minor changes, v2.2.3

README.md

@@ -1,8 +1,8 @@
 # med4way
 ### A Stata command for the 4-way decomposition using parametric regression models
 
-- Current version: `2.2.2` 
-- Release date: `14jun2018`
+- Current version: `2.2.3` 
+- Release date: `25nov2018`
 
 ---
 

med4way.ado

@@ -1,8 +1,9 @@
 *! Hello, I'm med4way.ado
-*! v2.2.2 - 14jun2018
+*! v2.2.3 - 25nov2018
 
 /* 
 Previous versions:
+v2.2.2 - 14jun2018
 v2.2.1 - 19sep2017
 v2.2.0 - 31jul2017
 v2.1.1 - 28jul2017
@@ -246,6 +247,13 @@ program define med4way, eclass
 		}
 	}
 
+	//casecontrol works only with yreg logistic
+	if ("`casecontrol'"=="true") & !("`yreg'"=="logistic") {
+		display as error "Error: option casecontrol can only be specified together with a " /*
+			*/ "logistic regression model for the outcome (yreg)."
+			error 198
+	}
+
 	//validate rare outcome
 	if ("`yreg'"=="logistic") {
 		if "`casecontrol'"=="true" {
@@ -615,9 +623,13 @@ program define validate_c, rclass
 // 	c_local eretc "`c'"		// c used in med4way's ereturn
 	c_local wrnngtxt `wrnngtxt' 
 
+	mata: st_local("c_missing", strofreal(hasmissing(strtoreal(tokens(st_local("c")))))) // check that there are no missing values in c. It happens for example if c() contains strings.
+	if (`c_missing' == 1) {
+		di as error "Error: please, check the option c(). It must contain only numbers and/or dots (.)"
+		error 198
+	}
 	tempname cmatrix // c needs to be a matrix to pass it on to mata -> dump c into cmatrix
 	mata: st_matrix("`cmatrix'", strtoreal(tokens(st_local("c"))))
-
 	return mat cmatrix = `cmatrix'
 end validate_c 
 

med4way.pkg

@@ -6,8 +6,8 @@ d A.Discacciati, A.Bellavia, L.Valeri
 d
 d After installation, see help med4way
 d
-d Current version: 2.2.2
-d Distribution-Date: 14jun2018
+d Current version: 2.2.3
+d Distribution-Date: 25nov2018
 d
 f med4way.ado
 f med4way_engine.ado

med4way.sthlp

@@ -1,5 +1,5 @@
 {smcl}
-{* *! version 2.2.2 14jun2018}{...}
+{* *! version 2.2.3 25nov2018}{...}
 
 {cmd:help med4way}
 {hline}
@@ -91,22 +91,22 @@ Note: the 4-way decomposition holds without any assumptions about confounding. H
 {p 7 6 2}{opt logb:inomial}: logbinomial regression (GLM with binomial distribution and log link function){p_end}
 {p 7 6 2}{opt poi:sson}: Poisson regression{p_end}
 {p 7 6 2}{opt negb:inomial}: negative binomial regression{p_end}
-{p 7 6 2}{opt aft, {ul on}e{ul off}xponential}: Accelerated Failure Time (exponential survival distribution) ({helpb stset} required){p_end}
-{p 7 6 2}{opt aft, {ul on}w{ul off}eibull}: Accelerated Failure Time (Weibull survival distribution) ({helpb stset} required){p_end}
-{p 7 6 2}{opt cox}: Cox proportional hazards model ({helpb stset} required){p_end}
+{p 7 6 2}{opt aft, {ul on}e{ul off}xponential}: Accelerated Failure Time regression (exponential distribution) ({helpb stset} required){p_end}
+{p 7 6 2}{opt aft, {ul on}w{ul off}eibull}: Accelerated Failure Time regression (Weibull distribution) ({helpb stset} required){p_end}
+{p 7 6 2}{opt cox}: Cox proportional hazards regression ({helpb stset} required){p_end}
 
 {phang}
 {opt mreg(string)} specifies the form of the regression model for the mediator. The available forms are:
 
 {p 7 6 2}{opt lin:ear}: linear regression{p_end}
-{p 7 6 2}{opt logi:stic}: logistic regression regression{p_end}
+{p 7 6 2}{opt logi:stic}: logistic regression{p_end}
 
 {phang}
 {opt c(string)} fixes the values of the covariates {it:cvars} at which to compute the 4-way decomposition. If {it:cvars} are specified but this option is omitted, {it:cvars} will be automatically fixed at their respective mean values. If this option is specified, the number of values of {opt c(numlist)} must correspond to the number of {it:cvars}. A dot (.) can be used to fix the value of a specific covariate to its mean. 
 Example: the covariates specified are {it:cvar1 cvar2 cvar3} and the user wants to fix the value for {it:cvar2} to 6, while letting the values for {it:cvar1} and {it:cvar3} to be equal to their respective means. This can be achieved with the option {opt c(. 6 .)}.
 
 {phang}
-{opt casec:ontrol} specifies that the data comes from a case-control study (that is, sampling was done on the outcome).
+{opt casec:ontrol} specifies that the data comes from a case-control study (that is, sampling was done on the outcome). This option can be specified only together with a logistic regression model for the outcome.
 
 {phang}
 {opt full:output} specifies that, in addition to the 4 components of the total effect (controlled direct effect, reference interaction, mediated interaction, pure indirect effect), the following quantities are to be estimated: the proportions of the total effect due to each of the 4 components, the overall proportion mediated, the overall proportion due to interaction, and the overall proportion that would be eliminated if the mediator {it:mvar} were fixed to the value {opt m(#)}. 
@@ -245,7 +245,7 @@ For example, to calculate the overall proportion mediated (op_m){p_end}
 
 {title:References}
 
-{phang}Discacciati, A., Bellavia, A., Lee, J.J., Mazumdar, M., Valeri, L., 2018. Med4way: a Stata command to investigate mediating and interactive mechanisms using the four-way effect decomposition. International Journal of Epidemiology.
+{phang}Discacciati, A., Bellavia, A., Lee, J.J., Mazumdar, M., Valeri, L., 2018. Med4way: a Stata command to investigate mediating and interactive mechanisms using the four-way effect decomposition. International Journal of Epidemiology. doi: 10.1093/ije/dyy236
 
 {phang}VanderWeele, T.J., 2014. A unification of mediation and interaction: a 4-way decomposition. Epidemiology (Cambridge, Mass.), 25(5), p.749.