0.53, 0.67, 0.74, 0.56) n <- c(40, 22, 13, 12, 28, 22, 27, 28, 15, 23) zr <- zcor_from_r(r, n) m2 <- meta(zr) zr m2 ```

nsue function